€¦ · halcrow, inc. 4190 lougheed highway, suite 508 burnaby, bc v5c 6a8 canada halcrow, inc....

Virginia Department of Transportation CONFIDENTIAL PPTA NEGOTIATIONS

I-95/I-395 HOT Lanes Project

Independent Traffic and Revenue Forecasts

Draft Final Report

January 2009

Halcrow, Inc.

Halcrow, Inc. 4190 Lougheed Highway, Suite 508 Burnaby, BC V5C 6A8 Canada www.halcrow.com Halcrow, Inc. has prepared this proposal in accordance with the instructions of their client, VDOT, for their sole and specific use. Any other persons who use any information contained herein do so at their own risk. © Halcrow, Inc. 2009




Draft Final Report

January 2009

Halcrow, Inc.




Contents Amendment Record This report has been issued and amended as follows:

Issue Revision Description Date Signed

1 0 DRAFT 11/14/08 KK

2 0 DRAFT FINAL REPORT 01/29/09 KK

Contents

1 Introduction 1 1.1 Context 1 1.2 This Study 3 1.3 Structure of the Report 3

2 I-95/I-395 Corridor Review 5 2.1 Principal Highways 5 2.2 Corridor Transit Services 5 2.3 Informal Carpooling (‘Slugging’) 6 2.4 Historic Traffic Counts (I-95/I-395 and US-1) 8 2.5 I-95/I-395 Traffic Data 10 2.6 Travel Time Data 13 2.7 Origin/Destination Data 14 2.8 Truck Traffic 15 2.9 Land Use and Economic Trends 17

3 Review of Existing HOT Lanes Facilities 20 3.1 Existing HOT lanes in North America 20 3.2 HOT lanes Proposed or Under Development 24 3.3 Violation of HOT Lane regulations 25

4 Traffic & Revenue Forecasting Assumptions 27 4.1 Value of Time (VOT) and toll escalation 27 4.2 Vehicle Operating Costs (VOC) 28 4.3 Annual Expansion Factor 28 4.4 Ramp Up 29 4.5 Treatment of growth beyond the model years 30 4.6 Inflation 31 4.7 Discount Rate 31

5 Base Year Model – Preparation, Calibration and Validation 32 5.1 Data Sources 32 5.2 Zoning System and Network refinement 33 5.3 Volume Delay Functions (VDF) 33 5.4 Base Year Matrix Preparation 34 5.5 Validation Results 39

6 Future Year Model 46 6.1 Future Networks 46

I-95/I-395 HOT Lane

Contents

6.2 Future Land Use 46 6.3 Future Year Matrices 48 6.4 Model Structure 48 6.5 Toll Diversion Model 50

7 Traffic & Revenue Forecasts 53 7.1 ‘Throughput’ Tests 53 7.2 Revenue Optimization 60 7.3 Toll Schedule Analysis 66

8 Sensitivity Testing and Risk Analysis 73 8.1 Introduction 73 8.2 Sensitivity Tests 73 8.3 Risk Analysis 82

9 Summary 87 9.1 ‘Throughput’ Tests 87 9.2 Revenue Optimization Tests 87 9.3 Sensitivity Tests 88 9.4 Risk Analysis 88

Appendix A – 2005 Base Year Model Validation Results 89

Appendix B – Key Road Network Improvements (FAMPO CLRP) 93

Appendix C – Key Road Network Improvements (NCRTPB CLRP) 95

Appendix D – I-95/I-395 HOT Ingress and Egress Ramps 97

Appendix E – Detailed Results from Main Tests 100

Appendix F – Sensitivity Tests – Detailed Results 129

Appendix G – Sensitivity of Traffic Model Forecasts to Fuel Prices 170

Appendix H – Risk Analysis methodologies 175

Appendix I – 90% & 10% Confidence Intervals – Detailed Results 178

I-95/I-395 HOT Lane

Contents

List of Tables

Page Table 1.1 Existing HOV Lanes Operation 2 Table 2.1 Travel Time Survey Results 14 Table 2.2 Historic Population for Selected Jurisdictions (1990-2000) 17 Table 3.1 Violations in existing HOT Lanes 26 Table 4.1 Real GDP/Capita Assumptions 28 Table 4.2 Observed Annualization Factors 29 Table 5.1 HOV Lanes Traffic Composition, Northbound between 06:00-09:00 37 Table 5.2 Travel Time Validation Results 40 Table 6.1 Total Population and Employment (2005, 2015 and 2030) 47 Table 7.1 Annual Revenue for Target Minimum Speed Thresholds 54 Table 7.2 Revenue optimal toll rates (2008 $ per/mile) 61 Table 7.3a Toll Regime used to maintain 45 mph target minimum speed 66 Table 7.3b Toll Regime used to maintain 55 mph target minimum speed 67 Table 7.3c Revenue Optimal Toll Regime 67 Table 7.4a Average segment speeds for 45 mph target minimum speed scenario 68 Table 7.4b Average segment speeds for 55 mph target minimum speed scenario 68 Table 7.4c Average segment speeds for Revenue Optimal Toll Regime scenario 68 Table 7.5 Lane utilization for 45 mph target minimum speed toll schedule 69 Table 7.6 Lane utilization for revenue optimal toll schedule 69 Table 7.7 Possible Toll Schedule (Toll Schedule v1) 70 Table 7.8 Annual Revenue for Revenue Optimal & Proposed Toll Schedule v1 71 Table 8.1 Summary Sensitivity Results 74 Table 8.2 Ramp up Factors for Northern Section 76 Table 8.3 Ramp up Factors 77 Table 8.4 Summary of inputs to risk analysis 83 Table 8.5 Risk analysis results 84

List of Figures

Page Figure 1.1 Proposed I-95/I-395 HOT Lanes Project 4 Figure 2.1 2005 AM Peak Bus Services I-95/I-395 6 Figure 2.2 Slug Locations 7 Figure 2.3 Map of I-95 and US-1 8 Figure 2.4 Traffic profiles of I-95 (SR-110 to Mudd Tavern Road) 9

I-95/I-395 HOT Lane

Contents

List of Figures

Page Figure 2.5 Traffic profiles of US-1 (Glebe to Thornburg) 10 Figure 2.6 I-95/I-395 2006 Hourly Profiles (Jefferson Davis Highway to

Germanna Hwy) 11

Figure 2.7 I-95/I-395 2006 Hourly Profiles (Joplin Road to Dumfries Road) 11 Figure 2.8 I-95/I-395 2006 Hourly Profiles (Edsall Road to Duke Street) 12 Figure 2.9 I-95/I-395 2005 Average Day of Week Profiles 12 Figure 2.10 I-95/I-395 2005 Monthly Traffic Profiles 13 Figure 2.11 2000 CTPP Trip Origin (Auto Driver) 15 Figure 2.12 2000 CTPP Trip Destination (Auto Driver) 15 Figure 2.13 2005 Average Daily Truck Profiles of I-95 (SR 110 to Mudd Tavern

Road) 16

Figure 2.14 2005 Average Daily Truck Profiles along US 1 (Glebe to Thornburg) 16 Figure 2.15 Employment for DC-VA-MD-WV area (1969-2006) 18 Figure 2.16 Gross Domestic Product from 1997 to 2006 19 Figure 5.1 Traffic Zones in the Southern Area 33 Figure 5.2 Adjusted Volume Delay Function (Freeways) 34 Figure 5.3 2000 CTPP Auto Driver vs. 2000 NCRTPB HBW Auto Driver (Traffic

Originated from Spotsylvania/ Fredericksburg/ Stafford) 36

Figure 5.4 AM Peak Period Sluggers and Formal HOV3+ on HOV Lanes 38 Figure 5.5 PM Peak Period Sluggers and Formal HOV3+ on HOV Lanes 38 Figure 5.6a RSQ of Observed to Model Fit for I-95/I-395 HOV & GP Lanes 40 Figure 5.6b RSQ of Observed to Model Fit for I-95/I-395 HOV & GP Lanes 41 Figure 5.6c RSQ of Observed to Model Fit for I-95/I-395 HOV & GP Lanes 42 Figure 5.7 RSQ of Observed to Model Fit for US 1 43 Figure 5.8a RSQ of Observed to Model Fit for I-495, I-66 and SR 236 44 Figure 5.8a RSQ of Observed to Model Fit for I-495, I-66 and SR 236 45 Figure 6.1 Model Structure 49 Figure 6.2 Typical Diversion Curve 50 Figure 7.1 Initial traffic segments 55 Figure 7.2 Proposed Tolling Segments 56 Figure 7.3a 2015 AM Peak Toll rates derived from throughput tests 57 Figure 7.3b 2015 PM Peak Toll rates derived from throughput tests 57 Figure 7.3c 2015 Off Peak Toll rates derived from throughput tests 58 Figure 7.4a 2030 AM Peak Toll rates derived from throughput tests 58 Figure 7.4b 2030 PM Peak Toll rates derived from throughput tests 59 Figure 7.4c 2030 Off Peak Toll rates derived from throughput tests 59 Figure 7.5 2030 AM Peak Revenue Optimization Curve 62 Figure 7.6 2030 PM Peak Revenue Optimization Curve 62

I-95/I-395 HOT Lane

Contents

List of Figures

Page Figure 7.7a 2015 AM Peak Revenue Optimal Toll Rates 63 Figure 7.7b 2015 PM Peak Revenue Optimal Toll Rates 63 Figure 7.7c 2015 Off Peak Revenue Optimal Toll Rates 64 Figure 7.8a 2030 AM Peak Revenue Optimal Toll Rates 64 Figure 7.8b 2030 PM Peak Revenue Optimal Toll Rates 65 Figure 7.8c 2030 Off Peak Revenue Optimal Toll Rates 65 Figure 7.9a 2015 & 2030 AM Peak Toll Rates

(Toll Schedule v1 and revenue optimal) 71

Figure 7.9b 2015 & 2030 PM Peak Toll Rates (Toll Schedule v1 and revenue optimal)

72

Figure 7.9c 2015 & 2030 Off Peak Toll Rates (Toll Schedule v1 and revenue optimal)

72

Figure 8.1 Summary impact of sensitivity tests on Base Case revenue in 2015 75 Figure 8.2 Summary impact of sensitivity tests on Base Case revenue in 2030 75 Figure 8.3 Revenue distribution curves for 2015 and 2030 84 Figure 8.4 Revenue stream risk envelope (2012-2071) 85 Figure 8.5 Total revenue probability for model years (2015 & 2030) 86

1

I-95/I-395 HOT Lane

Introduction

1 Introduction

1.1 Context

The I-95 in Virginia is an existing 179 mile north-south freeway that runs from the North Carolina border in Greensville County to the District of Columbia border. I-395 is the 10 mile continuation of the freeway between the I-495 Capital Beltway interchange and the District of Columbia border.

The Project

Under provisions of Virginia’s Public-Private Transportation Act of 1995, Virginia Department of Transportation, in partnership with Fluor Virginia Inc. and Transurban USA, Inc. (FTU), proposes to construct and operate a system of High Occupancy Toll (HOT) lanes on I-95 and I-395 in Northern Virginia.

The proposed project would add a third lane to the existing High Occupancy Vehicle (HOV) lanes on I-95/395 from South Eads Street (Exit 8C of I-395) near the Pentagon in Arlington County, to their existing southern terminus south of Route 234 (Dumfries Road at Exit 152 of I-95) near Dumfries in Prince William County – a distance of approximately 28 miles. This would be accomplished by restriping and/or reconstructing and overlaying existing pavement. Two new lanes would be constructed to extend the system south from Dumfries to a new southern terminus at Massaponax.

In addition to the new lanes, 33 new or modified intermediate entry/exit points between the general-purpose and HOT lanes are to be provided using a combination of direct, flyover, or slip ramps. Improvements such as the addition of new park-and-ride lots and enhanced bus services are also proposed as part of the project, to encourage transit use.

Existing HOV Arrangement

I-95/I-395 between Eads St and Dumfries currently has 8-10 traffic lanes, with configurations ranging from 3-2-3 to 4-2-4 (two reversible HOV lanes in the median). Between Dumfries and Fairfax County Parkway, it is mostly configured as 3-2-3, with occasional additional lanes for general traffic gained and dropped around interchanges. From Fairfax County Parkway to the Capital Beltway (I-495) the configuration is predominantly 4-2-4. North of the Capital Beltway, the configuration includes sections of 3-2-3, 4-2-3 and 4-2-4, with additional lanes as I-395 passes through most of the interchanges. Between Dumfries and Massaponax, the I-95 has 6 lanes (3 lanes per direction) but currently has no HOV facilities. Vehicles with three or more occupants (HOV3+) and hybrids (of any occupancy) are allowed to use the HOV lanes during peak periods; HOV lanes are currently open to all traffic during the off peak.

2

I-95/I-395 HOT Lane

Introduction

Table 1.1 shows the HOV lanes’ times of operation. On weekdays, the HOV lanes are open to traffic traveling in a northbound direction between 11pm and 11am. The median-located lanes are then closed until 1pm to reverse their direction of use, and between 1pm and 9pm are open to traffic in the southbound direction. Note that operation of the HOV lanes varies slightly on Fridays.

Table 1.1 - Existing HOV Lanes Operation

HOV3+ and Hybrids All Closed

Northbound Weekdays 6:00am - 9:00am 9:00am -11:00am11:00pm - 6:00am 11:00am - 1:00pm

Southbound Weekdays 3:30pm - 6:00pm 1:00pm - 3:30pm6:00pm - 9:00pm 9:00pm - 11:00pm

Proposed HOT Lanes

The proposed I-95/I-395 HOV/Bus/HOT Lanes project is shown in Figure 1.1. The project will expand the existing High Occupancy Vehicle (HOV) lanes on I-95/I-395 from two to three lanes and extend two new lanes south to Massaponax.

The tolling plan proposed by FTU includes conversion of the existing partially restricted reversible HOV operation to a fully restricted HOT operation, with HOT operation on the new lanes south of Dumfries. Under this conversion, the HOT lanes would be dedicated for use by qualifying HOV traffic, vehicles with three or more occupants (HOV3+) and buses, which would continue to travel free.

Non-HOV traffic, single occupancy vehicles (SOV) and vehicles with two occupants (HOV2), would be permitted to travel on the facility by paying a toll. The tolls would vary depending on the level of use of the facility, with higher prices during periods of higher volumes and lower prices during periods of lower volumes. Hybrids will be subject to the same toll regime based on occupancy as conventionally-powered vehicles. Trucks will be prohibited. Hours of operation for the HOV/Bus/HOT lanes are proposed to be similar to the current tidal arrangements.

Proposed timing of the HOT lanes project is such that the northern section is anticipated to open in 2012, with the southern section following in 2015. Model runs have therefore been carried out with both the northern and southern sections in place in 2015 and 2030. Additional tests have also been carried out to derive demand for the northern section operating alone in 2015, and extrapolated back to a provide revenue stream from their opening in 2012.

3

I-95/I-395 HOT Lane

Introduction

1.2 This Study

Halcrow has been appointed to undertake an independent Traffic and Revenue Study for the I-95/I-395 project. This report presents the traffic and revenue forecasts, sensitivity tests and risk analysis developed by Halcrow.

1.3 Structure of the Report

Following the introduction, the remainder of the report is structured as follows:

• Chapter 2 provides an overview of the I-95/I-395 HOT lanes corridor; • Chapter 3 presents a review of existing HOT lanes facilities elsewhere in the US, in

order to set the I-95/I-395 assessments in context; • Chapter 4 briefly outlines the overall traffic and revenue methodology and sets out

some of the key parameters and assumptions; • Chapter 5 describes the base year model preparation (2005), including its

calibration and validation; • Chapter 6 outlines preparation of the future year models (2015 & 2030); • Chapter 7 describes the main traffic and revenue forecasts completed, including the

process used, and results obtained, in determining revenue optimal toll rates, and a suggested possible toll schedule;

• Chapter 8 considers the sensitivity tests carried out to assess the response of the model and methodology to a number of variables. It goes on to outline the risk analysis undertaken against a series of key variables, to identify a range of forecast values and the associated probability of their occurrence in the future; and

• Chapter 9 summarises the main results of the study.

There are nine appendices:

• Appendix A – 2005 Base Year Model Validation Results • Appendix B – Key Road Network Improvements (FAMPO CLRP) • Appendix C – Key Road Network Improvements (NCRTPB CLRP) • Appendix D – I-95/I-395 HOT Ingress and Egress Ramps • Appendix E – Detailed Results from Main Tests • Appendix F – Sensitivity Test Results • Appendix G – Sensitivity of Traffic Model Forecasts to Fuel Prices • Appendix H – Risk Analysis methodologies • Appendix I – 90% and 10% Confidence Interval Results

4

I-95/I-395 HOT Lane

Introduction

Figure 1.1 - Proposed I-95/I-395 HOT Lanes Project

5

I-95/I-395 HOT Lane

I-95/I-395 Corridor Review

2 I-95/I-395 Corridor Review

2.1 Principal Highways

I-95/I-395

Interstate 95 (I-95) in Virginia is an existing 179 mile north-south freeway that runs from the North Carolina border in Greensville County to the District of Columbia border. I-395 is the 10 mile continuation of the freeway between the I-495 Capital Beltway interchange and the District of Columbia border.

I-95/I-395 traverses a wide variety of rural and major urban areas and serves several military installations, including the Quantico U.S. Marine Corps Reservation near Triangle in Prince William County, Fort Belvoir in Lorton and the Pentagon in Arlington County. At present, the I-95/I-395 between Eads Street and Dumfries has 8-10 lanes with a 3-2-3 or 4-2-4 roadway arrangement with two reversible high occupancy vehicle (HOV) lanes in the median. Between Dumfries and Fairfax County Parkway, it is mostly configured as 3-2-3, with occasional additional lanes for general traffic gained and dropped around most interchanges. From Fairfax County Parkway to the Capital Beltway (I-495), the configuration is predominantly 4-2-4. North of the Capital Beltway, the configuration includes sections of 3-2-3, 4-2-3 and 4-2-4, with additional lanes as I-395 passes through most of its interchanges.

Vehicles with three or more occupants (HOV3+) and hybrids of any occupancy are allowed to use the HOV lanes during peak periods. The HOV lanes are open to all traffic during the off peak. Between Dumfries and Massaponax, the I-95 has 6 lanes (3 lanes per direction).

US Route 1

US Route 1 (US-1) is the other major north-south route between Fredericksburg and the District of Columbia (DC), and essentially runs parallel to I-95 as far as Alexandria and then along I-395 towards DC. US-1 is an undivided highway with at-grade intersections, and features both rural and urban sections. There are typically 4 lanes (2 per direction), with additional lanes at intersections and in urban areas. North of Fort Belvoir as US-1 approaches DC, it is predominantly a 6-lane highway (3 per direction). US-1 mainly serves local traffic, with daily volumes ranging from 20,000 to 35,000.

2.2 Corridor Transit Services

Figure 2.1 shows AM peak transit services for the I-95/I-395 corridor (as in 2005). During the AM peak, there are some 127 buses per hour on I-95/I-395 in the vicinity of King Street in Alexandria, with around 17 buses per hour in the vicinity of Fredericksburg. Also in the AM peak, there are also 10 Washington Metropolitan Area Transit Authority (WMATA) Metrorail trains, 2 Virginia Railway Express (VRE) trains and 2 Amtrak trains. According to a recent

6

I-95/I-395 HOT Lane


transit and TDM study1, transit and carpool usage of the I-95/I-395 corridor is relatively high. Transit mode share tends to increase progressively towards the northern part of the corridor as reflected by the higher density of services.

Figure 2.1 - 2005 AM Peak Bus Service I-95/I-395

Source: I-95/I-395 Transit/TDM Study Final Report, February 2008.

2.3 Informal Carpooling (‘Slugging’)

‘Slugging’ is the term applied to the formation of ad-hoc, informal carpools for commuting. The activity begain in the late 1970s, shortly after the establishment of the HOV restricted lanes on I-95/395. Vehicles that did not meet the required number of individuals to take advantage of HOV lanes would pick up additional passengers who were unknown to the drivers, initially at bus stops in suburban areas. Over time, locations became well known and

1 I-95/I-395 Transit/TDM Technical Advisory Committee, “I-95/I-395 Transit/TDM Study Final Report”, February 29, 2008.

7

I-95/I-395 HOT Lane


slugging more structured. According to a 2006 slugger count study2, there are approximately 19 locations where inbound sluggers can be picked-up, with 6 drop locations in the DC area (shown in Figure 2.2).

Figure 2.2 - Slug Locations

Source: www.slug-lines.com, November 14, 2008.

Based on counts in 2005 of HOV and 2006 of sluggers, as many as 50% of HOV3+ vehicles could include ‘sluggers’ in the morning peak (6:00-9:00am). No comparable information is available to quantify the amount of slugging that takes place in the evening peak, though the VHB report notes that there is more slugging activity in the morning than the evening, and that it is not uncommon for a morning slugger to use transit services for the journey home. It is likely there is some seasonality in slugging behavior, as inclement weather may discourage people from waiting for a ride. However, insufficient count data is available to confirm this. The 2006 slug counts undertaken by VHB were done in the months of April and May.

2 VHB/Vanasse Hangen Brustlin, “Dynamic Ridesharing (Slugging) Data: I-95 Corridor“, June 2006

8

I-95/I-395 HOT Lane


2.4 Historic Traffic Counts (I-95/I-395 and US-1)

Figure 2.3 shows I-95/I-395 and US-1, and major intersecting roads, between Fredericksburg and DC. Historical annual average daily traffic3 (AADT) for each route is discussed below.

Figure 2.3 - Map of I-95 and US 1

SR110

Seminary Rd

Old Keene Mill

US 1

Dumfries Rd

Russell Rd

Dale Blvd

Courthouse Rd

Garrisonville Rd

SR 3

Mudd Tavern Rd

US 1

Warrenton Rd

Glebe Rd

SR 241

Gunston Rd

Fuller Rd

Garrisonville Rd

Thornburg

Forbes St

SR 208

Fall Hill Ave

I-95/I-395

Figure 2.4 shows the yearly variation from 2001 to 2006 of traffic along selected segments of I-95/I-395 between SR-110 in Arlington County and Mudd Tavern Road in Spotsylvania County. In general, traffic volumes increase progressively towards the northern part of the study corridor. In 2006, annual average daily traffic (AADT) was 97,000 north of Mudd Tavern Road and 139,000 north of Garrisonville Road in Stafford County. From Joplin Road to Prince William Parkway, the I-95/I-395 carries 160,000-187,000 AADT. The average daily traffic volume exceeds 280,000 just south of I-495. Within the Capital Beltway, the AADT ranges from 165,000 to 209,000.

3 Traffic Engineering Division of the Virginia Department of Transportation, “The Daily Traffic Volume Estimates Including Vehicle Classification Estimates”, http://www.virginiadot.org/info/ct-TrafficCounts.asp, June 2008.

9

I-95/I-395 HOT Lane


Figure 2.4 - Traffic Profiles of I-95 (SR-110 to Mudd Tavern Road)

0

50,000

100,000

150,000

200,000

250,000

300,000SR

110

SR 2

7

SR 1

20

Qua

ker L

ane

Kin

g S

t

Sem

inar

y R

d

Duk

e S

t

Fairf

ax C

ount

y Li

ne

Old

Kee

ne M

ill

US

1

Occ

oqua

n R

iver

Prin

ce W

illia

m P

kwy

Dal

e B

lvd

Dum

fries

Rd

Jopl

in R

d

Rus

sell

Rd

Gar

rison

ville

Cou

rthou

se R

d

War

rent

on

SR 3

US

1

Mud

d Ta

vern

Rd

AA

DT

2001 2002 2003 2004 2005 2006

In general, traffic on the I-95 corridor has been increasing. For the period of 2004 to 2006, some sections of the corridor have shown substantial growth. Closer to Washington DC, sections show only marginal increases, which are likely to be as a result of capacity constraints. In 2004, traffic volumes increased rapidly on freeway segments between Garrisonville Road and Dumfries Road, and between King Street and SR 120.

Prior to 2004, the traffic profile on sections between Courthouse Road and Dale Boulevard indicates a mix of long-distance commuting trips and shorter-distance local trips, as illustrated by on/off ramp volumes at intermediate interchanges. However, over the period 2004 to 2006, I-95/I-395 appears to draw considerably more traffic, suggesting that population and employment growth in the outer suburbs has resulted in more long-distance commuting trips.

US-1

Figure 2.5 shows the yearly traffic distribution along selected sections of US-1 between Glebe in Arlington County and Thornburg in Spotsylvania County from 2001 to 2006. Overall, traffic growth on US-1 has been relatively stable, with daily traffic volumes varying between 20,000 and 35,000 on most sections of the corridor. Some sections of US-1 show higher traffic volumes as a result of variations in the numbers of lanes. For instance, sections north of Fort Belvoir (Gunston Rd) heading for DC typically have 6 lanes (3 per direction).

10

I-95/I-395 HOT Lane


Figure 2.5 - Traffic Profiles along US-1 (Glebe to Thornburg)

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000G

lebe

Rd

King

St

SR 2

41

Gun

ston

Rd

US

1 Fr

aley

Blv

dSo

uth

Int.

NC

L D

umfri

es

Fulle

r Rd

Gar

rison

ville

Rd

Forb

es S

t

Fall H

ill Av

e

SR 2

08

Thor

nbur

g

AA

DT

2001 2002 2003 2004 2005 2006

Towards D.C.

2.5 I-95/I-395 Traffic Data

Hourly Profiles

Figures 2.6 to 2.8 show the hourly traffic profiles4 at three locations on I-95/I-395, for an average weekday in June/July 2006. These locations are:

• Jefferson Davis Highway to Germanna Highway (southern section); • Joplin Road to Dumfries Road (transition area); and • Edsall Road to Duke Street (north of Capital Beltway).

The hourly profile on the southern section between Jefferson Davis Highway and Germanna Highway shows a slight afternoon peak but no defined morning peak, which indicates a typical daily traffic profile for a rural highway. For sections from Dumfries Road towards the northern part of the corridor, the hourly traffic profile reflects a more typical urban highway pattern, with a distinct morning and afternoon peak. It is also evident that the number of hours that can be considered within the peak periods (both AM and PM) expands progressively towards the northern part of the study corridor.

4 Fluor Transurban, “schematic_i95 with counts 5-7-07 summary.xls”.

11

I-95/I-395 HOT Lane


Figure 2.6 - I-95/I-395 2006 Hourly Profiles (Jefferson Davis Highway to Germanna Hwy)

Hourly Profile (Jefferson Davis Hwy to Germanna Hwy)

0

2000

4000

6000

8000

10000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour of Day

Hou

rly T

raff

ic V

olum

e (v

ph)

Northbound Southbound

Figure 2.7 - I-95/I-395 2006 Hourly Profiles (Joplin Road to Dumfries Road)

Hourly Profile (Joplin to Dumfries)

0

2,000

4,000

6,000

8,000

10,000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour of Day

Hou

rly T

raff

ic V

olum

e (v

ph)


12

I-95/I-395 HOT Lane


Figure 2.8 - I-95/I-395 2006 Hourly Profiles (Edsall Road to Duke Street)

Hourly Profile (Edsall to Duke)

0

2,000

4,000

6,000

8,000

10,000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour of Day

Hou

rly T

raff

ic V

olum

e (v

ph)


Figure 2.9 - I-95/I-395 2005 Average Day of Week Profiles

0

20,000

40,000

60,000

80,000

100,000

Mon Tue Wed Thur Fri Sat Sun

Dai

ly T

raff

ic V

olum

e (v

ph)

Morris - Massponas Courthouse - Garrisonville Edsall - Duke

13

I-95/I-395 HOT Lane


Day of Week Profile

Figure 2.9 shows weekly traffic profiles for selected locations along I-95/I-395, for an average week in 2005. Between Morris Road and Massaponax, the average daily weekday traffic is generally lower than daily traffic on weekends, suggesting a high level of discretionary travel using the corridor on weekends. Stafford County indeed offers numerous recreational amenities, including private marinas, public fishing lakes, parks, athletic fields and golf courses. Conversely, for the two northern sections, average weekday traffic is higher than at weekends, reflecting the higher proportion of commuter traffic.

Seasonal Variation

Figure 2.10 shows monthly traffic volumes for key segments on the I-95/I-395 corridor. The summer months are more peaked for southern than northern sections, as a result of increased recreational activity in summer months.

Figure 2.10 - I-95/I-395 2005 Monthly Traffic Profiles

0

20,000

40,000

60,000

80,000

100,000

Jan Feb March April May June July Aug Sept Oct Nov Dec

Mon

thly

Tra

ffic

Vol

ume

(MA

DT)

Morris - Massponas Courthouse - Garrisonville Edsall - Duke

2.6 Travel Time Data

Travel time surveys between 6:00 AM and 6:30 PM were organized by Vollmer in June 2006. Observed journey times are summarized in Table 2.1. The routes included:

• I-95/I-395 GP lanes between Courthouse Road / SR-208 in Spotsylvania County and the George Washington Memorial Parkway in Arlington County;

14

I-95/I-395 HOT Lane


• I-95/I-395 HOV lanes between the southern start / terminus just south of Dumfries Road / SR-234 in Prince William County and the George Washington Memorial Parkway in Arlington County; and

• US Route 1: Courthouse Rd/SR-208 in Spotsylvania Co. & 23rd Str in Arlington Co.

Table 2.1 - Travel Time Survey Results

Road Description Type Dir AM (6-9 AM) PM (4-7 PM) OP (1-3 PM)

NB 75.7 47.6 43.7

SB 48.9 85.7 48.7

NB 29.8 n/a n/a

SB n/a 28.0 24.7

NB 99.1 85.8

SB 71.6 93.3

Observed

US-1 Courthouse Rd / SR-208 to 23rd Street in Arlington County

All purposes

Courthouse Rd / SR-208 to George Washington Memorial Parkway GP

I-95 / I-395

HOVSouthern Start / Terminus to George Washington Memorial Parkway

Not surveyed

2.7 Origin/Destination Data

There is no recent origin-destination (OD) survey data in the study area suitable for model development. Also, as 2007 household travel survey data was also not available, the most recent OD datasets available for model verification are the 2000 Census Transportation Planning Package data (CTPP) and 2006 slugger count data. Note that the 2000 CTPP OD data can only be used to verify commuting trips. Figures 2.11 and 2.12 show the distribution of origins and destinations (respectively) within the counties that make up the study area, based on 2000 CTPP OD data5.

Figure 2.11 & 2.12 Location Codes

DC CR – District of Columbia, core DC NC – District of Columbia, non-core MTG – Montgomery County PG – Prince George County ARLCR – Arlington County, core ARNCR – Arlington County, non-core ALX – Alexandria FFX – Fairfax County LDN – Loudoun County PW – Prince William County FRD – Frederick County

CAR – Carroll County HOW – Howard County AAR – Anne Arundel County CAL – Calvert County STM –St.Mary’s County CHS – Charles County FAU – Fauquier County STA – Stafford County CL/JF – Clarke County, VA/Jefferson County, WVa SP/FB – Fredericksburg/Spotsylvania County KGEO – King George County

5 National Capital Region Transportation Planning Board, “NCRTPB Travel Forecasting Model, Version 2.2, Specification, Validation, and User’s Guide”, March 1, 2008.

15

I-95/I-395 HOT Lane


Figure 2.11 - 2000 CTPP Trip Origin (Auto Driver)

2000 Trip Origin

0%

5%

10%

15%

20%

25%

30%

DC

CR

DC

NC

MTG PG

ARLC

R

ARN

CR

ALX

FFX

LDN

PW FRD

CAR

HO

W

AAR

CAL

STM

CH

S

FAU

STA

CL/

JF

SP/F

B

Traf

fic D

istr

ibut

ion

(%)

Figure 2.12 - 2000 CTPP Trip Destination (Auto Driver)

2000 Trip Destination

0%

5%

10%

15%

20%

25%

30%

DC

CR

DC

NC

MTG PG

ARLC

R

ARN

CR

ALX

FFX

LDN

PW FRD

CAR

HO

W

AAR

CAL

STM

CH

S

FAU

STA

CL/

JF

SP/F

B

Traf

fic D

istr

ibut

ion

(%)

2.8 Truck Traffic

Figure 2.13 shows the 2005 average daily truck traffic percentage along selected segments of I-95/I-395 between SR-110 in Arlington County and Mudd Tavern Road in Spotsylvania County. Truck traffic proportions diminish from around 18% in Spotsylvania to 10% in Prince William County, down to 3% north of the Fairfax County Line.

16

I-95/I-395 HOT Lane


Figure 2.13 - 2005 Average Daily Truck Profiles of I-95 (SR 110 to Mudd Tavern Road)

0

10,000

20,000

30,000

40,000

50,000S

R 1

10

SR 2

7

SR

120

Qua

ker L

ane

Kin

g St

Sem

inar

y R

d

Duk

e St

Fairf

ax C

ount

y Li

ne

Old

Kee

ne M

ill

US

1

Occ

oqua

n R

iver

Prin

ce W

illia

m P

kwy

Dal

e B

lvd

Dum

fries

Rd

Jopl

in R

d

Rus

sell

Rd

Gar

rison

ville

Cou

rthou

se R

d

War

rent

on

SR 3

US

1

Mud

d Ta

vern

Rd

AA

DT

Figure 2.14 - 2005 Average Daily Truck Profiles along US 1 (Glebe to Thornburg)

0%

5%

10%

15%

20%

Gle

be R

d

King

St

SR 2

41

Gun

ston

Rd

US

1 Fr

aley

Blv

dSo

uth

Int.

NC

L D

umfri

es

Fulle

r Rd

Gar

rison

ville

Rd

Forb

es S

t

Fall H

ill Av

e

SR 2

08

Thor

nbur

g

Truc

k %

Towards D.C.

17

I-95/I-395 HOT Lane


Figure 2.14 shows the 2005 average daily truck traffic percentage along selected sections of US-1 between Glebe in Arlington County and Thornburg in Spotsylvania County. Average daily truck proportions on US-1 are lower than I-95/I-395, and relatively consistent along US-1 at 2-3%, rising only at Thornburg to around 5%.

2.9 Land Use and Economic Trends

Land use and economic data are fundamental to the development of the transport models and the derivation of future traffic forecasts for the I-95/I-395 project. This section provides an overview of the demographics and economic indicators for the region.

Population Growth

The population for selected jurisdictions in the study corridor and the state of Virginia is shown in Table 2.2 for 1990 to 2000. Between 1990 and 2000, the population of Virginia has increased steadily at 1.3% per annum The population in the central jurisdictions has remained relatively stable, whereas the inner suburbs of the region have grown at 1.7% per annum. With a growth of 3.2% per annum, the outer suburbs have been experiencing the fastest growth rate.

Table 2.2 - Historic Population for Selected Jurisdictions (1990-2000) Selected Jurisdictions 1990 2000 Annual Growth

1990-2000District of Columbia 606,900 572,100 -0.6%Arlington County 171,200 189,200 1.0%City of Alexandria 111,500 129,100 1.5%

Central Jurisdiction 889,600 890,400 0.0%Montgomery County 74,300 83,600 1.2%Prince George's County 27,600 33,200 1.9%Fairfax Co./City/Falls Church 851,100 1,007,100 1.7%

Inner Suburbs 953,000 1,123,900 1.7%Prince William County 216,600 283,800 2.7%Stafford County 62,600 93,600 4.1%City of Fredericksburg 19,400 19,300 -0.1%Spotsylvania County 58,800 91,600 4.5%

Outer Suburbs 357,400 488,300 3.2%Virginia 6,216,900 7,104,100 1.3%

Source: U.S. Census Bureau, Virginia Employment Commission.

18

I-95/I-395 HOT Lane


Employment Growth

Figure 2.15 shows total employment for the Washington-Arlington-Alexandria Metropolitan Statistical Area, DC-VA-MD-WV (MSA)6 from 1969 to 2006. In 2006, there were 3.8 million jobs in the DC-VA-MD-WV area. The average employment growth rate during 1969 to 1982 was 2% per annum, followed by a sudden increase in employment growth at a rate of 4.4% per annum between 1983 and 1990. Thereafter, the growth rate has been maintained at approximately 2% per annum.

Based on recent growth trend analysis for 2005-2030 done by the National Capital Region Transportation Planning Board (NCRTPB)7, job growth in Northern Virginia is anticipated to outpace growth in the Maryland suburbs and the District of Columbia. Despite the significant percentage increases in employment in the outer suburbs of Virginia and Maryland, the central jurisdictions and inner suburbs will still have the highest levels of employment.

Figure 2.15 - Employment for DC-VA-MD-WV area (1969-2006)

Employment (1969-2006)

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

4,000,000

4,500,000

1969 1973 1977 1981 1985 1989 1993 1997 2001 2005

Year

3.83 million

Source: Bureau of Economic Analysis.

6 The DC-VA-MD-WA Metropolitan Statistical Area includes: District of Columbia (DC), Calvert (MD), Charles (MD), Frederick (MD), Montgomery (MD), Prince George’s (MD), Arlington (VA), Clarke (VA), Fauquier (VA), Loudoun (VA), Stafford (VA), Warren (VA), Alexandria (VA), Fairfax, Fairfax City + Falls Church (VA), Spotsylvania + Fredericksburg (VA), Jefferson, WV.

7 The National Capital Region Transportation Planning Board, “Growth Trends to 2030: Cooperative Forecasting in the Washington Region (Fall 2007)”, http://www.mwcog.org/planning/planning/trends/, June 2008.

19

I-95/I-395 HOT Lane


Real Gross Domestic Product (Real GDP)

Real GDP/capita is used as a proxy for average incomes in modeling responses to tolls. Figure 2.16 compares the Virginia Real Gross Domestic Product (Real GDP) and Maryland real GDP against the real GDP of the United States. Between 1997 and 2006, National GDP has increased at 3% per annum. By contrast, Maryland has increased at 3.5% per annum, while Virginia has increased at 4% per annum.

Figure 2.16 - Gross Domestic Product from 1997 to 2006

Gross Domestic Product (1997-2006)

80

90

100

110

120

130

140

150

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Year

Inde

x (1

997

= 10

0)

Maryland Virginia United States

Further information about how forecast values of GDP/capita are used in the analysis can be found in Chapter 4.

20

I-95/I-395 HOT Lane

Review of Existing HOT Lanes

3 Review of Existing HOT Lanes Facilities

There are a number of HOT lane facilities currently operating in North America that differ in operational or organizational set-up. It is therefore important to understand the features of individual schemes, in particular including the similarities and differences, and consider the experiences elsewhere when considering the potential demand for the I-95/I-395 HOT lanes. The following sections provide an overview of existing HOT lanes facilities, as well as several facilities that are in the construction or planning phases.

Existing HOT lanes in North America typically operate on heavily congested corridors feeding major business districts. They range from 6 to 38 miles in length, but most are around 10 miles long. While some only offer free usage to HOV3+ vehicles (as proposed for the I-95/I-395 HOT lanes), most currently allow free access to vehicles with two or more occupants (HOV2+). A common feature though is that in most cases trucks are not permitted to access the HOT lanes. Toll charges for existing facilities vary from a simple monthly fee for access to complex dynamic charges that vary according to congestion levels. Often the HOT lane facilities will restrict traffic volumes such that they deliver a minimum level of service (such as LOS B) or travel speed (e.g. 55mph). Table 3.1 includes some summary information.

3.1 Existing HOT lanes in North America

91 Express Lanes

The 91 Express Lanes in Orange County, CA opened in 1995 as the nation’s first facility to implement value pricing and the first fully automated tollway in the world. Only vehicles with valid FasTrak transponders are permitted to enter the Express Lanes. The facility

is a ten mile 12-lane highway consisting of four general purpose and two HOT lanes in each direction. The HOT lanes operate in the median and are separated from the adjacent GP lanes by a painted buffer and pylons, with access only permitted at the end points.

SOV and HOV2+ are required to pay a toll to use the HOT lanes. Toll rates vary by time of day on a pre-set schedule, from $1.25 to a maximum of $9.50 (note that the $9.50 toll only applies in the eastbound direction between 3pm to 4pm on Fridays, and is the highest per mile toll for any toll road in the country). The highest toll in the morning rush hour, 7am to 8am westbound Monday to Thursday is $4.35. HOV3+ are generally permitted to use 91 Express Lanes without paying a toll, with the exception on weekdays between 4pm and 6pm in the eastbound direction, a toll discounted by 50% has to be paid by HOV3+. There are several discount incentive plans offered to customers. For example, for a $20 per month fee, customers can belong to the ‘91 Express Club’, which offers a $1.00 discount on every trip.

21

I-95/I-395 HOT Lane


Annual daily traffic on both GP and HOT lanes is approximately 335,000, with the HOT lane volume close to 40,000 per day. Average travel time savings for users over the 10 mile section is typically 20-25 minutes. Annual toll revenue in 2007 was approximately $40 million. with the average toll paid per trip around $3.00.

I-15 Express Lanes

An 8-mile reversible HOV facility was opened on Interstate 15 (I-15), a major north-south urban freeway in San Diego California, in 1988. An initial attempt was made to increase the usage of

the HOV lanes in 1996, by issuing permits to use the HOV lanes irrespective of vehicle occupancy. Permits were distributed on a first-come/first-served basis for a fixed fee of $50 per month. In 1998, the HOV lanes were converted to a fully electronic reversible HOT lanes. In September 2008, a new 4.5 mile section opened offering two HOT lanes per direction. Further expansion plans envisage extending the facility to a length of over 20 miles by 2012.

Over the original 8 mile section, I-15 is a 10-lane facility with four general purpose lanes in each direction (some parts have five lanes) and a median HOT lane facility consisting of two reversible lanes. The completed facility will provide flexible entry/exit locations at 30 locations with four HOT lanes (generally two in each direction) over the entire length, and a moveable median barrier that would allow 3:1 tidal operation during the busiest time periods. The reversible section operates in the southbound direction (inbound commute) from 5:30am to 12:00pm Monday to Friday, with northbound direction (outbound commute) operation from 1pm to 7pm It has northbound-only operation for 24-hours on Saturdays and Sundays.

The system allows HOV2+ carpools, vanpools, buses and motorcycles to use the facility free of charge. Dynamic tolling is featured, using the FasTrak electronic toll collection system. Rates vary between $0.50 and $8.00 depending on congestion levels. Fees are posted at the entry points and can change every 6 minutes as required to preserve LOS C conditions in the HOT lanes. The newly opened 4.5 mile section operates in two directions for 24 hours per day and currently offers an introductory rate of $0.50 to use this segment. Traffic on I-15 ranges from 170,000 to 295,000 vehicles per day. Total HOT lane volume is approximately 20,000 vehicles per day, of which 75% (of weekday traffic) can use the lanes without paying a toll. Toll-paying HOT lane traffic generates approximately $2 million per year in revenues.

Katy Freeway

Houston’s Katy Freeway HOV lane opened in 1984 as a 12-mile reversible median facility open to buses and vanpools. Over the following decade, several changes were made to allow others to use the HOV lane, in an effort to improve utilization. In 1998,

QuickRide was introduced which enabled vehicles with two or more occupants (HOV2+) to access the lanes during peak hours for a $2 charge. Vehicles with three or more occupants

22

I-95/I-395 HOT Lane


(HOV3+) are permitted to use the facility at all times free of charge. HOV2+ trips remain free during off-peak hours. Single-occupant vehicles are barred at all times. QuickRide features an electronic transponder (Trip Tag) which can be used on six HOV lanes in the Houston Metro area. In 2006, average daily traffic on the Katy freeway was around 250,000 per day.

In 2008, the Katy Freeway was widened from 11 lanes to 18 lanes between I-610 and State Highway 6, including four main lanes, three frontage road lanes and two toll (or ‘managed’) lanes in each direction over a 23 mile length. The managed section is a transponder only facility, with electronic fee collection at three on-line tolling locations, with multiple access points via slips and direct access ramps. For an initial period (to spring 2009), Metro and school buses are permitted to use ‘managed’ facility free of charge at all times. Motorcycles and HOV2+ will have free access during HOV hours (5am-11am and 2pm-8pm). Following this initial period, the lanes will be open to SOV traffic during non-peak hours on payment of a toll. Depending on congestion levels in these trials, SOVs may also be permitted to use the lanes during peak hours on payment of an appropriate toll. The second phase is anticipated to involve a dynamic pricing scheme, in order to maintain a minimum speed of 45 mph.

MnPass 394

Eleven miles of HOV lanes were constructed on I-394 in Minneapolis, Minnesota, in 1988, subsequently being converted to HOT lanes in May 2005 (MnPass). I-394 is a 6-lane facility with two general purpose and

one HOT lane per direction. MnPass was the first HOT lane facility implemented in the United States without barrier-separation, using just ‘diamond’ lanes. Over the 8 miles between I-494 and Minnesota State Highway 100 (MIN-100), HOT lanes are separated from other traffic lanes by double white lines, with limited entrance and exit points. For the remaining 3 miles between MIN-100 and I-94 downtown, the HOT lanes form a reversible facility separated from other lanes by concrete barriers. This segment operates tidally to accommodate eastbound traffic in the morning and westbound traffic in the afternoon. The white line separated and reversible expressway section has multiple mid-point access locations.

MnPass 394 uses a dynamic pricing system. Tolls generally range from $1 to $4, and have a potential maximum of $8. Prices are set to ensure minimum speeds of 50-55 mph. General traffic is restricted from 6am to 9am into Minneapolis (westbound) and from 3pm to 6pm from Minneapolis to the suburbs (eastbound). Other hours are charged at $0.25 on the reversible section, but are free on the one-way section. SOVs were previously allowed to use the lanes without paying a toll in off-peak times, but subsequently only permitted when the lanes are designated as ‘open’ on variable-message signs. The lanes are free to buses, motorcycles, and HOV2+, regardless of time of day, and these vehicles do not require transponders to use the lanes. In 2006, the average daily traffic on I-394 ranged from 100,000 to 150,000. Annual toll revenue was estimated at approximately $1.5 million per year.

23

I-95/I-395 HOT Lane


I-25 Express Lanes

The Denver I-25 Express Lanes opened in 2006. Interstate 25 (I-25) is an 8-lane facility consisting of three general purpose lanes per direction and two reversible express lanes operating in the median over a 6.6 mile section between downtown Denver and 70th Avenue. HOV2+, buses, and motorcycles travel without paying a toll as long as they are in the lane marked ‘HOV’ when they pass through the toll collection points near 58th Avenue. The HOT lanes operate 20 hours a day, closing to reverse the facility direction. Tidal operation

sees southbound use into Denver until 10am, closure until 12pm and the reverse direction thereafter. Toll rates for the I-25 Express Lanes vary by time of day on a fixed schedule (from $0.5 to $3.25 per trip) and are collected automatically with an ExpressToll transponder. Total average daily traffic is approximately 240,000, with 12,000 using the express lanes and approximately 4,000 paying the toll. In 2008, annual toll revenue was $2.5 million.

SR-167 Good To Go

SR-167 south of Seattle WA is a 6-lane facility with two general purpose lanes and one HOT lane per direction on a 9 mile section between Renton and Auburn. The HOT lanes opened in May 2008 as a pilot project. HOV2+, buses and motorcycles can access the

lanes free of charge. The lanes operate in both directions between 5:00 a.m. to 7:00 p.m. and feature dynamic tolling using the ‘Good To Go’ electronic transponder system. There are no toll booths. Readers above the HOT lane interrogate transponders when drivers pass under one on entering the HOT lane, and a toll will debit from their preset account electronically. A white light also flashes so that state patrol can see they are legally entering the lane. A ‘shield’ is used to deactivate the transponder when driving in a carpool. SOV tolls range between $1 and $9, depending on where the driver enters the HOT lane and congestion levels. Average daily traffic is approximately 120,000, with daily HOT lane volume of 19,000 of which only around 1,250 pay a toll. In the short time that SR-167 has been operational; it has averaged monthly revenues of $25,000.

I-15 Express Lanes

The I-15 in Salt Lake City is an 8-lane facility with three general purpose and one HOT lane per direction operating over a 38 mile section. The facility opened in September 2006 when pre-

existing HOV lanes were converted to the HOT (‘Express’) lanes. The facility allows free access for HOV2+, buses, motorcycles, emergency vehicles and clean fuel vehicles. A limited number of SOVs can purchase a permit (decal) to access the lanes for a fee of $50/month. Vehicles can access the Express Lanes at the beginning and end points as well as at 16 intermediate points along the corridor, indicated by a dotted white line. The remainder of the Express Lanes is marked with a double white line, providing a two-foot buffer between the general purpose and Express Lanes. The Utah Department of Transport

24

I-95/I-395 HOT Lane


(UDOT) allows up to 2,200 registered SOVs per month to use the facility (the number is restricted to ensure that speeds do not fall below 55 mph). Plans are underway to implement electronic payment with congestion pricing in 2010.

3.2 HOT lanes Proposed or Under Development

I-95 Express – Miami

I-95 in Broward County Miami is a 6-lane facility with two general purpose lanes and one HOV lane per direction. An HOV lane facility has recently been constructed in the median, and it is now proposed to convert this to a HOT

lane facility. The intention is to incorporate a moveable barrier to allow the HOT lanes to be reversible and electronic variable pricing. Eventually, the lanes will stretch from I-395 in Downtown Miami to Broward Boulevard in Fort Lauderdale, a distance of 24 miles. It is envisaged that tolls will be collected via the Sun Pass transponder, with prices expected to range between $0.25 and $2.65 for the initial 7 mile section. No toll will be charged to emergency vehicles, registered vanpools, registered carpools of 3 or more persons commuting to work, registered hybrid vehicles, motorcycles, public buses, and school buses. Trucks cannot use the lane. Results from Florida Department of Transportation’s preliminary feasibility study concluded that variable-priced toll revenues could support most of the capital costs of the project. An investment grade traffic study is currently underway, with further activities anticipated to develop an implementation plan, including public education/outreach, seeking proposals from private-sector developer/operators, and a monitoring/evaluation plan.

I-95 Express Toll Lanes – Baltimore

The I-95 Express Toll Lanes in east Baltimore city is a 10-mile segment that is currently under construction. Once complete, there will be two express lanes and four general-purpose lanes in each direction. Congestion pricing will be used to manage the traffic in the express lane.

I-495 Fairfax

The I-495 Virginia HOT Lanes Project, in Fairfax, is a 14 mile facility, currently under construction and scheduled to open in 2013. Tolls will be dynamically priced according to congestion levels, as well varying by time of day. Rateswill range from $0.10 to $1.00 depending on congestion.

I-680 Almeda County Express lanes

The I-680 Express Lane project is located on a 14-mile stretch of the southbound I-680 between Highway 84 in the north and Highway 237 in the south of Alameda County, California.

Construction began in late October 2008 to convert an existing HOV lane to an Express lane with new striping, three specific entry and exit points, overhead electronic signs, and a

25

I-95/I-395 HOT Lane


FasTrak electronic toll collection system. The new Express Lane will be separated from general purpose lanes by triple lines except at entry and exit points. Carpools will continue to use the lane without charge, while SOVs will be able to use the lane for a toll that will vary depending on the level of congestion. The operation will be fully electronic. The project is due to open in late 2010.

LBJ Freeway

The local consensus building team for reconstruction of the LBJ Freeway (I-635) in north Dallas has recommended that the busiest stretch of the 8-lane freeway east of Dallas Fort Worth airport be rebuilt with eight free and six HOT lanes. The section that is planned for conversion is a total of 9.5

miles. The lanes will provide options for travelers to by-pass general purpose lane congestion through a value pricing mechanism, which includes dynamic congestion pricing; half price for HOV usage during rush hours (peak operating periods) as well as other incentives.

C470 Express Lanes

An Express Lanes Feasibility Study (ELFS) was conducted to examine the feasibility of tolled express lanes to address congestion along the corridor from South I-25 to West I-70 in Denver. The express toll lane alternative would add two tolled express lanes in each direction on C-470

between Kipling and I-25. The express lanes would be built using the existing median, and the existing lanes on C-470 would remain free. Toll collection would be electronic only, with transponders that can also be used on other facilities, including E-470 and the Northwest Parkway. Drivers will have access to the barrier-separated express lanes at a number of locations, generally indirectly through slip ramps, although specific ramps will provide direct access at two locations. Toll pricing will be variable according to demand, allowing for consistent free-flow traffic in the express tolled lanes. Estimated opening day peak-hour toll to travel the entire 12.5-mile corridor will be approximately $2.50.

3.3 Violation of HOT Lane regulations

Traffic counts identifying HOV lane users indicate that violation of the high occupancy rules can be significant. For instance, in the existing I-95/I-395 HOV lanes, violators can be around 30% of all traffic in the lanes (Table 5.1).

Analysis of existing HOT lane facilities indicates that the violation rate varies greatly. In particular though, violation rates are dependent on the design of the HOT lanes, tolling facilities and the degree of involvement from the police in the enforcement of the facility rules. Information has been found on violation rates for a number of the facilities discussed in this chapter – presented in Table 3.1.

26

I-95/I-395 HOT Lane


Table 3.1 - Violations in existing HOT Lanes

Project Details Geometry Toll structure Penalty Violation Rate SR91 Express Lanes (Orange County, CA)

10 miles, 2 lanes in each direction

Express lanes in a separate barrier section

Tolls fixed depending on time of day and day of the week $1.25 to $9.50

Cost of the toll plus up to $55 in additional fees

2%-4%

Route I-15 (San Diego)

12.5 miles, 2 reversible lanes with shoulder

Two entry/exit points at the terminus points. Barrier section.

Dynamic pricing - $0.50 to $8 depending on distance travelled, time of day and level of congestion

$341 moving violation for first offense.

15%

I-394 MnPass Express Lanes (Minneapolis)

10 miles, 1 lane in each direction

Access points through breaks in double white line to correspond with a gantry

Dynamic pricing to maintain traffic at 55mph – peak hour ave $1-$4, $0.25 min, $8 max

$142 fine. Partnerships with local police departments.

4% in reversible section; 9% in diamond lane section

I-25 Express Lanes (Denver)

7 miles, 2 lanes in one direction (1 for HOV2+, 1 for Toll User)

Express lanes in a separate barrier section with specific entry and exit points depending on time of day

Set tolls depending on time of day: $0.50 - $3.50

$70 fine AM – 10% PM – 30%

I-15 Express Lanes (Salt Lake City)

38 miles, 1 lane in each direction

19 access points which consist of a 3000-9000 feet break in the double white line. In addition 4 entry/exit points.

Fixed permit system currently of $50/month for solo drivers. Will be converting to an ETC system in 2010 to maintain speeds at 55mph.

$82 fine 2 week blitz March 2007 – 216 citations for solo drivers without a decal; 82 citations for crossing the double white line.

SR167 (Seattle) 9 miles, 1 lane in each direction

Access points through breaks in double white line to correspond with a gantry

Dynamic pricing to maintain traffic at 55mph. Average toll $1.07, highest $6 (at Nov 08)

Minimum of $124 <5%

QuickRide System (I-10 & US 290, Houston)

13 miles on I-10, 10 miles on US 290

Single-lane reversible in a separate barrier section

Free for HOV 3+ drivers, $2 each way for HOV 2 drivers. $57.50 total initial set-up fees

Maximum $200 fine. Enforcement by METRO police stationed at HOV lane exit locations.

55%-65% (2005)

27

I-95/I-395 HOT Lane

T&R Forecast Assumptions

4 Traffic & Revenue Forecasting Assumptions

The methodology employed to produce the independent traffic and revenue forecasts in this study has two basic stages.

• Initially, a traffic model is used to develop traffic volumes for the modeled time periods (AM and PM peaks and Off-Peak in 2015 and 2030).

• These results are then ‘post-processed’ to determine revenue from HOT lane tolls, as well as to scale the modeled values to daily and annual figures.

Development of the traffic model itself consists of two stages, including setting-up the Base Year model (2005), and subsequently using this as the basis for two future-year models (2015 and 2030). Chapters 5 and 6 describe development of the base year and future year traffic models respectively.

The remainder of this chapter sets out some key parameters and assumptions used in both the traffic modeling and post-processing.

4.1 Value of Time (VOT) and toll escalation

A key parameter (in particular latterly for the toll diversion model) is the value of time. Values of time of $15.10 per hour for autos and $30.20 per hour for heavy trucks have been used. Note that these values are for 2005, to coincide with the model base year. These are based on a benchmark analysis of Halcrow’s in-house database of values of time and GSP/capita across North America. In 2005, Halcrow’s database gives an equivalent VOT range for North America of US$11.50-18.00 per hour, with an average of US$14.00 per hour. The VOT of $15.10 is slightly above the average in Halcrow’s database, consistent with economic factors in the study area compared to average values. Halcrow does not have an equivalent dataset for trucks, but has found that the overall ratio between heavy truck and car values of time is typically 2.0. Hence, in practice, tolls charged for trucks are typically 2-3 times those charged to cars.

It is generally accepted that willingness to pay for time-savings increases with income and, assuming that economic growth outperforms population growth, over time as well. Real GDP/capita is used as a proxy for average incomes, and an elasticity of VOT to GDP/capita of 0.75 has been assumed for this study.

Increases in the value of time (over time) are also exhibited by truck drivers, largely reflecting real increases in their salaries. On this basis it can be argued that the elasticity of truck drivers’ VOT to GDP/capita is close to unity. Historically, the range used by most economists

28

I-95/I-395 HOT Lane


is between 0.5 and 1.0 but many of the later assessments tend to the higher end of this range. Table 4.1 provides a summary of our Real GDP/Capita assumptions between the model years of 2015 and 2030.

Table 4.1 - Real GDP/Capita Assumptions

Year Real GDP/Capita

2015 1.7%2016 1.7%2017 1.7%2018 1.6%2019 1.5%2020 1.5%2021 1.5%2022 1.5%2023 1.5%2024 1.5%2025 1.5%2026 1.5%2027 1.5%2028 1.4%2029 1.4%2030 1.4%

Chapter 8 provides details of value of time sensitivity testing.

4.2 Vehicle Operating Costs (VOC)

The auto and truck operating costs (e.g., fuel and oil, maintenance and tires) were adjusted to represent 2005 conditions using information from the American Automobile Association (AAA). The vehicle operating costs in 2005 $ are as follows:

• Car: 16.7 cents/mile • Truck: 44.9 cents/mile

Chapter 8 has details of the vehicle operating cost sensitivity testing.

4.3 Annual Expansion Factor

The annual expansion factor will be a function of the level of congestion during weekends, VOT and vehicle occupancy by different trip purposes. Considering overall traffic counts, our examination of traffic count data along the I-95/I-395 corridor indicates high expansion factors from average weekday conditions (as represented by the traffic model) to annual totals. A selection of annualization factors are shown in Table 4.2, indicating that values for the corridor are approximately 350 at the northern end and even higher (360+) at the southern end. This suggests that traffic volumes on weekends are as high as those on weekdays.

29

I-95/I-395 HOT Lane


However, while this may be the case in terms of overall traffic volumes on the corridor, a factor of 350 is unlikely to be appropriate for expanding daily HOT lane volumes. This is because a high proportion of weekend trips are likely to be leisure orientated and thus discretionary, and with greater tendency towards carpooling. In addition, traffic volumes on weekends are less likely to result in high directional peak periods.

Table 4.2 – Observed Annualization Factors

Route Location Factor I-95 Thornburg to Massaponax Church Rd 385 I-95 Courthouse Rd to Garrissonville Rd 365 I-395 Courthouse Rd to Garrissonville Rd 362 I-395 Edsall Rd to Duke Rd 348 I-395 Edsall Rd to Duke Rd 350

There is limited detailed information available from similar HOT lane facilities about their use at weekends and off-peak. Information is available for SR91 in California, which has a similar set of annualization factors overall to the I-95/I-395 corridor. The SR91 data indicates that an annualization factor from average weekday to annual total of 297 has been observed for the last 5 years. Recent data indicates that this may have risen slightly, so we have assumed a figure of 300 in the main forecasts for this study.

Additional analysis on weekend travel characteristics has also been carried out to estimate likely upper and lower ranges of annual expansion factors, which have in turn been considered through the sensitivity tests.

Chapter 8 has details of the annualization sensitivity testing.

4.4 Ramp Up

The traffic model effectively forecasts a sudden increase in demand for the HOT lanes. However, in practice, this increase in traffic is not likely to happen immediately on opening, and may take some time to materialize fully. This is common to all toll roads (and transport facilities in general) and is known as ‘ramp-up’, and it tacitly takes into account both short and long term decisions on the part of drivers, which could include changing jobs, moving houses, etc as well as more straightforward re-routing or carpooling.

As the HOT lanes represent an improvement rather than an entirely new facility, and the majority of the traffic on the HOT lanes is likely to be locally diverted traffic, the ramp-up effect is likely to be smaller than for a new facility, though the introduction of a toll option to a previously free facility may lead to some initial resistance to pay.

30

I-95/I-395 HOT Lane


The basic ramp-up assumption employed in this study is similar to that observed when SR91 opened its HOT lane facilities. Therefore, in the opening year, the ramp-up factor is 60% of the forecast. In year two this rises to 85%, finally reaching 100% in year 3 (and onwards). The ramp-up factors have been applied separately to the northern and southern sections, to reflect differing opening years. For the northern section, ramp-up has been applied as follows:

• 2012 – 60% • 2013 – 85% • 2014 onwards – 100%

For the southern section it was assumed that only a proportion of traffic using the southern section would actually be new trips. The remainder of the trips on the southern section would therefore be existing trips, which had been generated following the opening of the northern section. This means that the ramp-up factors for the southern section affect a smaller proportion of the revenue. An analysis was undertaken of traffic using the southern section, which determined that around 34% of traffic in the southern section consisted of ‘new’ trips. Ramp-up factors should only be applied to this proportion, the remainder being ramped at 100%. Hence, southern section ramp-up has been applied as follows:

• 2015 – 86% • 2016 – 95% • 2017 onwards – 100%

Both lower (conservative) and higher (aggressive) ramp-up factors have also been examined as part of the sensitivity testing process. Chapter 8 has details of ramp-up sensitivity testing.

4.5 Treatment of growth beyond the model years

The traffic model produces forecasts of traffic volumes for the years 2015 and 2030. These ‘model years’ have been used as the basis for developing a revenue stream from 2012 to 2071. Between the model years (2015-2030), an interpolation function has been used to identify revenue values for these years.

Beyond 2030 a growth stream has been assumed for HOT lane traffic. This has made use of the growth rates implied by the differences between 2015 and 2030 model years’ revenue, which has in turn been calculated from all the assumptions put into the traffic models, and extrapolated variations of these into the future.

Interpolation between the 2015 and 2030 model years implies a decrementing growth rate per annum. Generally the rate decreases by around 0.1% per annum over the period between the model years. An on-going annual rate reduction of 0.1% is therefore assumed to continue beyond 2030, so that (for example) if the growth rate is 2.6% from 2029 to 2030, it will be 2.5% from 2030 to 2031, 2.4% from 2031 to 2032 and so on. This is not assumed to

31

I-95/I-395 HOT Lane


continue indefinitely, but to stop reducing at a residual level of 1% per annum. The point at which 1% per annum residual growth is reached varies according to the relationship between the 2015 and 2030 model results, but is typically around 2046.

However, it is not appropriate to simply scale the annual revenue by an assumed growth rate, as this does not immediately take into account that some sections of the HOT lane system may be reaching capacity. The dynamic nature of the HOT lane system is such that traffic flow will be controlled, in order not to exceed capacity and maintain appropriate operating conditions for users. Hence, in growing HOT lane revenues beyond 2030, a capacity capping mechanism has been introduced. This mechanism considers the capacity and volume on each section of the HOT lanes (between intersections) and checks that there is capacity for traffic volumes to rise by the annual rate each year. Where it cannot, because capacity is reached, the revenue from that section is capped going into the future.

4.6 Inflation

The base year traffic model is calibrated to 2005 traffic volumes using 2005 $ figures for value of time and vehicle operating costs. Toll and revenue figures are in 2008 $. The annual rate of inflation is assumed to be consistent with the Consumer Price Index (CPI) increasing by approximately 2.5% per annum. Note though that inflationary changes to monetary values are not directly reflected, and the forecasts are inelastic to CPI, as all toll and revenue values are all quoted in 2008 $.

4.7 Discount Rate

‘Discounting’ is used to compare monetary values that occur in different time periods. It is a separate concept from inflation, and is based on the principle that people (generally) prefer to receive goods and services now rather than later – also known as ‘time preference’. A discount rate is therefore used to convert revenues to ‘present values’, so that they can be compared, and a present value of the whole revenue stream calculated by summing the year-on-year discounted figures.

Public sector long-term project discount rates vary, but are typically 4-5%8. Higher rates are typically used in the private sector to account for greater returns and risk. A discount rate of 9% has been assumed in the calculation of present value revenues.

8 For instance the White House Office of Management & Budget, Circular A-94, “Guidelines and Discount Rates for Benefit-Cost Analysis of Federal Programs” suggests a rate of 4.9% for 30-year projects, though this was nearer to 8% in the early 1990s. The UK Treasury “Green Book” requires 3.5% (was 6% until 2003). The Government of Canada Regulation suggests 8% for regulatory interventions and a social preference rate of 3%.

32

I-95/I-395 HOT Lane

Base Year Model

5 Base Year Model – Preparation, Calibration and Validation

This section describes the preparation, calibration and validation of the 2005 Base Year Model. Our overall approach consisted of refining the existing NCRTPB model for the purpose of producing traffic and revenue forecasts for the I-95/I-395 HOT lanes. The model was validated to 2005 conditions using the following overall methodology:

• Refinements to the zoning system and network, particularly towards the southern end of I-95/I-395;

• Review traffic parameters such as Volume Delay Functions (VDF); • Review auto and truck cost assumptions (see Chapter 4); • Prepare 2005 base year matrices; and • Validate to 2005 conditions using traffic counts and journey time data. Given the

high level of transit usage along the corridor, relevant buses were pre-loaded onto the highway network in order to accurately reflect existing HOV usage.

Three models were prepared in order to represent the following time periods:

• Morning Peak (AM): 6:00-9:00am; • Afternoon Peak (PM): 4:00-7:00pm; and • Off peak: 1:00-3:00pm.

5.1 Data Sources

Traffic Counts

A comprehensive set of historical count data, as well as 2005 counts, classification and HOV class survey data has been assembled for the validation process. Counts from VDOT’s AADT reports were analyzed for the timeframe 2001 to 2006. Additional counts collected by Vollmer in June and July 2006 were used to supplement the VDOT counts. These counts were taken along the entire I-95/I-395 corridor at various locations, including all ramps.

Journey Time Survey

Journey time surveys were organized by Vollmer in June 2006 and the results are described in Chapter 2. This information was used to validate the travel times produced by the base year models.

33

I-95/I-395 HOT Lane

Base Year Model

5.2 Zoning System and Network refinement

The NCRTPB regional model consists of 2,191 traffic zones in the modeled region. This zoning system was refined in the southern area of the I-95/I-395 corridor in order to provide more realistic loading of trips onto the network (from the centroid connectors), in turn resulting in improved link volume estimates. A total of 76 zones were added in the counties of Stafford, Fredericksburg and Spotsylvania. Figure 5.1 illustrates the new zoning system in the southern area.

Figure 5.1 - Traffic Zones in the Southern Area

5.3 Volume Delay Functions (VDF)

As part of the calibration/validation exercise, the volume delay functions (VDFs) were re-examined. The VDFs used in the NCRTPB model take the form of a conical volume delay function which has a very steep slope for freeways. They also include a queuing function component for ramps and freeways, in a range from 0 minutes at volume/capacity (V/C) ratios of 0.8 or less, to 14 minutes for V/Cs of 1.4 or higher.

An initial review of the NCRTPB model’s journey time validation during peak hours revealed that the model significantly overestimated journey time on the I-95/I-395 corridor, which has possibly led to a decrease in modeled average vehicle miles traveled (VMT) between 2000

34

I-95/I-395 HOT Lane

Base Year Model

and 20309. Given this finding, the VDFs were subsequently replaced with traditional BPR (Bureau of Public Roads) speed-flow functions and adjusted to match the observed journey time data. An alpha value of 0.4 and beta value of 7 were determined to give the best representation of travel times for freeways. Similarly, values of 0.4 and 4 were used for alpha and beta respectively on at-graded roads. The beta value is higher for freeways as speeds tend to be close to free flow conditions for light and moderate traffic volumes, before dropping sharply as demand approaches capacity. The adjusted VDFs proved satisfactory during the calibration/validation exercise.

The equation of the BPR function is shown below. Figure 5.2 compares the original VDF with the updated VDF curves for freeways.

β

α

+

CapacityVolume

eedFreeFlowSpLength 1**60

Figure 5.2 - Adjusted Volume Delay Function (Freeways)

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Volume/Capacity Ratio

Trav

el T

ime

(min

)

5.4 Base Year Matrix Preparation

The NCRTPB model consists of four trip purposes including Home-Based Work (HBW), Home-Based Shopping (HBS), Home-Based Other (HBO) and Non-Home Based (NHB). It

9 Version 2.1 of the NCRTPB model showed average trip lengths increasing slightly between 2005 and 2030. As part of the latest NCRTPB model calibration exercise (version 2.2), volume delay functions were revised, resulting in average trip lengths declining by 6% between 2000 and 2030.

35

I-95/I-395 HOT Lane

Base Year Model

also has other market sectors such as trucks. To undertake base year model calibration, the following 2005 base year matrices were extracted from the NCRTPB model for the AM, PM and Off Peak periods:

• SOV – SOVs, commercial vehicles • HOV2 – HOV with 2 occupancy vehicles • HOV3+ – HOV with 3 or more occupancy vehicles • Trucks – Medium/heavy trucks • Airpax – Airport passenger vehicles

The extracted NCRTPB 2005 base year matrices were subsequently validated against available origin-destination data which included the 2000 census data and OD information on sluggers, formal HOV3+ and violator/hybrid counts.

2000 Origin-Destination Census Data

In the absence of a corridor origin-destination survey or current household travel survey data, the 2000 Census Transportation Planning Package data (CTPP) was used to verify the 2005 base year Home-Base Work (HBW) trip matrix from the NCRTPB model.

Figure 5.3 presents the traffic distribution comparison of auto driver HBW trip originating from Spotsylvania/ Fredericksburg/Stafford between the 2000 CTPP and the 2000 NCRTPB model. The comparison shows that the NCRTPB 2000 HWB trip matrix has significantly lower trips between Stafford/Spotsylvania/Fredericksburg and counties in the northern part of the I-95/I-395 corridor. As such, adjustments to the 2005 NCRTPB HBW trip matrix were made based on the CTPP data to increase trips between the southern and northern counties.

Figure 5.3 Location Codes

DC CR – District of Columbia, core DC NC – District of Columbia, non-core MTG – Montgomery County PG – Prince George County ARLCR – Arlington County, core ARNCR – Arlington County, non-core ALX – Alexandria FFX – Fairfax County LDN – Loudoun County PW – Prince William County FRD – Frederick County

CAR – Carroll County HOW – Howard County AAR – Anne Arundel County CAL – Calvert County STM –St.Mary’s County CHS – Charles County FAU – Fauquier County STA – Stafford County CL/JF – Clarke County, VA/Jefferson County, WVa SP/FB – Fredericksburg/Spotsylvania County KGEO – King George County

36

I-95/I-395 HOT Lane

Base Year Model

Figure 5.3 - 2000 CTPP Auto Driver vs. 2000 NCRTPB HBW Auto Driver (Traffic Originated from Spotsylvania/ Fredericksburg/ Stafford)

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

DC

CR

DC

NC

MTG PG

ARLC

R

ARN

CR

ALX

FFX

LDN

PW FRD

CAR

HO

W

AAR

CAL

STM

CH

S

FAU

STA

CL/

JF

SP/F

B

KGEO

Traf

fic D

istr

ibut

ion

from

SP/

FB/S

TA (%

) 2000 CTPP NCRTPB

Model Periods

The Halcrow base year models (AM, PM, Off Peak) were calibrated to 2005 observed morning peak (6am to 9am) counts, afternoon peak (4pm to 7pm) counts and Off peak counts (1pm to 3pm). The 2005 base year model validation results are shown in Appendix A of the report. Note that the total number of PM peak period trips is considerably higher than the AM peak period. The observed time of day profiles shown in Figures 2.6 to 2.8 are selected segments along the entire corridor. Those hourly profiles were used to expand the modeled off-peak volume by segments along the I-95/I-395 to represent the remaining 18 hours.

Analysis of Slugger, formal HOV3+ and Violator/Hybrid Counts

In order to accurately model the traffic volume and travel time for the I-95/I-395 corridor, it is important to determine counts for sluggers, formal HOV3+ carpooling and violators/hybrids on the HOV lanes. VDOT conducted an HOV classification survey in the fall of 2005 and 2006. This data was used to estimate the total number of HOV3+ on three locations on the I-95/I-395 corridor located near the Capital Beltway, namely:

• Glebe Road Station (inside the Capital Beltway); • Newington Station (outside Capital Beltway); and • Occoquan Station (outside Capital Beltway).

37

I-95/I-395 HOT Lane

Base Year Model

However, it should be noted that the HOV survey did not provide information on sluggers specifically; in that it only identified numbers of HOV3+ vehicles, hybrids and violators. Table 5.1 summarizes the HOV lanes traffic composition on Glebe Road Station (I-395 inside the Beltway) and Newington (I-95 outside the Beltway) for 2005 and 2006.

Table 5.1 - HOV Lanes Traffic Composition, Northbound between 06:00-09:00

Total Veh HOV3+ Veh Hybrids Violators Violators %

Glebe Road Station - Northbound from 6-9 AM2005 8,525 4,991 1,228 2,306 27%2006 8,856 6,029 1,182 1,645 19%

Newington south of flyover - Northbound from 6-9 AM2005 8,447 4,996 1,848 1,604 19%2006 8,231 5,051 1,665 1,515 18%

The survey indicates that a significant proportion of traffic using the HOV lanes does not actually comprise HOV3+, but that hybrids and violators make up a significant proportion of traffic. At Newington, approximately 40% of vehicles were violators and hybrids in both 2005 and 2006 AM peaks. Near Glebe Road Station, the 2005 proportion was also around 40%, though had dropped to just over 30% in 2006. A more recent survey (fall 2007) at a different location (nearer the Beltway) identified the total number SOV and HOV2 vehicles at 30%, which implies a further fall as this figure also includes hybrid vehicles with one or two occupants.

Based on the HOV class survey data, the approximate split of total HOV3+ and violators/hybrids on the HOV lanes was obtained. The formal HOV3+ and the sluggers were then separately determined using 2006 slugger count data10. Figure 5.4 shows the estimated profile for sluggers and formal HOV3+ carpooling traversing the I-95 corridor in the AM peak period. A similar profile was also obtained for the PM peak period as illustrated in Figure 5.5. Note that in the PM peak period, a slight reduction in sluggers was assumed following a review of the 2006 slugger count data.

The 2006 data provides a matrix of slugger movements in persons. This matrix was converted to vehicles by assuming that two sluggers were being picked up by one vehicle on the slug lines. The slugger vehicle matrix was then imported into the model and assigned to obtain the slug flow along the I-95/I-395 HOV lane. The proportion of sluggers was subsequently calculated for each segment on the corridor. The average proportion was 40% of HOV vehicles in the AM peak. A similar procedure was adopted during the PM peak, which also showed approximately an average of 40% sluggers. Given that the sluggers basically

10 VHB/Vanasse Hangen Brustlin, “Dynamic Ridesharing (Slugging) Data: I-95 Corridor“, June 2006

38

I-95/I-395 HOT Lane

Base Year Model

only operate during morning and evening peaks, we have assumed that sluggers represent 40% of daily HOV3+ in 2005 and 2006. The base year model was calibrated to account for these additional vehicles.

Figure 5.4 - AM Peak Period Sluggers and Formal HOV3+ on HOV Lanes

AM Peak Period (6:00 - 9:00)

0

500

1,000

1,500

2,000

2,500

3,000

3,500

Joplin Rd Opitz Blvd Lorton Rd Franconia Rd Duke St Shirlington Rd

Traf

fic V

olum

e

Sluggers Formal HOV3+

Figure 5.5 - PM Peak Period Sluggers and Formal HOV3+ on HOV Lanes

PM Peak Period (16:00 - 19:00)

0

500

1,000

1,500

2,000

2,500

3,000

3,500

Joplin Rd Opitz Blvd Lorton Rd Franconia Rd Duke St Shirlington Rd

Traf

fic V

olum

e

Sluggers Formal HOV3+

39

I-95/I-395 HOT Lane

Base Year Model

5.5 Validation Results

The results of the model network assignment were compared to independent traffic counts and travel time surveys in order to assess the model’s ability to reflect these counts and assess (validate) performance.

Traffic Count Validation

The validation efforts focused on the I-95/I-395 corridor, US Route 1, the Capital Beltway (I-495), I-66 and SR236. These key roads include approximately 200 directional count stations. Appendix A provides a comparison of the AM, PM and Off Peak models relative to the observed count station data.

The GEH statistic11 is used as a measure of ‘goodness of fit’ between modeled values and observed counts, and is especially appropriate to reflect the level of error associated with the magnitude of the count being analyzed. For traffic and revenue studies, a GEH statistic of less than 10 on individual screenlines has been recommended by some agencies (e.g. TransFund, New Zealand). The GEH statistic is defined by:

( )( )21

212

VV0.5VVGEH+×

−= where V1 and V2 are the estimated and actual flow values.

The RSQ12 of the model fit to observed counts for the AM, PM and Off Peak hours are shown in Figures 5.6 for the I-95/I-395 corridor. US-1 results are summarized in Figures 5.7 for the AM and PM peak hours (note that the Off peak counts for US-1 were not consistent, and as such they were not used during the validation exercise). Finally, the validation results for I-495, I-66 and SR 236 have been combined into Figures 5.8 for the AM, PM and Off peak hours. Overall it can be noted that the validation results are good, with an RSQ varying between:

• 0.93 and 0.98 on the I-95/I-395 corridor; • 0.91 and 0.94 on US 1; and • 0.95 and 0.98 on I-495, I-66 and SR 236.

11 The GEH statistic gets its name from Geoffrey E. Havers, who invented it in the 1970s. The GEH statistics is an empirical formula that is useful for a variety of traffic analyses, particularly in forecasting and modelling. It is used as a measure of ‘goodness of fit’ between modeled values and observed counts. The statistic is designed to reflect the level of error associated with the magnitude of the count being analyzed. For example, taking into account that a 10% error on a count of 100 cars is of less significance than 10% error on a count of 3,000 cars.

12 The RSQ represents the root mean square (also known as the quadratic mean or referred to as RMS), and is a measure of how well the model fits to observed data.

40

I-95/I-395 HOT Lane

Base Year Model

Travel Time Validation

The AM, PM and Off peak models travel time were compared with observed travel time survey routes on I-95/I-395 (both GP and HOT lanes) and US-1. Table 5.2 summarizes the travel time validation results.

Overall, there is a good fit between modeled and observed travel times, particularly on the I-95/I-395 corridor. Note that the midday model (i.e. off peak) shows a slight over-estimation of travel time on the GP lanes in the southbound direction. However, this is not of great concern because the off peak model attempts to validate combined traffic volumes on both GP and HOV lanes; this is due to a lack of detailed count information during the off peak period.

Table 5.2 - Travel Time Validation Results

Road Description Type Dir Obs Est % Diff Obs Est % Diff Obs Est % Diff

NB 75.7 71.5 -6% 47.6 49.0 3% 43.7 48.5 11%

SB 48.9 53.0 8% 85.7 79.2 -8% 48.7 55.8 15%

NB 29.8 29.6 -1% n/a n/a n/a n/a n/a n/a

SB n/a n/a n/a 28.0 30.7 10% 24.7

NB 99.1 89.6 -10% 85.8 75.0 -13%

SB 71.6 67.9 -5% 93.3 90.6 -3%Not surveyed

PM (4-7 PM) OP (1-3 PM)

see note (*)

US-1 Courthouse Rd / SR-208 to 23rd Street in Arlington County

All purposes

AM (6-9 AM)

I-95 / I-395

Courthouse Rd / SR-208 to George Washington Memorial Parkway GP

Southern Start / Terminus to George Washington Memorial Parkway HOV

Note: (*) Given the lack of detailed counts during the off peak period, the validation exercise focused on estimating combined traffic volumes on GP and HOV lanes. As such, journey time validation efforts on the HOV lane were not feasible.

Figure 5.6a - RSQ of Observed to Model Fit for I-95/I-395

I-95/I-395: Total Volume Comparison GP Lanes (AM Peak)

R2 = 0.98

5,000

10,000

15,000

20,000

25,000

30,000

35,000

5,000 10,000 15,000 20,000 25,000 30,000 35,000

Observed (ve/3h)

Mod

el (v

eh/3

h)

41

I-95/I-395 HOT Lane

Base Year Model

Figure 5.6b - RSQ of Observed to Model Fit for I-95/I-395

I-95/I-395: Total Volume Comparison GP Lanes (PM Peak)

R2 = 0.93

5,000

10,000

15,000

20,000

25,000

30,000

35,000

5,000 10,000 15,000 20,000 25,000 30,000 35,000Observed (ve/3h)

Mod

el (v

eh/3

h)

I-95/I-395: Total Volume Comparison HOV Lanes (AM Peak)

R2 = 0.96

2,000

4,000

6,000

8,000

10,000

2,000 4,000 6,000 8,000 10,000Observed (ve/3h)

Mod

el (v

eh/3

h)

42

I-95/I-395 HOT Lane

Base Year Model

Figure 5.6c - RSQ of Observed to Model Fit for I-95/I-395

I-95/I-395: Total Volume Comparison HOV Lanes (PM Peak)

R2 = 0.94

2,000

4,000

6,000

8,000

10,000

2,000 4,000 6,000 8,000 10,000Observed (ve/3h)

Mod

el (v

eh/3

h)

I-95/I-395: Total Volume Comparison GP & HOV Lanes (Off Peak)

R2 = 0.93

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000Observed (ve/3h)

Mod

el (v

eh/3

h)

43

I-95/I-395 HOT Lane

Base Year Model

Figure 5.7 - RSQ of Observed to Model Fit for US 1

US1: Total Volume Comparison (AM Peak)

R2 = 0.91

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000Ob served ( ve/ 3 h)

Mod

el (v

eh/3

h)

US1: Total Volume Comparison (PM Peak)

R2 = 0.94

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000Observed (ve/3h)

Mod

el (v

eh/3

h)

44

I-95/I-395 HOT Lane

Base Year Model

Figure 5.8a - RSQ of Observed to Model Fit for I-495, I-66 and SR 236

I-495/I-66/SR236: Total Volume Comparison (AM Peak)

R2 = 0.98

500

5,500

10,500

15,500

20,500

500 5,500 10,500 15,500 20,500Observed (ve/3h)

Mod

el (v

eh/3

h)

I-495/I-66/SR236: Total Volume Comparison (PM Peak)

R2 = 0.96

500

5,500

10,500

15,500

20,500

500 5,500 10,500 15,500 20,500Observed (ve/3h)

Mod

el (v

eh/3

h)

45

I-95/I-395 HOT Lane

Base Year Model

Figure 5.8b - RSQ of Observed to Model Fit for I-495, I-66 and SR 236

I-495/I-66/SR236: Total Volume Comparison (Off Peak)

R2 = 0.95

500

3,500

6,500

9,500

12,500

500 3,500 6,500 9,500 12,500Observed (ve/3h)

Mod

el (v

eh/3

h)

46

I-95/I-395 HOT Lane

Future Year Model

6 Future Year Model

Following the validation of the base year traffic models as described in Chapter 5, the next steps involved the development of future networks, matrices and the HOT Lane toll diversion model. This chapter sets out steps followed, as well as key information and techniques used.

6.1 Future Networks

Future year road and transit network assumptions were based on the future NCRTPB models in which the 2006 Financially Constrained Long-Range Plan (CLRP) and the 2007-2012 Transportation Improvement Program (TIP) were included for the northern section. Future networks in the southern corridor were compared against 2030 Constrained Long Range Plan. The list of key road network improvements as described in this plan is shown in Appendix B while Appendix C presents the 2006 CLRP.

The future networks have been coded to allow appropriate use of the proposed HOT lanes, in line with the project description provided earlier in the report. As such, this adds a third lane to the existing High Occupancy Vehicle (HOV) lanes on I-95/395 from South Eads Street (Exit 8C of I-395) near the Pentagon in Arlington County, to their existing southern terminus south of Route 234 (Dumfries Road at Exit 152 of I-95) near Dumfries in Prince William County – the northern section. Two new lanes reversible HOT lanes would be constructed to extend the system south from Dumfries to a new southern terminus at Massaponax – the southern section. Directional use of the HOT lanes is specified to be consistent with the modeled periods. The preliminary ingress and egress ramps assumed in the traffic model were based on information provided by VDOT as shown in Appendix D.

6.2 Future Land Use

The NCRTPB model covers approximately 50% of the Fredericksburg MPO region (FAMPO) and uses land use forecasts from the Cooperative Forecasting Program’s Round 7.1 for years 2000 through 203013. However, an issue with the NCRTPB land use forecasts is that the Round 7.1 Cooperative forecasts for the portion of the FAMPO region included in the NCRTPB model are not consistent with FAMPO’s approved land use forecasts.

Comparison of the two land use forecasts shows that the total employment for the portion of the FAMPO region included in the NCRTPB model is approximately 14% higher than FAMPO’s assumption. As such, Halcrow updated the NCRTPB land use forecasts with the approved FAMPO land use forecasts (both population and employment) provided by FAMPO.

13 National Capital Region Transportation Planning Board, “NCRTPB Travel Forecasting Model, Version 2.2, Specification, Validation, and User’s Guide”, March 1, 2008.

47

I-95/I-395 HOT Lane

Future Year Model

Additionally, adjustments were made to reflect the latest BRAC assumption by incorporating approximately 6,400 employments to the Mark Center.

Table 6.1 provides a summary of the 2005, 2015 and 2030 population and employment data used in the traffic model. Between 2005 and 2015, the total population and employment for the entire region is estimated to grow at annual rates of 1.4% and 1.7% respectively. Similarly, annual growth rates between 2015 and 2030 are approximately 1% for both population and employment.

Table 6.1 - Total Population and Employment (2005, 2015 and 2030) Total Population Total Employment

2005 2015 2030 2005 2015 20300 District of Columbia 577,830 633,130 714,060 745,300 814,030 881,4201 Montgomery Co. Md. 940,350 1,034,820 1,147,280 500,280 580,350 670,5102 Prince George's Co. Md. 850,540 928,905 990,590 347,600 392,535 517,8803 Arlington Co. Va. 199,260 226,540 242,490 194,880 228,650 258,4504 City of Alexandria Va. 135,850 149,390 171,090 105,740 119,370 147,9105 Fairfax Co. Va. 1,076,410 1,245,205 1,375,910 642,720 784,110 913,5106 Loudoun Co. Va. 247,340 355,520 468,540 130,310 204,095 290,7507 Prince William Co. Va. 399,870 509,065 613,680 137,880 173,940 217,7609 Frederick Co. Md. 220,880 265,565 339,700 122,160 150,345 167,26010 Howard Co. Md. 271,990 302,685 324,990 149,290 180,510 219,75011 Anne Arundel Co. Md. 513,700 543,750 571,680 271,700 315,720 374,58012 Charles Co. Md. 138,000 162,290 204,200 56,480 64,885 69,15014 Carroll Co. Md. 169,230 194,800 226,740 61,220 69,210 72,45015 Calvert Co. Md. 82,790 93,500 101,390 29,360 33,660 35,56016 St. Mary's Co. Md. 93,510 107,800 127,600 52,380 59,665 63,14017 King George Co. Va. 21,230 26,850 37,250 7,300 11,400 16,34018 City of Fredericksburg Va. 22,260 23,740 28,340 25,070 31,310 41,02019 Stafford Co. Va. 109,730 156,250 217,980 35,350 48,630 65,00020 Spotsylvania Co. Va. 89,140 121,130 165,040 27,990 38,670 51,90021 Fauquier Co. Va. 64,710 86,640 131,210 24,150 31,550 43,37022 Clarke Co. Va. 14,060 16,165 18,800 6,350 7,240 8,55023 Jefferson Co. W.Va. 46,810 57,060 76,420 18,530 23,585 30,670

6,285,490 7,240,800 8,294,980 3,692,040 4,363,460 5,156,930

JURISDICTION

Total

Population growth for counties located in the southern section of the I-95/I-395 corridor (Stafford, Fredericksburg, Spotsylvania and Prince George) are higher than the total regional growth at around 3% per annum between 2005 and 2015 and 2% per annum from 2015 to 2030. Similar growth rates are also observed for the employment forecast for counties in the southern section.

48

I-95/I-395 HOT Lane

Future Year Model

6.3 Future Year Matrices

Future year trip matrices (SOV, HOV2, HOV3+, Truck) for 2015 and 2030 were determined by running the NCRTPB models with the networks and land use assumptions described above. The resulting trip production and attraction by purpose in each horizon was Fratared14 using the 2005 adjusted matrices. This approach was used as the VDFs in the NCRTPB model had a notable impact on future trip distribution in the corridor. Additionally, this approach ensured that the adjusted commuting patterns were reflected in the future. Analysis of changes in future generalized cost in the Halcrow models suggests that the introduction of the HOT lanes would not significantly affect trip distribution.

6.4 Model Structure

The NCRTPB model is a conventional four-step model, which consists of trip generation, trip distribution, modal split and assignment. Future matrices are generated from the NCRTPB model and used to produce baseline trip matrices in each horizon year according to mode.

As noted earlier, when the 2000 CTPP was used to verify the 2005 Home-based work trip matrix from the NCRTPB model, this indicated that the matrix generated by the NCRTPB model has significantly lower number of trips between Stafford/Spotsylvania/Fredericksburg and counties in the northern part of the I-95/I-395 corridor. Matrices were therefore adjusted, based on the CTPP data, to increase trips between the southern and northern counties.

The toll diversion model is then applied to the future matrices and iterated until the toll and non-toll route shares reach convergence (approximately four iterations).

The model structure varies in detail according to the tests being carried out. Figure 6.1 shows the ‘full’ structure with the toll diversion model integrated with the NCRTPB model and considering the situation where the target minimum traffic speeds in the HOT lanes should be 55 mph. Hence, in this example, once convergence is reached within the toll diversion model, the next step is to undertake a speed check on the HOT lanes to ensure that the (in this case) 55 mph target minimum speed requirement is maintained. If some sections of the HOT lanes do not meet the speed requirement, toll rate levels are increased and the toll diversion model is re-applied. This process is repeated until the minimum speed requirement is achieved throughout.

14 The Fratar method applies growth factors to the traffic originating at, and/or destined to the zones represented in an OD table, which is then successively corrected until acceptably balanced.

49

I-95/I-395 HOT Lane

Future Year Model

Figure 6.1 - Model Structure

OD MatrixAdjustment

HOV2 SOV HOV3+ Truck

HOT Lanes/GP Lanes Logit Choice

Minimum Toll Rate on HOT Lanes at $0.10/mile

Total VehiclesHOT Lanes

HOV2Toll

HOV2Free

SOVToll

SOVFree HOV3+ Truck

Total VehiclesGP Lanes

Assignment

Convergence?

Speed Check onHOT Lanes

Speed > 55mphIncrease Toll Rate

HOV2Toll

HOV2Free

SOVToll

SOVFree HOV3+ Truck

YES

NO

NO

YES

NCRTPB Model

50

I-95/I-395 HOT Lane

Future Year Model

6.5 Toll Diversion Model

Under the proposed HOT lane concept, the use of a HOV lane by SOV or HOV2 is relaxed in exchange for payment of a variable toll. Thus a typical peak SOV and HOV2 user will be faced with a route choice: either to use the existing free but congested highway facility with extended and variable travel time, or pay the HOT toll and benefit from a travel-time saving as well as a more consistent arrival time. This choice decision is normally modeled using a logit function to estimate the relative attractiveness of each route, and is analogous to similar transportation route/mode choice problems. The higher the travel-time difference between the GP lanes and the tolled HOT lane facility the more SOV and HOV2 users are willing to pay.

Logit-based diversion model

A logit-based diversion model is a conventional and common technique for estimating toll road traffic. An example of a typical diversion curve resulting from a logit model is shown in Figure 6.2. The parameters that define the logit based diversion curve can be derived from stated preference studies of potential users, revealed preferences of actual users of a similar facility, previous studies, published reports or inferred from the historical relationship between VOT and GDP/capita. These parameters are a series of coefficients for each of the key variables (such as time and cost), and can be simplified into coefficients relating to time and cost and a scaling parameter which defines the steepness of the curve and thus the sensitivity of the model.

Figure 6.2 - Typical Diversion Curve

EXAMPLE OF DIVERSION CURVE

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 20% 40% 60% 80% 100% 120% 140% 160% 180% 200%

toll charge as % of value of time saving

% s

hare

usi

ng to

ll ro

ad

51

I-95/I-395 HOT Lane

Future Year Model

Model Used

For this study, a logit model has been used to estimate HOT lane usage. Parameters have been derived from existing sources, through a benchmarking exercise using Halcrow’s database of VOT against GDP/capita data (or GSP/capita). The scaling parameter has been assumed from previous values derived by Halcrow, and tested in the model for sensitivity.

The logit model estimates the probability of one alternative being chosen over another given the utility of each option. In the case of HOT lanes, the probability of an SOV or HOV2 user choosing the tolled HOT lanes over the non-tolled GP lanes is given by:

( )

( )

+

=

GPHOT

HOT

UU

UPHOTexpexp

exp

Where: PHOT = probability of choosing the HOT lane

UHOT = Generalized CostHOT * scaling parameter (λ) UGP = Generalized CostGP * scaling parameter (λ)

and: Generalized CostHOT = IVTHOT + (VOC + Toll) * VOT coefficient

Generalized CostGP = IVTGP + VOC * VOT coefficient

The generalized costs include the in-vehicle travel (IVT) time, vehicle operating costs (VOC) and HOT lane toll costs. The VOT coefficient is used to convert the monetary values into generalized minutes.

Process

The application of the toll diversion models involves the following steps:

• Path building – toll and non-toll paths identified between origins and destinations to determine travel time, operating costs and toll costs (generalized costs)

• Diversion calculations – the diversion model is applied to determine the proportion of trips using the toll road for each origin and destination. These proportions are then applied to the horizon year matrices. Separate logit diversion models are applied to each of the SOV and HOV2 market segments, to determine their probability of using the HOT lanes.

• Assignment – a multi-class equilibrium assignment is used to assign the toll and non-toll matrices to the network. Note that only the toll users have the option of using the toll facility. This process includes optimization of speeds in the HOT lane to ensure the traffic in these lanes moves freely (at a speed of at least 55mph).

This process is repeated until the network travel times and costs converge.

52

I-95/I-395 HOT Lane

Future Year Model

Treatment of HOV3+ and Sluggers

Attempts were initially made during the course of the study to identify appropriate modeling techniques to assess changes in carpooling behavior that may result from the availability of HOT lanes. In particular, informal car pooling (known as ‘slugging’) takes place on the I-95/I-395 corridor to facilitate access to the existing HOV lanes, which are only available to HOV3+ users, particularly focused on major employment destinations such as the Pentagon.

Recent information on sluggers suggests that this market is growing faster than other markets15. Therefore, in order to reflect a slowdown in growth for this market, sluggers were assumed to have the same growth as the formal HOV3+, SOV and HOV2 markets moving into the future. This assumption is intended to reflect a slight shift from slugging to paying HOT lane users.

When considering the potential impacts of HOT lanes on the slugging market, it is clear that the HOT lanes represent an opportunity for people who currently pick up sluggers to choose whether to continue to pick up sluggers. As such, a percentage of this market may choose to pay to use the HOT lanes instead of traveling free as a slugger vehicle. However, in order to model this explicitly, detailed information about the behavior of drivers who pick up slugs is required. Such information was not available to allow the calibration of a logit model for this market segment.

As the detailed issues surrounding slugging are not well understood sensitivity tests have been included to vary the assumptions relating to ‘sluggers’ to determine the impact on traffic and revenues. In addition, slugging is also included in the risk analysis.

Motorway Bonus

People generally prefer driving on purpose built high-speed highways (motorways, freeways, etc) because there is less conflicting movement – i.e. traffic is all going in the same direction and there are no intersections requiring stopping and starting. Note that this is distinct from the issue that such highways can be congested and hence cause delay over free-flow journeys, in that they are usually still preferable to the alternatives for many drivers. Hence, a ‘motorway bonus’ has been used in the toll diversion model to reflect this. The bonus takes the form of a 20% distance-based benefit for relevant links. To further reflect that there will be a distinct, but similar, advantage for users of the HOT lanes, a further 10% benefit has been applied to HOT lane links.

15 Virginia Department of Transportation (VDOT), “Analysis of AM Peak Period Travel in Northern Virginia’s I-95 Corridor Outside the Beltway in the Fall of 2007”, October 27, 2008.

53

I-95/I-395 HOT Lane

T&R Forecasts

7 Traffic & Revenue Forecasts

This Chapter sets out the main traffic and revenue forecasts carried out using the models and techniques developed in the study (and described in Chapters 4, 5 and 6). There are three sections in this Chapter:

• Throughput tests – these tests are designed to identify the toll rates that would be required to meet a selection of target minimum speeds in the HOT lanes;

• Revenue optimization – this analysis was carried out to identify the maximum toll rates that can be charged on each segment of the HOT lanes; and

• Toll schedule analysis – a realistic toll regime was identified taking the results of the throughput tests and revenue optimization (Toll Schedule v1).

Detailed results of the throughput tests, the test using revenue optimal toll rates and the test using Toll Schedule v1 can be found in Appendix E.

7.1 ‘Throughput’ Tests

The first full test carried out examined the toll rates and revenue potential of maintaining speeds in the HOT lanes at or above 55 mph (using the full model structure shown in Figure 6.1). Initially, toll rates were varied for 26 road segments individually, the 26 segments being derived from proposed interchange locations as shown in Figure 7.1. The speed on each segment was examined during the modeling process, and the toll rate increased on any individual segment with an average speed less than 55mph. This results in separate toll rates for each of the 26 segments. We also examined the toll rates and revenue potential for the target minimum speeds of 45 mph and 65 mph.

However, it would be impractical to optimize toll rates over each of 26 segments, not least because this is unlikely to be how toll rates are set in practice. Hence, a similar ‘throughput’ analysis was conducted for six tolling segments (A-F) shown in Figure 7.2. The locations of these segments have been identified in consultation with VDOT to ensure compatibility with FTU’s plans for the corridor. In essence, the overall modeling methodology remains the same for these tests, but tolls are set in blocks, where the rate is the same across each of the segments in their entirety. Hence, if part of the segment fails the target minimum speed tests, the tolls across the whole segment are increased accordingly.

Figures 7.3 and 7.4 show the resulting toll rates for each of the ‘6’ segments in 2015 and 2030 respectively, for all time periods. Table 7.1 provides a summary of the revenue for each of the target minimum speeds in 2015 and 2030, for the ‘26’ and ‘6’ segment approaches.

54

I-95/I-395 HOT Lane

T&R Forecasts

Table 7.1 - Annual Revenue for Target Minimum Speed Thresholds

Annual Rev ($M)

Toll Range (per mile)

Annual Rev ($M)


26 77.1 174.06 83.1 178.9

26 92.1 191.66 91.8 172.7

26 102.7 179.26 90.9 130.0

65 mph

$0.12-$1.46 $0.14-$1.73

$0.14-$2.16

$0.29-$3.90$0.12-$2.92

$0.12-$1.70

2015 (2008$) 2030 (2008$)

45 mph

55 mph

Speed Minimum Toll segment

Modeled annual revenue ranges from $77.1 (45 mph with 26 segments) to $102.7M (65 mph also with 26 segments) in 2015. In 2030, the range is $130.0M (65 mph with 6 segments) to $191.6M (55 mph with 26 segments). Per mile toll rates vary significantly, between $0.12 (the minimum assumed) and $2.92 in 2015 (depending on location and target minimum speed), and between $0.14 (minimum) and $3.90 in 2030.

With a target minimum speed of 45 mph, revenues are higher with 6-segments than with 26-segments. This indicates that toll rates are below revenue optimal in several places when the corridor is divided into smaller segments. Aggregating the effects across longer segments introduces higher tolls than would otherwise be the case, resulting in a higher revenue total.

For the 55 mph throughput tests, there is little difference in revenues in 2015 whether the tolls are levied over 6 or 26 segments. In 2030, revenue is notably lower with 6-segments. This indicates that the aggregated toll rates for 6-segments are likely to be beyond revenue optimal compared to individual segment rates (the reverse of the situation for 45 mph tests).

When the target minimum speed is 65 mph, the resulting toll rates are higher across the whole corridor, whether 6 or 26 segments are assumed. In particular, this is noticeable in the northern segments, in order to meet the target minimum speed in the most congested areas. In 2015, 26 tolling segments and a target minimum speed of 65 mph generates the highest revenue of all the throughput tests (albeit it is worth noting that the traffic levels are the lowest). However, by 2030 the need to escalate toll rates to maintain the 65 mph speed target means that rates are beyond revenue optimal on several segments, thus resulting in lower overall revenues than the 55 mph scenario (with either 6 or 26 toll segments).

Detailed results of the throughput tests can be found in Appendix E, including an initial assessment of transactions in the HOT lanes for the 55 mph throughput test.

55

I-95/I-395 HOT Lane

T&R Forecasts

Figure 7.1 - Initial traffic segments

56

I-95/I-395 HOT Lane

T&R Forecasts

Figure 7.2 – Proposed Tolling Segments

A

B

C

D

E

F

57

I-95/I-395 HOT Lane

T&R Forecasts

Figure 7.3a - 2015 AM Peak Toll rates derived from throughput tests

2015 Toll Rates - AM Peak (Throughput tests)

- $0.20

$0.40$0.60$0.80

$1.00$1.20$1.40

$1.60$1.80$2.00

$2.20$2.40

$2.60$2.80$3.00

$3.20$3.40$3.60

$3.80$4.00

A B C D E F

Toll Segment (not to scale)

Toll

Rat

e (2

008

$/m

ile)

45mph 55mph 65mph

Figure 7.3b - 2015 PM Peak Toll rates derived from throughput tests

2015 Toll Rates - PM Peak (Throughput tests)

- $0.20

$0.40$0.60

$0.80$1.00

$1.20$1.40

$1.60$1.80

$2.00$2.20

$2.40$2.60

$2.80$3.00

$3.20$3.40

$3.60$3.80

$4.00

A B C D E F


Toll

Rate

(200

8 $/

mile

)

45mph 55mph 65mph

58

I-95/I-395 HOT Lane

T&R Forecasts

Figure 7.3c - 2015 Off Peak Toll rates derived from throughput tests

2015 Toll Rates - Off Peak (Throughput tests)

- $0.20

$0.40$0.60

$0.80$1.00$1.20

$1.40$1.60

$1.80$2.00$2.20

$2.40$2.60

$2.80$3.00$3.20

$3.40$3.60

$3.80$4.00

A B C D E F


Toll

Rate

(200

8 $/

mile

)45mph 55mph 65mph

Figure 7.4a - 2030 AM Peak Toll rates derived from throughput tests

2030 Toll Rates - AM Peak (Throughput tests)

- $0.20

$0.40$0.60

$0.80$1.00$1.20

$1.40$1.60

$1.80$2.00$2.20

$2.40$2.60

$2.80$3.00$3.20

$3.40$3.60

$3.80$4.00

A B C D E F


Toll

Rate

(200

8 $/

mile

)

45mph 55mph 65mph

59

I-95/I-395 HOT Lane

T&R Forecasts

Figure 7.4b - 2030 PM Peak Toll rates derived from throughput tests

2030 Toll Rates - PM Peak (Throughput tests)

- $0.20

$0.40$0.60

$0.80$1.00

$1.20$1.40

$1.60$1.80

$2.00$2.20

$2.40$2.60

$2.80$3.00

$3.20$3.40

$3.60$3.80

$4.00

A B C D E F


Toll

Rate

(200

8 $/

mile

)45mph 55mph 65mph

Figure 7.4c - 2030 Off Peak Toll rates derived from throughput tests

2030 Toll Rates - Off Peak (Throughput tests)

- $0.20

$0.40$0.60

$0.80$1.00$1.20

$1.40$1.60

$1.80$2.00$2.20

$2.40$2.60

$2.80$3.00$3.20

$3.40$3.60

$3.80$4.00

A B C D E F


Toll

Rate

(200

8 $/

mile

)

45mph 55mph 65mph

60

I-95/I-395 HOT Lane

T&R Forecasts

7.2 Revenue Optimization

In the ‘throughput’ tests, we have assumed that no section of the roadway is allowed to fall below a target minimum speed threshold. The higher the target speed, the greater the likelihood that this results in sub-optimal toll rates being required. This is especially the case when longer, more aggregate, segments are used for tolling. With more relaxed speed criteria, the impact of segmentation is a less significant issue.

The purpose of revenue optimization therefore is to identify the toll levels that generate the most revenue in the HOT lanes. In general, revenue rises as tolls increase from a low starting value, but the elasticity of toll users’ willingness to pay usually results in revenues leveling-off and ultimately decreasing after reaching a peak toll – the so-called ‘revenue optimal’ rate. In this analysis, the speed of the traffic in the HOT lanes is not controlled by the modeling process, in order to allow the maximum revenue (and hence toll rates) to be identified.

For the I-95/395 HOT lanes this is complicated by the requirement for different toll rates at different locations on the route. Revenue optimal toll rates differ accordingly as well. As such, the methodology employed applies a series of toll levels across the corridor in its entirety. Segment revenue totals were then considered separately and compared with the respective toll rates for the segment, in order to identify optimal toll levels for each segment.

A further complication in assessing the optimum revenue generated by the HOT lanes will be the ultimate requirement to meet (or exceed) SAFETEA-LU standards for HOV (and HOT) facilities16. However, the definition of the standards relates to on-going time-series analysis of the speed profile, requiring exceedance of the 10th percentile situation for compliance with the minimum speed. The traffic modeling process is essentially calibrated to average ‘neutral’ day information. It can be argued that this approximates to the 50th percentile speed in a time series, which is in turn likely to be higher than the 10th percentile required by SAFETEA-LU, but insufficient evidence has been noted in data available to date to support this. It is also not clear exactly how the speed criteria will be monitored and used by the HOT lane operator on a day-to-day basis or to report compliance with SAFETEA-LU standards (over the lengths of the HOT lanes or over time). A future refinement of the modeling process would therefore identify the relationship between model speeds, the speeds that are going to be monitored and the methodology, and to reflect those linkages in the model’s relationship between speed and toll rates. This will result in a series of toll rates that can be compared to the revenue optimal rates identified in this study to isolate semi-optimal rates related to speed monitoring practice and the SAFETEA-LU standards.

16 SAFETEA-LU Standard for Determining Degraded HOV facility states “the operation of a HOV facility shall be considered to be degraded if vehicles operating on the facility are failing to maintain a minimum average operating speed 90 percent of the time over a consecutive 180-day period during the morning or evening weekday peak hour periods (or both). A minimum average operating speed is 45 mph for HOV facilities with a speed limit of 50 mph or greater”

61

I-95/I-395 HOT Lane

T&R Forecasts

Revenue optimization has been undertaken for the ‘6’ tolling segments, in both the 2015 and 2030 model years, for all three modeled time periods (AM, PM and Off Peak), in total requiring over 100 model runs.

Methodology

The methodology applied to identify revenue optimum toll rates consists of a series of model runs where the toll rates change each time. For these tests, the ‘full’ modeling process was not employed, and the toll levels are fixed, with no variation in relation to speeds. The starting point was to apply the toll rates from the 55 mph throughput test, as this provides an indication of the sort of toll levels that may be required to maintain service levels. These toll rates were systematically increased and decreased, running the models each time.

The outputs from all the model runs were analyzed, and the amount of revenue generated by each segment identified for every toll rate used. The relationship between toll rates and revenues was then plotted on revenue/rate curves. The revenue optimal toll rate for the segment is the toll value point on the curve at which the revenue for the segment peaks.

Figures 7.5 and 7.6 show the resulting revenue curves for 2030 AM and PM by segment. Table 7.2 provides a summary of the resulting revenue optimal toll rates.

Table 7.2 - Revenue optimal toll rates (2008 $ per/mile)

AM PM OP AM PM OP AM PM OP

Eads-Seminary A $1.28 $0.76 $0.49 $1.62 $1.19 $0.58 1.6% 3.0% 1.1%

Seminary-FrSp Pwy B $1.10 $0.85 $0.49 $1.19 $1.30 $0.58 0.5% 2.9% 1.1%

FsSp Pwy-Gord C $0.43 $0.55 $0.30 $0.65 $0.87 $0.43 2.8% 3.1% 2.4%

Gord-Garris D $0.30 $0.55 $0.24 $0.65 $1.01 $0.43 5.3% 4.1% 4.0%

Garris-MtView E $0.18 $0.18 $0.18 $0.22 $0.22 $0.22 1.3% 1.3% 1.3%

MtView-Massap F $0.49 $0.49 $0.24 $0.58 $0.58 $0.29 1.1% 1.1% 1.3%

2015 2030Segment

CAGR (2015-2030)

In general, in 2015 the revenue optimal toll rates are higher than (or close to) the rates from the 45 mph and 55 mph throughput tests, excluding the most northerly segment; 65 mph rates are typically above revenue optimal. In 2030, 55 mph and 65 mph throughput rates are generally above revenue optimal; 45 mph rates are close to or below. Figures 7.7 and 7.8 illustrate revenue optimal toll rates alongside the throughput rates. Annual revenue estimates for the revenue optimal test are estimated at $105.7M in 2015, increasing to $210.5M in 2030. Further discussion of the revenue optimal toll rates follows in the next section, in defining a workable toll schedule.

Detailed results of the test using revenue optimal toll rates can be found in Appendix E, including an initial assessment of transactions in the HOT lanes for the revenue optimal test.

62

I-95/I-395 HOT Lane

T&R Forecasts

Figure 7.5 - 2030 AM Peak Revenue Optimization Curve

HOT Lanes - AM Peak - Northbound only - 2030

-

$20,000

$40,000

$60,000

$80,000

$100,000

$120,000

- $0.50 $1.00 $1.50 $2.00 $2.50 $3.00

Toll per mile (2008 $)

Rev

enue

(3 h

r)

Eads-Sem Sem-FrSp Pwy FrSp Pwy-Gord Gord-Garris Garris-MtView MtView-Massap

Figure 7.6 - 2030 PM Peak Revenue Optimization Curve

HOT Lanes - PM peak - Southbound only - 2030

-

$20,000

$40,000

$60,000

$80,000

$100,000

$120,000

- $0.50 $1.00 $1.50 $2.00 $2.50 $3.00

Toll per mile (2008 $)

Rev

enue

(3 h

r)

Eads-Sem Sem-FrSp Pwy FrSp Pwy-Gord Gord-Garris Garris-MtView MtView-Massap

63

I-95/I-395 HOT Lane

T&R Forecasts

Figure 7.7a - 2015 AM Peak Revenue Optimal Toll Rates

2015 Toll Rates - AM Peak

- $0.20

$0.40$0.60$0.80

$1.00$1.20$1.40

$1.60$1.80$2.00

$2.20$2.40

$2.60$2.80$3.00

$3.20$3.40$3.60

$3.80$4.00

A B C D E F


Toll

Rat

e (2

008

$/m

ile)

45mph 55mph 65mph Revenue Optimal toll rates

Figure 7.7b - 2015 PM Peak Revenue Optimal Toll Rates

2015 Toll Rates - PM Peak

- $0.20

$0.40$0.60$0.80

$1.00$1.20$1.40

$1.60$1.80$2.00

$2.20$2.40

$2.60$2.80$3.00

$3.20$3.40$3.60

$3.80$4.00

A B C D E F


Toll

Rat

e (2

008

$/m

ile)


64

I-95/I-395 HOT Lane

T&R Forecasts

Figure 7.7c - 2015 Off Peak Revenue Optimal Toll Rates

2015 Toll Rates - Off Peak

- $0.20

$0.40$0.60$0.80

$1.00$1.20$1.40

$1.60$1.80$2.00

$2.20$2.40

$2.60$2.80$3.00

$3.20$3.40$3.60

$3.80$4.00

A B C D E F


Toll

Rat

e (2

008

$/m

ile)


Figure 7.8a – 2030 AM Peak Revenue Optimal Toll Rates

2030 Toll Rates - AM Peak

- $0.20

$0.40$0.60$0.80

$1.00$1.20$1.40

$1.60$1.80$2.00

$2.20$2.40

$2.60$2.80$3.00

$3.20$3.40$3.60

$3.80$4.00

A B C D E F


Toll

Rat

e (2

008

$/m

ile)


65

I-95/I-395 HOT Lane

T&R Forecasts

Figure 7.8b – 2030 PM Peak Revenue Optimal Toll Rates

2030 Toll Rates - PM Peak

- $0.20

$0.40$0.60$0.80

$1.00$1.20$1.40

$1.60$1.80$2.00

$2.20$2.40

$2.60$2.80$3.00

$3.20$3.40$3.60

$3.80$4.00

A B C D E F


Toll

Rat

e (2

008

$/m

ile)


Figure 7.8c – 2030 Off Peak Revenue Optimal Toll Rates

2030 Toll Rates - Off Peak

- $0.20

$0.40$0.60$0.80

$1.00$1.20$1.40

$1.60$1.80$2.00

$2.20$2.40

$2.60$2.80$3.00

$3.20$3.40$3.60

$3.80$4.00

A B C D E F


Toll

Rat

e (2

008

$/m

ile)


66

I-95/I-395 HOT Lane

T&R Forecasts

7.3 Toll Schedule Analysis

Following on from the throughput tests and identification of a ‘revenue optimal’ toll schedule, the next step in the assessment provides background information to assist with the selection of a preliminary toll schedule that could actually be applied in practice. In the first instance, this schedule is required as a ‘realistic’ toll schedule to be used in sensitivity testing and risk analysis carried out as part of this study.

In selecting an appropriate toll schedule, some of the issues to be considered include:

• Maximizing revenues, while being close to target minimum speeds; • Public acceptability (such as good utilization of lanes, tolls that are similar to other

facilities, etc.); and • Reasonable toll escalation policy.

Maximizing Revenue

Table 7.3a-c show the toll rates derived by the throughput tests for the target minimum speeds of 45 mph and 55 mph, as well as the revenue optimal analysis. Tables 7.3a-b have been color coded to highlight where the toll rates have exceeded revenue optimal in order to meet the minimum speed criteria (cells that are red).

In 2015, a few segments have toll rates that exceed revenue optimal in order to meet the speed criteria at 55 mph. By 2030, the northern section requires toll rates beyond revenue optimal in order to meet the 55 mph criteria. It should be noted that for the target minimum speed scenarios we have assumed the speed cannot fall below the minimum speed between any interchange within the segment (i.e., the average speed over the segment will be higher).

Table 7.3a - Toll Regime used to maintain 45 mph target minimum speed Toll Regime (2008 $)

SegmentAM PM OP AM PM OP

Eads-Seminary A $1.46 $0.12 $0.12 $1.73 $1.30 $0.14Seminary-FrSp Pwy B $0.12 $0.85 $0.12 $1.01 $1.30 $0.14FsSp Pwy-Gord C $0.12 $0.24 $0.12 $0.72 $1.30 $0.14Gord-Garris D $0.12 $0.12 $0.12 $0.29 $1.30 $0.14Garris-MtView E $0.12 $0.12 $0.12 $0.14 $0.14 $0.14MtView-Massap F $0.12 $0.12 $0.12 $0.14 $0.14 $0.14Cost to travel entire length of HOT lane $11.59 $13.22 $6.90 $28.51 $52.08 $8.18

Revenue Throughput (45mph)2015 2030

67

I-95/I-395 HOT Lane

T&R Forecasts

Table 7.3b - Toll Regime used to maintain 55 mph target minimum speed Toll Regime (2008 $)




Table 7.3c - Revenue Optimal Toll Regime Toll Regime (2008 $)



Revenue Optimal2015 2030

Speeds

Tables 7.4a-c show the average speed over each segment for the 45 mph and 55 mph throughput tests and the revenue optimal tolls. Note that the average speeds are notably higher than the target minimum speed (45 mph or 55 mph), mostly as a result of the calculation methodology used. For the revenue optimal scenario, speeds are apparently above a 45 mph target minimum speed implied by SAFETEA-LU. As noted earlier in this chapter however, whether the speed calculations fit with the monitoring process and time-series nature of the SAFETEA-LU standard requires further consideration.

It is important to note that where toll rates change significantly between adjacent segments, this can influence the overall travel speeds. For instance, the 45mph throughput test results in a toll rate requirement in segment ‘A’ of $1.46 per mile, while in segments ‘B’ and ‘C’ it is only $0.12 per mile. This attracts more traffic to segments ‘B’ and ‘C’ than the revenue optimal scenario where the rates are similar in ‘A’ and ‘B’, resulting in slower segment speeds. On balance, these tables suggest that with some refinement of the toll schedule, the 45-55 mph speed minimum can be met in 2015, but that by 2030 there may be issues in the north. Please note that this information is high level and in order to accurately predict speeds the relationship between monitoring methodology and SAFETEA-LU standards requires further investigation (as discussed earlier). In addition, to fully validate estimated travel speeds, a full

68

I-95/I-395 HOT Lane

T&R Forecasts

Highway Capacity Manual (HCM) analysis would be required, making use of the geometric details of the HOT lane design.

Table 7.4a - Average segment speeds for 45 mph target minimum speed scenario Speed (mph)


Eads-Seminary A 45.8 52.8 63.4 49.9 55.2 62.1Seminary-FrSp Pwy B 54.5 55.4 63.8 58.0 55.6 62.3FsSp Pwy-Gord C 56.3 55.0 63.8 56.3 63.6 59.7Gord-Garris D 62.7 56.5 64.0 57.3 64.3 59.9Garris-MtView E 64.3 64.1 64.7 60.9 64.5 62.6MtView-Massap F 64.4 65.0 65.0 61.3 65.1 64.7


Table 7.4b - Average segment speeds for 55 mph target minimum speed scenario Speed (mph)




Table 7.4c - Average segment speeds for Revenue Optimal Toll Regime scenario Speed (mph)




Acceptability

From a public acceptability perspective, an important issue is the utilization. Tables 7.5 and 7.6 show average volume/capacity ratios by segment for the 45mph throughput and revenue optimal tests, as a measure of lane utilization. At 45 mph HOT lane utilization is reasonable throughout the day. With revenue optimal tolls, utilization is reasonable in the north during the peaks, but low in the south. Given that the revenue curves are relatively flat in the south, this would suggest the selection of a lower toll rate in the south.

69

I-95/I-395 HOT Lane

T&R Forecasts

Table 7.5 - Lane utilization for 45 mph target minimum speed toll schedule HOT Lane usage (Volume/Capacity)


Eads-Seminary A 96% 88% 44% 90% 83% 48%Seminary-FrSp Pwy B 88% 89% 44% 83% 89% 50%FsSp Pwy-Gord C 84% 86% 43% 85% 63% 52%Gord-Garris D 67% 84% 39% 80% 54% 50%Garris-MtView E 61% 66% 37% 80% 61% 50%MtView-Massap F 51% 34% 24% 69% 36% 32%

2015 2030Revenue Throughput (45mph)

Table 7.6 - Lane utilization for revenue optimal toll schedule HOT Lane usage (Volume/Capacity)


Eads-Seminary A 79% 76% 30% 90% 85% 34%Seminary-FrSp Pwy B 60% 77% 29% 79% 93% 34%FsSp Pwy-Gord C 57% 61% 30% 79% 80% 35%Gord-Garris D 44% 48% 27% 62% 68% 32%Garris-MtView E 39% 38% 27% 57% 60% 35%MtView-Massap F 32% 17% 16% 49% 32% 21%


Proposed Toll Schedule

Another important issue surrounds the general acceptability of the proposed toll schedule in comparison with other similar facilities either in planning or in operation.

For instance, SR 91 in California currently levies charges between $0.125 and $1.00 per mile, varying by time-of-day and day-of-the-week. Information from the ‘Capital Beltway HOT Lanes Investment Grade Traffic and Revenue Study’ (Feb 2007) identifies likely peak toll rates in 2015 of between $1.15 and $1.54 per mile, and off peak tolls of approximately $0.15 per mile. These rates are consistent with the peak revenue optimal toll rates identified for the I-95/I-395. Off Peak rates at existing facilities appear to be significantly lower than the revenue optimal rates.

A final issue is the how the toll rates will be escalated under the concession agreement and how this compares to other facilities. A reasonable toll escalation policy should keep pace with inflation and also incorporate an element to capture an increased willingness to pay. For example, the Indiana Toll Road has an annual escalation rate of the greater of CPI or Nominal GDP/capita (which is approximately 2% higher than CPI). The Capital Beltway report suggests an escalation policy of 4% per annum (in nominal terms).

For the I-95/I-395 T&R study, Halcrow has assumed that willingness to pay will increase at 0.75xreal GDP/capita, or approximately 1% above CPI.

70

I-95/I-395 HOT Lane

T&R Forecasts

Table 7.7 presents a possible toll schedule. Figure 7.9 shows the proposed toll schedule (Toll Schedule v1) alongside the revenue optimal toll rates. The proposed toll schedule is based on the following criteria:

• Toll rates slightly below revenue optimal during the peak periods; • Ability to use a uniform escalation rate over all segments without exceeding

revenue optimal in future (for instance, revenue optimal toll rates during the AM peak for segment ‘B’ are similar in 2015 and 2030, thus requiring to start with a lower rate in 2015); and

• If toll revenue curves are flat (particularly relevant in off peak periods), select a lower toll rate.

• Toll rates are presented in 2015 and 2030 under both a CPI+1% and CPI+2% escalation scenario.

This toll schedule has been used as the basis for the sensitivity testing and risk analysis detailed in Chapter 8. Appendix E contains detailed results of a test using the toll schedule in Table 7.7, assuming an escalation rate of CPI+2% from 2015 to 2030. A comparison of the revenues in 2015 and 2030 are presented in Table 7.8. Annual revenue estimates for Toll Schedule v1 are slightly lower than the optimal schedule in 2015 ($103.3M vs. $105.7M) and approximately 7% lower in 2030 ($195.8M vs. $210.5M).

Table 7.7 - Possible Toll Schedule (Toll Schedule v1) Toll Regime (2008 $)



SegmentAM PM OP

Eads-Seminary A $1.68 $1.01 $0.27Seminary-FrSp Pwy B $1.01 $1.01 $0.27FsSp Pwy-Gord C $0.54 $0.67 $0.27Gord-Garris D $0.40 $0.67 $0.27Garris-MtView E $0.27 $0.27 $0.27MtView-Massap F $0.27 $0.27 $0.27Cost to travel entire length of HOT lane $30.49 $34.06 $15.25

2015 2030 (1% esc.)

2030 (2% esc.)

Possible Toll Schedule

71

I-95/I-395 HOT Lane

T&R Forecasts

Table 7.8 – Annual Revenue for Revenue Optimal and Proposed Toll Schedule v1

Annual Rev ($M)


Annual Rev ($M)


Revenue Optimal 6 105.7 $0.18-$1.28 210.5 $0.22-$1.62

Toll Schedule v1 6 103.3 $0.20-$1.25 195.8 $0.27-$1.68

2015 (2008$) 2030 (2008$)Toll Rates Toll

segments

Figure 7.9a - 2015 & 2030 AM Peak Toll Rates (Toll Schedule v1 & revenue optimal)

2015 & 2030 Toll Rates - AM Peak

-

$0.20

$0.40

$0.60

$0.80

$1.00

$1.20

$1.40

$1.60

$1.80

$2.00

A B C D E F


Toll

Rat

e (2

008

$/m

ile)

Toll v1: 2015 Toll v1: 2030 Rev Opt: 2015 Rev Opt: 2030

72

I-95/I-395 HOT Lane

T&R Forecasts

Figure 7.9b - 2015 & 2030 PM Peak Toll Rates (Toll Schedule v1 & revenue optimal)

2015 & 2030 Toll Rates - PM Peak

-

$0.20

$0.40

$0.60

$0.80

$1.00

$1.20

$1.40

$1.60

$1.80

$2.00

A B C D E F


Toll

Rat

e (2

008

$/m

ile)


Figure 7.9c - 2015 & 2030 Off Peak Toll Rates (Toll Schedule v1 & revenue optimal)

2015 & 2030 Toll Rates - Off Peak

-

$0.20

$0.40

$0.60

$0.80

$1.00

$1.20

$1.40

$1.60

$1.80

$2.00

A B C D E F


Toll

Rat

e (2

008

$/m

ile)


73

I-95/I-395 HOT Lane

Sensitivity & Risk Analysis

8 Sensitivity Testing and Risk Analysis

8.1 Introduction

This chapter provides an overview of the sensitivity tests and output that have been carried out as part of I-95/I-395 project. The results of sensitivity testing provide an indication of the potential variability in forecasting HOT lane usage and revenue, and are therefore the primary inputs to the risk analysis process.

The remainder of this chapter discusses the sensitivity tests, including the rationale behind the values used and the results obtained, and goes on to describe the risk analysis carried out as well as its outcomes.

8.2 Sensitivity Tests

All sensitivity tests have used the potential toll schedule outlined in Chapter 7 as their HOT lane charging basis (Toll Schedule v1 in Table 7.7, assuming a CPI+2% toll escalation rate), with variations in the sensitivity test variables only. Six variables have been tested, resulting in 13 sets of test results:

• Background traffic growth; • Early years’ ramp-up; • Vehicle operating costs; • Value of time; • Sluggers, HOV3 and transit use; and • Annual expansion factors.

Overall Results of Sensitivity Tests

Table 8.1 summarizes the results of the sensitivity tests in terms of the impact on revenue in 2015 and 2030. The information provides an indication of the revenue sensitivity around the Base Case revenue forecast, it does not provide information on the likelihood of these events occurring. The formal risk analysis results provide a more thorough understanding of the traffic risks and the associated range as well as the likelihood of occurrence.

The sensitivity tests that have the greatest impact on toll revenues are background growth, vehicle operating costs and value of time. The changes to Slugs/HOV3+/Transit and the annual expansion factor have a less pronounced impact. Figures 8.1 and 8.2 give an overview of the relative impact of each of the sensitivity tests on the Base Case revenue in 2015 and 2030.

74

I-95/I-395 HOT Lane


While the sensitivity tests provide an indication of the revenue sensitivity around the base case traffic forecast, it does not provide information on the likelihood of the events that have been tested actually occurring. Simply combining the low or high cases does not consider the interdependency between different variables and represents a highly unlikely outcome. A formal risk analysis, as described below, provides a more thorough understanding of the risks as well as the likelihood of occurrence.

Each of the variables and accompanying sensitivity testing is discussed further below.

Table 8.1 - Summary Sensitivity Results

Sensitivity Test Year Total Revenue (2008 $)

% change against base case

Toll Schedule v1 (base case) 2015 $103.3 -2030 $195.8 -

Test 1a Low Background Growth 2015 $91.0 -12%(-2yrs 2015) (-5yrs 2030) 2030 $165.0 -16%

Test 1b High Background Growth 2015 $115.6 12% (+2yrs 2015) (+5yrs 2030) 2030 $217.0 11%

Test 3a Change VOC 2015 $92.5 -10%(+13%) Fuel (+20%) 2030 $173.6 -11%

Test 3b Change VOC 2015 $118.6 15%(-13%) Fuel (-20%) 2030 $217.5 11%

Test 4a Change VOT 2015 $86.8 -16% (-20%) 2030 $171.5 -12%

Test 4b Change VOT 2015 $115.2 12% (+20%) 2030 $210.2 7%

Test 5a Slugs(-5% 2015, -7% 2030)HOV3+/Transit (SOV +2% AM, 2015 $107.0 4%+1% PM, 0% OP - 2015 & 2030) 2030 $202.0 3%

Test 5b Slugs(+5% 2015, +7% 2030)HOV3+/Transit (SOV -2% AM, 2015 $99.2 -4%-1% PM, 0% OP - 2015 & 2030) 2030 $189.2 -3%

Test 6a Annual Expansion Factor 2015 $99.9 -3%Low (290) 2030 $189.3 -3%

Test 6b Annual Expansion Factor 2015 $108.5 5%Medium (315) 2030 $205.6 5%

Test 6c Annual Expansion Factor 2015 $111.9 8%High (325) 2030 $212.1 8%

75

I-95/I-395 HOT Lane


Figure 8.1 - Summary impact of sensitivity tests on Base Case revenue in 2015

Sensitivity Tests - 2015

-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%

Background Growth VOC VOT Slugs/HOV3+/Transit Annual ExpansionFactor

% C

hang

e in

Ann

ual R

even

ue (2

008

$

Figure 8.2 - Summary impact of sensitivity tests on Base Case revenue in 2030

Sensitivity Test - 2030

-20%

-15%

-10%

-5%

0%

5%

10%

15%

Background Growth VOC VOT Slugs/HOV3+/Transit Annual ExpansionFactor

% C

hang

e in

Ann

ual R

even

ue (2

008

$

Background Traffic Growth

In the traffic model, population and employment data is used to predict future traffic levels. A preliminary five year running average annual growth for population and employment between 1995 and 2005 and the average annual growth for the base forecast were compared. Based on the preliminary analysis, a low case growth scenario (population and employment at minimum average historic growth) to 2015 would result in approximately a 2 year lag (e.g.,

76

I-95/I-395 HOT Lane


2013 levels in 2015), while the high case scenario (at maximum growth levels) would result in a 2 year advance (2017 levels by 2015). Similar analysis to 2030 suggests background growth could vary at the extremes by up to ±5 years. Sensitivity testing has therefore considered low and high scenarios where background growth is -2 years in 2015 and -5 years in 2030 for low growth, and high growth with +2 years in 2015 and +5 years in 2030.

Tests

• Test 1a – Low Background Growth (-2yrs 2015) (-5yrs 2030) • Test 1b – High Background Growth (+2yrs 2015) (+5yrs 2030)

Results

As would be expected, revenue is greater with the high background growth assumption than the base, and lower with low growth case. Revenue drops -12% in 2015 and -16% in 2030 for the low case (Test 1a), rising by 12% and 11% respectively for high (Test 1b).

Revenue impacts are higher in 2030 compared to 2015 for the low growth case, as the change assumed is a greater percentage reduction in traffic growth in the later years for this test. This amount of change is not replicated in the other direction for the high background growth, where the impacts in both 2015 and 2030 are similar, as a result of greater incidence of capacity constraint in the HOT lanes in the later time horizon.

Table 8.1 summarizes the results of the sensitivity tests in terms of the impact on revenue in 2015 and 2030. Appendix F includes revenue streams for sensitivity tests.

Ramp Up

Ramp-up is the period immediately following the opening of a project when travelers adjust their behavior to take advantage of the new facility. As the HOT lanes represent a change to an existing facility, rather than necessarily an entirely new one, the ramp up effect is likely to be smaller. However, the introduction of a toll option to a previously wholly toll-free facility may lead to some initial resistance to pay. This sensitivity test examines lower and higher ramp up factors, derived from information from similar facilities (such as SR91 in California). Table 8.2 shows the ramp-up factors used.

Table 8.2 - Ramp up Factors

central low high1 0.65 0.60 0.902 0.80 0.70 1.003 1.00 0.80 1.004 1.00 0.90 1.005 1.00 1.00 1.00

Ramp-up FactorsYear

77

I-95/I-395 HOT Lane


Tests

• Test 2a – Low Ramp Up • Test 2b – High Ramp Up

Results

The ramp-up factors have been applied separately to the northern and southern sections of the HOT lanes (as discussed in Chapter 4). Table 8.3 show how the ramp-up factors are applied to the northern and southern sections, for both high and low scenarios. Resulting full revenue streams for the ramp-up sensitivity tests are presented in Appendix F.

Table 8.3 - Ramp up Factors

HIGH case – TOTAL LOW case – TOTAL Year Unramped Ramp up Discounted

Revenue & RampedRevenue

2008 $M 2008 $M2012 71.6 45.7 2013 76.8 49.9 2014 81.9 48.8 2015 103.3 56.3 2016 109.5 54.9 2017 115.6 53.2 2018 121.8 51.5 2019 128.0 49.6 2020 134.1 47.7

Year Unramped Ramp up DiscountedRevenue & Ramped

Revenue2008 $M 2008 $M

2012 71.6 60% 30.4 2013 76.8 70% 34.9 2014 81.9 80% 39.1 2015 103.3 90% 50.6 2016 109.5 100% 54.2 2017 115.6 100% 52.8 2018 121.8 100% 51.2 2019 128.0 100% 49.6 2020 134.1 100% 47.7

HIGH case – Northern section only LOW case – Northern section only Year Unramped Ramp up Discounted


2008 $M 2008 $M2012 71.6 90% 45.7 2013 76.8 100% 49.9 2014 81.9 100% 48.8 2015 89.5 100% 49.0 2016 94.8 100% 47.6 2017 100.1 100% 46.1 2018 105.4 100% 44.5 2019 110.7 100% 42.9 2020 116.0 100% 41.2



2012 71.6 60% 30.4 2013 76.8 70% 34.9 2014 81.9 80% 39.1 2015 89.5 90% 44.1 2016 94.8 100% 47.6 2017 100.1 100% 46.1 2018 105.4 100% 44.5 2019 110.7 100% 42.9 2020 116.0 100% 41.2

HIGH case – Southern section only LOW case – Southern section only Year Unramped Ramp up Discounted


2008 $M 2008 $M2012 - - 2013 - - 2014 - - 2015 13.8 97% 7.3 2016 14.6 100% 7.4 2017 15.5 100% 7.2 2018 16.4 100% 6.9 2019 17.3 100% 6.7 2020 18.2 100% 6.5



2012 - - 2013 - - 2014 - - 2015 13.8 86% 6.5 2016 14.6 90% 6.6 2017 15.5 93% 6.7 2018 16.4 97% 6.7 2019 17.3 100% 6.7 2020 18.2 100% 6.5

78

I-95/I-395 HOT Lane


However, note that ramp-up factors are not included as a variable in the risk analysis, as their effects are reflected over several years, whereas the effects of other variables can be wholly tested for individual modeled years. The various ramp-up scenarios have been included in the derivation of the risk assessment revenue streams. Central ramp-up is applied to the base forecasts, while high ramp-up can be applied to a more risky revenue stream, and low to a less risky stream.

Vehicle Operating Cost (VOC)

The future of fuel prices is uncertain, particularly given the wide fluctuations in the last 12-months. Despite the recent reduction in oil prices however, there is a common perception that fuel prices are set to rise in real terms as demand outstrips supply, combined with constraints in oil refinery and distribution infrastructure, though this will be offset to some extent by improved fuel efficiency and new technology.

Vehicle operating cost used in the base case forecast is assumed at $0.167/mile (2005$). The calculation of travel costs (excluding time factors) within the traffic model is based on an estimate of 2005 vehicle operating costs, which are assumed to remain constant in real terms for the central forecasts. These costs apply to travel on all roads (tolled and un-tolled) and the short term route choice impacts of any changes in these costs are unlikely to have any significant impact on traffic flows on individual roads. In the longer term, significant changes in vehicle operating costs could lead to generally shorter or longer trips being undertaken, with consequential reductions or increases in traffic on individual highway links.

Both an upside and downside VOC assumption appears prudent in the current market. For the sensitivity tests, we have assumed a 20% increase in fuel prices as high oil-price low revenue case, with a corresponding low fuel price high revenue case reduction in fuel prices of 20%. Note that a 20% change in fuel prices results in a corresponding approximately 13% change in total vehicle operating costs, as VOC also includes allowance for costs such as maintenance and tires.

As well as adjusting the VOC assumptions used in the model assignment process, we have also modeled the impact that operating costs can have on trip lengths and trip totals. This has followed an elasticity model approach. Details behind the rationale and methodology applied in this approach can be found in Appendix G.

Tests

• Test 3a – Change VOC (+13%) Fuel (+20%) • Test 3b – Change VOC (-13%) Fuel (-20%)

79

I-95/I-395 HOT Lane


Results

Increasing fuel prices in the low revenue case resulted in similar reductions in total revenue in both 2015 and 2030 (revenue falling -10% and -11% respectively). When fuel prices are reduced in the high revenue case there is a bigger impact in 2015 than 2030 (+15% in 2015 versus +11% in 2030). This is related to higher congestion in the GP lanes making this test less sensitive in 2030.


Value of Time (VOT)

The value of time used in the base case forecast is assumed at $15.10/hour (2005$) which is derived from the TPB model and Halcrow's database. The VOT estimates for this study fall within our benchmark range for North America (US$11.50 to 18.05), but lie at the middle of this range. These values are assumed to increase in the future at GDP/capita but may increase at different rates in practice. It is acknowledged that a single value is potentially unrepresentative over an entire study area, so a range of values of time have been included in the sensitivity testing.

Values of time used or reported in studies we have undertaken or audited in North America vary from location to location (arguably reflecting different users’ perceptions by region), but are typically within ±20% of an overall central value. We have therefore used this range to indicate the limits VOT for this study. As such, a VOT of $18.10 was used for the high case and $12.10 for the low case.

Tests

• Test 4a – Change VOT (-20%) • Test 4b – Change VOT (+20%)

Results

In the low case there was a larger impact on revenue in 2015 than 2030 (-16% versus -12%). A similar pattern is observed for the high case (in this instance +12% in 2015 and +7% in 2030). This is due to higher congestion in the GP lanes in 2030, as well as an increase in driver’s willingness to pay tolls.


80

I-95/I-395 HOT Lane


Slugs, HOV3+ and Transit

In the base case, we have assumed that ‘slugging’ activity will continue to grow at the same rate as formal HOV3+ carpooling. According to 2006 AM slugging count data, the slugging activity has doubled since 1999 (note that slugging counts in 1999 are actually inferred estimates). This suggests that slugging activity could be growing faster than formal HOV3+, but it is difficult to determine if there will be more or less slugging activity with the introduction of HOT lanes, based on the existing information available on the behavior of slugs and HOV3+. There are also plans for additional transit services in the corridor, which will have some impact on car traffic in the corridor. As such, it is important to test a range of slug, HOV3+ and transit assumptions to understand the potential impact on HOT lane revenues. Low and high sensitivity assumptions have been determined for this combined market.

Slugging is not a government sponsored commuter program, but one created by local citizens to solve their own commuter problems. There are therefore no official plans setting out the potential growth (or reduction) in slugging activity. According to www.slug-lines.com, the following three requirements are all that is necessary in order to start a slug line:

• A parking area (typically a commuter parking lot); • The availability of an alternative transit system (bus, rail or metro); and • Flyers and notices to spread the word about the new slug line.

For the sensitivity tests, appropriate change rates have been determined by investigating the relationship between the existing number of slugs (using the 2006 slugger count data) and existing parking lots. This relationship was applied in 2015 and 2030, taking into account proposed parking lot changes along the corridor. Estimates of future parking spaces have been determined using the I-95/I-395 Transit/TDM Study, which proposed an additional 9,700 parking spaces by 2030.

HOV3+ trip matrices have therefore been modified to take account of changes in slugging activity along the I-95/I-395 corridor. The level of slugging is assumed to grow/reduce at ±5% of the formal HOV3+ growth rate in 2015, while in 2030 a change of ±7% is assumed. Transit was changed by assuming that the transit trips shifted to or from the single occupancy vehicle trips. SOV trips were then adjusted by ±2% for AM and ±1% for PM in the high and low revenue tests respectively.

Tests

• Test 5a – Slugs (-5% 2015, -7% 2030), HOV3+/Transit (SOV +2% AM, +1% PM, 0% OP – 2015 & 2030)

• Test 5b – Slugs (+5% 2015, +7% 2030), HOV3+/Transit (SOV -2% AM, -1% PM, 0% OP – 2015 & 2030)

81

I-95/I-395 HOT Lane


Results

The amount of effect on revenue that this sensitivity test identified was generally lower than the other tests, though the effect of this test was marginally higher in 2015 than 2030. Changes in revenue of +4% were modeled in 2015 and +3% in 2030 for the high case (less slugs and transit and more SOVs) and -4% and -3% respectively for the low case (more slugs and transit users).


Annual Expansion Factor

The precise annual expansion factor is the relationship between the average day modeled, and is a function of the level of congestion, use of the HOT lanes at weekends, VOT and vehicle occupancy by different trip purposes. Limited information is available on weekend use of HOT lanes, though it is an important factor.

Examining traffic count data along the I-95/I-395 corridor, indicates that annualization factors are generally high, with an average around 350, indicating that traffic flows on weekends are as high as those on weekdays. However, it is unlikely that toll-paying HOT lane use will be as evenly distributed. Hence, a factor of 300 was used in our base case, which is similar to that observed for the SR91 in California, where the average annualization factor for a weekday has been 297 over the last 5 years.

Examining more recent traffic count data on the SR91 enabled development of possible range of annualization factors to test.

• A low value of 290 was selected to test a level below the base case value, as a reasonable step down.

• An additional ‘medium’ value of 315 was also considered, as this is representative of the more recent observed relationship between the average amount of HOT lane paying traffic on SR91 for mid-week traffic (Tuesdays to Thursdays).

• An upper bound factor of 325 was chosen, as this represents an average of the annualization factors for toll-paying weekday (318) and a factored weekday (337) on the SR91.

Note that the factored weekday factor of 337 was determined by factoring the total paid traffic on the SR91 by the average contribution a typical day (including weekend days) makes to the overall total of both paid and unpaid traffic. This factoring is an attempt to make all days equivalent, to generate this upper bound expansion factor.

82

I-95/I-395 HOT Lane


Tests

• Test 6a – Annual Expansion Factor – Low • Test 6b – Annual Expansion Factor – Medium • Test 6c – Annual Expansion Factor – High

Results

The impact of the annualization factors on the revenue is not as significant as some of the other sensitivity tests, with -3% for the low case, +5% for medium and +8% for the high case. As the same factors are used in both 2015 and 2030, the change impact is the same on revenues in 2015 as 2030.


8.3 Risk Analysis

All forecasts are inherently subject to degree of uncertainty, related to which a level of risk can be associated with the likelihood that a forecast will be met. The risks associated with traffic forecasts derived from a traffic model (and their associated toll revenue estimates) can be ascribed to three main sources of risk:

• Assumptions used in the traffic models, or factors applied to model generated traffic or revenue estimates;

• Processes or parameters within the traffic models; and • Database used to calibrate and validate the models.

It is perhaps arguable whether it is possible to rigorously quantify risk for traffic forecasts. The range of assumptions can be extensive, and distribution of risk around little understood. However, many assumptions have little material effect on forecasts, and the risk around key issues is often reasonably understood. There is thus clearly a strong imperative to understand the likely relative levels of risk associated with such variables and the risk analysis process aims to do this. A risk analysis has been carried out to identify the range of probabilities surrounding the base traffic and revenue forecasts produced by Halcrow.

Methodology

The risk analysis process involves the following steps:

• Identify key input variables that affect the baseline forecasts; • Define probability distributions around each key variable; • Define sensitivity functions for each variable; and • Run the risk model (Monte Carlo simulation).

83

I-95/I-395 HOT Lane


Further details of the overall risk analysis methodology can be found in Appendix H.

The first task is to identify which variables need to be included in the risk analysis, and to identify the effects of changes in these variables on revenues. There are a number of sources of risk in traffic forecasts. Each of the sensitivities tested in the study represent key potential sources of risk as to why out-turn revenue could be higher or lower than the ‘central’ forecast, and is the essential reason for their inclusion in the sensitivity testing.

Secondly, a probability distribution needs to be defined for each of the selected variables. For each variable, maximum and minimum input values of the variable must be determined, and the shape of a probability curve designated. For variables that are likely to be more difficult to predict accurately a uniform distribution profile is selected. This assumes that any value within the range is more-or-less equally probable. When considering variables that are more centrally-focused, and there is some confidence that central values are likely to be the most appropriate and the variable is less likely to reach its extremes, a normal distribution is selected.

Table 8.4 provides a summary of the inputs to the risk analysis, and indicates how the key sensitivity tests were taken forward into the risk model. The risk analysis was undertaken separately for 2015 and 2030. Note that the remaining risk factors were assumed to be part of a general category of ‘other risk factors’ that is assumed to cover all other risks. This includes elements such as database error, model error, factors that we have not thought of and outcomes beyond the ranges we have considered.

Table 8.4 - Summary of inputs to risk analysis

Sensitivity Test Range (index on 2015/2030 Base Case forecasts)

2015 2030

Distribution profile

lower upper lower upper

Background Growth -12% +12% -16% +11% Normal VOC -10% +15% -11% +11% Uniform VOT -16% +12% -12% +7% Normal Slugs/HOV3+/Transit Reduction -4% + 4% -3% +3% Normal Annual Expansion -3% +8% -3% +8% Normal Other risk factors -20% +20% -20% +20% Normal

Once the probability distributions are defined and the impact of each variable on the revenues determined, the risk model is used to determine an overall probability distribution for the forecast revenues in each of the two forecast years. From these values, an 80% confidence interval revenue stream risk envelope has been determined, bounded by revenue streams that have a 90% and 10% probability of being met or exceeded.

84

I-95/I-395 HOT Lane


Note that ramp-up was not included as a risk variable, but has been applied directly to the revenue streams. Central ramp-up is applied to the basic central forecasts, while high ramp-up is applied to the 10% confidence revenue stream, and low ramp-up to the 90% confidence revenue stream

Results

The results of the risk analysis are shown in Table 8.5. This shows the annual revenue as indices of the equivalent year base case (expressed as 100). Figure 8.3 shows the revenue risk distribution curves for total annual revenue in each of the 2015 and 2030 model years. The key figures in Table 8.5 are the results for revenues with a 10% and 90% probability of being equaled or exceeded. These indicate that an 80% confidence interval spread of revenue is -14% to +20% of the base case forecast in 2015 and -16% to +15% in 2030.

Table 8.5 - Risk analysis results

Indices 2015 2030 Mean result 102.2 99.0 Median result 101.6 98.4 90% confidence 85.8 83.9 10% confidence 119.6 114.9

Note that results are indexed from the base case forecast for each model year (base = 100)

Figure 8.3 - Revenue distribution curves for 2015 and 2030

Figure 8.4 shows the 80% confidence interval risk envelope revenue streams from 2012 to 2071 (bounded by the 90% probability to 10%). Figure 8.5 is an alternative way of presenting the results and shows the probability of reaching the different revenues in each future year. Appendix I contains detailed results for the 90% and 10% confidence levels.

The distributions as shown in Figure 8.3 indicate that the base case itself likely to represent a result that is close to the average (both mean and median). In 2015 the average revenue probability result is +2% over the base case, indicating that the risk analysis judges the 2015

85

I-95/I-395 HOT Lane


modeled revenue to be slightly low. In 2030 the difference falls slightly to -1%, indicating that the 2030 modeled revenue is slightly above the average for the risk envelope. That the risk envelope narrows in 2030 compared to 2015, with the potential for achieving the base case revenue also reduced slightly, is related to the lower impact of VOC and VOT caused by increased willingness to pay and more traffic congestion in the later years.

Figure 8.4 - Revenue stream risk envelope (2012-2071)

2012-2071 Annual Revenue (80% confidence interval)

0

50

100

150

200

250

300

350

2012 2015 2018 2021 2024 2027 2030 2033 2036 2039 2042 2045 2048 2051 2054 2057 2060 2063 2066 2069 2072

Year

Annu

al R

even

ue (2

008$

mill

ions

)

10% confidence 90% confidence mean model

86

I-95/I-395 HOT Lane


Figure 8.5 - Total revenue probability for model years (2015 & 2030)

2015 & 2030 Annual Revenues (confidence range)

0

50

100

150

200

250

5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95%

Confidence

Annu

al R

even

ue (2

008$

mill

ions

)

2015 2030

87

I-95/I-395 HOT Lane

Summary

9 Summary

9.1 ‘Throughput’ Tests

To assess the effect of a key feature of the HOT lanes, namely that traffic speeds and journey times in the HOT lanes should not be compromised by traffic congestion, traffic speeds in the HOT lanes are modeled using the ‘full’ modeling procedure as shown in Figure 5.1.

This process is known as ‘throughput’ analysis in this report for a specific target minimum traffic speed. For these tests, toll rates can vary (i.e. rise) by as much as the model requires, in order to maintain the desired target minimum traffic speed. Three ‘throughput’ tests were carried out:

• 55 mph throughput` – the initial ‘base forecast’, carried out with a target minimum speed of 55 mph;

• 45 mph throughput – target minimum speed of 45 mph; and • 65 mph throughput – target minimum speed of 65 mph.

Annual revenue estimates range from $77.1M to $102.7M in 2015, increasing to $130.0M to $191.6M in 2030 for these tests. Results of the throughput tests are discussed in Chapter 7. Detailed results can then be found in Appendix E.

9.2 Revenue Optimization Tests

The ‘throughput’ tests form the basis of the methodology to identify ‘revenue optimal’ toll rates. Subsequently therefore to the throughput tests, a series of over 100 model runs were undertaken to identify the toll rates that give the optimal revenue for each segment of the HOT lanes.

These tests sought to ‘push’ the toll rates above and below those identified in the ‘throughput’ tests to determine the points at which toll revenues on individual segments were highest. The model runs did not employ the ‘full’ modeling procedure, in that the toll rates were fixed for each run of the model.

Culmination of the revenue optimization process is a potential toll schedule (Toll Schedule v1), based on the revenue optimal rates, but adjusted for compatibility with wider aims for the tolling regime.

Annual revenue estimates for the revenue optimal test are estimated at $105.7M in 2015, increasing to $210.5M in 2030. For Toll Schedule v1, revenues in 2015 are $103.3M and $195.8M in 2030. Details of the revenue optimization process and the results, and derivation

88

I-95/I-395 HOT Lane

Summary

of the recommended toll schedule can be found in Chapter 7. Results of tests of the revenue optimal toll rates and recommended toll schedule can be found in Appendix E.

9.3 Sensitivity Tests

The results of sensitivity testing provide an indication of the potential variability in forecasting HOT lane usage and revenue, and used to derive inputs to the risk analysis process.

Sensitivity test model runs also did not employ the ‘full’ modeling procedure. All the sensitivity tests have used the same toll levels (the potential toll schedule suggested at the end of the revenue optimization process).

All sensitivity tests have used the potential toll schedule outlined in Chapter 7 as their HOT lane charging basis, with variations in the sensitivity test variables only. Six variables have been tested, resulting in 13 sets of test results:

• Background traffic growth; • Early years’ ramp-up; • Vehicle operating costs; • Value of time; • Sluggers, HOV3 and transit use; and • Annual expansion factors.

Discussion of the sensitivity tests can be found in Chapter 8, with detailed revenue stream results in Appendix F.

9.4 Risk Analysis

The risk analysis carried out identifies a probability based assessment of the likelihood of achieving levels of revenue. A series of key variables was identified for inclusion in the risk analysis. Results of the sensitivity tests were then used to define the variable ranges.

The key output from the risk analysis is an 80% confidence interval risk envelope for the revenue stream (bounded by the revenue streams that have 10% and 90% probabilities of being equaled or exceeded). The results suggest that the 80% confidence interval spread of revenue is -14% to +20% of the base case forecast in 2015 and -16% to +15% in 2030. In 2015, the average revenue result is +2% above the base case, while in 2030 it is -1% below.

Full discussion of the risk analysis is in Chapter 8, with detailed revenue stream results for the resulting risk envelope in Appendix I.

89

I-95/I-395 HOT Lane

Appendix A

Appendix A – 2005 Base Year Model Validation Results

90

I-95/I-395 HOT Lane

Appendix A

I-95/I-395 Screenline Summary for AM

Obs Est %Diff GEH Obs Est %Diff GEH Obs Est %Diff GEHMorris Rd Massaponax Church Rd N 431 563 23% 5.9 6,036 5,862 -3% 2.3 6,467 6,425 -1% 0.5Massaponax Church Rd Jefferson Davis Highway (Rt 1) N 431 563 23% 5.9 6,036 5,862 -3% 2.3 6,467 6,425 -1% 0.5Jefferson Davis Highway (Rt 1) Germanna Highway (Rt 3) N 580 637 9% 2.3 8,126 8,322 2% 2.2 8,706 8,959 3% 2.7Germanna Highway (Rt 3) Warrenton Road (Rt 17) N 1,026 917 -12% 3.5 14,370 14,001 -3% 3.1 15,396 14,919 -3% 3.9Warrenton Road (Rt 17) Centreport Parkway (Rt 627) N 881 995 12% 3.7 12,337 13,056 6% 6.4 13,218 14,051 6% 7.1Centreport Parkway (Rt 627) Courthouse Road (Rt 630) N 946 982 4% 1.1 13,251 13,125 -1% 1.1 14,197 14,106 -1% 0.8Courthouse Road (Rt 630) Garrisonville Road (Rt 610) N 1,008 995 -1% 0.4 14,124 13,385 -6% 6.3 15,132 14,380 -5% 6.2Garrisonville Road (Rt 610) Russell Road N 1,137 1,036 -10% 3.1 15,932 15,592 -2% 2.7 17,069 16,628 -3% 3.4Russell Road Joplin Road (Rt 619) N 999 1,005 1% 0.2 13,993 14,024 0% 0.3 14,992 15,030 0% 0.3Joplin Road (Rt 619) Dumfries Road (Rt 234) N 879 1,069 18% 6.1 14,677 14,466 -1% 1.8 15,556 15,534 0% 0.2Dumfries Road (Rt 234) Dale Blvd (Rt 784) N 880 1,000 12% 3.9 14,686 13,955 -5% 6.1 15,566 14,955 -4% 4.9Dale Blvd (Rt 784) Opitz Blvd N 845 1,060 20% 7.0 15,035 16,185 7% 9.2 15,880 17,245 8% 10.6Opitz Blvd Prince William Pkwy(Rt 630/3000) N 835 936 11% 3.4 16,188 14,160 -14% 16.5 17,023 15,096 -13% 15.2Prince William Pkwy(Rt 630/3000) Gordon Blvd (Rt 123) N 839 1,115 25% 8.8 18,057 16,877 -7% 8.9 18,896 17,992 -5% 6.7Gordon Blvd (Rt 123) Lorton Rd (Rt 642) N 1,299 1,114 -17% 5.3 25,611 23,760 -8% 11.8 26,910 24,875 -8% 12.6Lorton Rd (Rt 642) Fairfax Cnty Pkwy (Rt 7100) N 1,202 1,217 1% 0.4 24,671 23,929 -3% 4.8 25,873 25,146 -3% 4.6Fairfax Cnty Pkwy (Rt 7100) Franconia-Springfield Parkway N 1,347 1,356 1% 0.3 24,604 24,415 -1% 1.2 25,951 25,771 -1% 1.1Franconia-Springfield Parkway Franconia Road (Rt 644) N 1,222 1,305 6% 2.3 22,976 21,872 -5% 7.4 24,198 23,177 -4% 6.6Franconia Road (Rt 644) Capital Beltway (I-495) N 1,659 1,663 0% 0.1 30,641 29,975 -2% 3.8 32,300 31,638 -2% 3.7Capital Beltway (I-495) Edsall Road (Rt 648) N 130 381 66% 15.7 23,270 22,168 -5% 7.3 23,400 22,548 -4% 5.6Edsall Road (Rt 648) Duke St (Rt 236) N 118 254 53% 10.0 21,872 21,044 -4% 5.7 21,990 21,298 -3% 4.7Duke St (Rt 236) Seminary Road N 139 200 30% 4.7 25,452 22,861 -11% 16.7 25,591 23,061 -11% 16.2Seminary Road King St (Rt 7) N 157 194 19% 2.8 27,850 26,509 -5% 8.1 28,007 26,702 -5% 7.9King St (Rt 7) S. Shirlington Road N 160 212 25% 3.8 28,225 27,968 -1% 1.5 28,385 28,180 -1% 1.2S. Shirlington Road S. Glebe Road (Rt 120) N 182 221 18% 2.7 31,332 30,529 -3% 4.6 31,514 30,751 -2% 4.3S. Glebe Road (Rt 120) S. Hayes Street N 171 134 -27% 3.0 29,938 28,872 -4% 6.2 30,109 29,006 -4% 6.4S. Hayes Street S. Eads Street N 138 136 -2% 0.2 23,103 22,372 -3% 4.8 23,241 22,508 -3% 4.8S. Eads Street Jefferson Davis Highway (Rt 1) N 138 136 -2% 0.2 21,408 23,015 7% 10.8 21,546 23,150 7% 10.7Jefferson Davis Highway (Rt 1) Boundary Channel Drive N 134 137 3% 0.3 25,453 26,127 3% 4.2 25,587 26,264 3% 4.2Boundary Channel Drive George Washington Mem. Pkwy N 278 236 -18% 2.6 34,051 34,820 2% 4.1 34,329 35,056 2% 3.9Morris Rd Massaponax Church Rd S 377 404 6% 1.3 5,288 5,443 3% 2.1 5,665 5,846 3% 2.4Massaponax Church Rd Jefferson Davis Highway (Rt 1) S 377 404 6% 1.3 5,288 5,443 3% 2.1 5,665 5,846 3% 2.4Jefferson Davis Highway (Rt 1) Germanna Highway (Rt 3) S 456 440 -4% 0.8 6,385 6,530 2% 1.8 6,841 6,970 2% 1.5Germanna Highway (Rt 3) Warrenton Road (Rt 17) S 589 574 -3% 0.6 8,246 8,059 -2% 2.1 8,835 8,633 -2% 2.2Warrenton Road (Rt 17) Centreport Parkway (Rt 627) S 514 462 -11% 2.4 7,201 7,842 8% 7.4 7,715 8,304 7% 6.6Centreport Parkway (Rt 627) Courthouse Road (Rt 630) S 521 547 5% 1.1 7,292 7,391 1% 1.2 7,813 7,938 2% 1.4Courthouse Road (Rt 630) Garrisonville Road (Rt 610) S 540 554 3% 0.6 7,563 7,449 -2% 1.3 8,103 8,003 -1% 1.1Garrisonville Road (Rt 610) Russell Road S 545 583 7% 1.6 7,635 7,276 -5% 4.2 8,180 7,859 -4% 3.6Russell Road Joplin Road (Rt 619) S 546 560 3% 0.6 7,645 7,484 -2% 1.9 8,191 8,044 -2% 1.6Joplin Road (Rt 619) Dumfries Road (Rt 234) S 595 639 7% 1.7 8,342 8,842 6% 5.4 8,937 9,481 6% 5.7Dumfries Road (Rt 234) Dale Blvd (Rt 784) S 556 559 0% 0.1 7,794 7,795 0% 0.0 8,350 8,354 0% 0.0Dale Blvd (Rt 784) Opitz Blvd S 584 538 -8% 1.9 8,177 8,317 2% 1.5 8,761 8,855 1% 1.0Opitz Blvd Prince William Pkwy(Rt 630/3000) S 584 538 -8% 1.9 8,177 8,317 2% 1.5 8,761 8,855 1% 1.0Prince William Pkwy(Rt 630/3000) Gordon Blvd (Rt 123) S 598 609 2% 0.5 8,379 8,160 -3% 2.4 8,977 8,769 -2% 2.2Gordon Blvd (Rt 123) Lorton Rd (Rt 642) S 550 562 2% 0.5 7,706 8,323 7% 6.9 8,256 8,885 7% 6.8Lorton Rd (Rt 642) Fairfax Cnty Pkwy (Rt 7100) S 600 643 7% 1.7 8,408 8,908 6% 5.4 9,008 9,551 6% 5.6Fairfax Cnty Pkwy (Rt 7100) Franconia-Springfield Parkway S 839 799 -5% 1.4 11,752 11,761 0% 0.1 12,591 12,560 0% 0.3Franconia-Springfield Parkway Franconia Road (Rt 644) S 839 799 -5% 1.4 11,752 11,761 0% 0.1 12,591 12,560 0% 0.3Franconia Road (Rt 644) Capital Beltway (I-495) S 803 877 8% 2.5 11,249 12,502 10% 11.5 12,052 13,379 10% 11.8Capital Beltway (I-495) Edsall Road (Rt 648) S 89 167 47% 6.9 10,911 10,813 -1% 0.9 11,000 10,980 0% 0.2Edsall Road (Rt 648) Duke St (Rt 236) S 93 102 9% 0.9 11,396 11,305 -1% 0.9 11,489 11,408 -1% 0.8Duke St (Rt 236) Seminary Road S 91 123 26% 3.1 11,150 11,579 4% 4.0 11,241 11,702 4% 4.3Seminary Road King St (Rt 7) S 88 112 21% 2.3 10,816 11,352 5% 5.1 10,904 11,463 5% 5.3King St (Rt 7) S. Shirlington Road S 95 95 -1% 0.1 11,659 11,790 1% 1.2 11,754 11,885 1% 1.2S. Shirlington Road S. Glebe Road (Rt 120) S 102 162 37% 5.2 12,522 13,465 7% 8.3 12,624 13,626 7% 8.7S. Glebe Road (Rt 120) S. Hayes Street S 97 109 11% 1.1 11,907 11,802 -1% 1.0 12,004 11,911 -1% 0.9S. Hayes Street S. Eads Street S 63 53 -18% 1.3 7,658 8,676 12% 11.3 7,721 8,729 12% 11.1S. Eads Street Jefferson Davis Highway (Rt 1) S 63 53 -18% 1.3 7,658 8,676 12% 11.3 7,721 8,729 12% 11.1Jefferson Davis Highway (Rt 1) Boundary Channel Drive S 130 141 8% 0.9 15,921 14,313 -11% 13.1 16,051 14,454 -11% 12.9Boundary Channel Drive George Washington Mem. Pkwy S 184 192 4% 0.5 22,540 23,449 4% 6.0 22,724 23,641 4% 6.0

Sc10062 Sc10062Truck Auto Total

Sc10062From To Dir

91

I-95/I-395 HOT Lane

Appendix A

I-95/I-395 Screenline Summary for PM

Obs Est %Diff GEH Obs Est %Diff GEH Obs Est %Diff GEHMorris Rd Massaponax Church Rd N 620 649 4% 1.1 6,358 6,681 5% 4.0 6,978 7,330 5% 4.2Massaponax Church Rd Jefferson Davis Highway (Rt 1) N 620 649 4% 1.1 6,358 6,681 5% 4.0 6,978 7,330 5% 4.2Jefferson Davis Highway (Rt 1) Germanna Highway (Rt 3) N 719 686 -5% 1.2 7,367 7,159 -3% 2.4 8,086 7,845 -3% 2.7Germanna Highway (Rt 3) Warrenton Road (Rt 17) N 872 806 -8% 2.3 8,937 9,087 2% 1.6 9,809 9,893 1% 0.8Warrenton Road (Rt 17) Centreport Parkway (Rt 627) N 649 723 10% 2.8 6,648 7,869 16% 14.3 7,297 8,592 15% 14.5Centreport Parkway (Rt 627) Courthouse Road (Rt 630) N 667 708 6% 1.6 6,837 7,275 6% 5.2 7,504 7,983 6% 5.4Courthouse Road (Rt 630) Garrisonville Road (Rt 610) N 718 721 0% 0.1 7,359 7,370 0% 0.1 8,077 8,091 0% 0.2Garrisonville Road (Rt 610) Russell Road N 779 797 2% 0.6 7,984 8,708 8% 7.9 8,763 9,505 8% 7.8Russell Road Joplin Road (Rt 619) N 823 777 -6% 1.6 8,433 8,115 -4% 3.5 9,256 8,892 -4% 3.8Joplin Road (Rt 619) Dumfries Road (Rt 234) N 920 905 -2% 0.5 9,428 9,944 5% 5.2 10,348 10,849 5% 4.9Dumfries Road (Rt 234) Dale Blvd (Rt 784) N 899 884 -2% 0.5 9,211 9,034 -2% 1.9 10,110 9,918 -2% 1.9Dale Blvd (Rt 784) Opitz Blvd N 908 991 8% 2.7 9,303 11,224 17% 19.0 10,211 12,215 16% 18.9Opitz Blvd Prince William Pkwy(Rt 630/3000) N 999 915 -9% 2.7 10,241 9,196 -11% 10.6 11,240 10,112 -11% 10.9Prince William Pkwy(Rt 630/3000) Gordon Blvd (Rt 123) N 982 1,011 3% 0.9 10,062 10,204 1% 1.4 11,044 11,215 2% 1.6Gordon Blvd (Rt 123) Lorton Rd (Rt 642) N 1,051 1,004 -5% 1.5 10,772 11,366 5% 5.6 11,823 12,370 4% 5.0Lorton Rd (Rt 642) Fairfax Cnty Pkwy (Rt 7100) N 985 998 1% 0.4 10,099 10,348 2% 2.5 11,084 11,346 2% 2.5Fairfax Cnty Pkwy (Rt 7100) Franconia-Springfield Parkway N 1,038 1,014 -2% 0.7 10,636 10,518 -1% 1.1 11,674 11,532 -1% 1.3Franconia-Springfield Parkway Franconia Road (Rt 644) N 870 973 11% 3.4 8,917 8,528 -5% 4.2 9,787 9,501 -3% 2.9Franconia Road (Rt 644) Capital Beltway (I-495) N 1,283 1,212 -6% 2.0 13,148 14,350 8% 10.2 14,431 15,563 7% 9.2Capital Beltway (I-495) Edsall Road (Rt 648) N 93 166 44% 6.5 12,107 12,590 4% 4.3 12,200 12,756 4% 5.0Edsall Road (Rt 648) Duke St (Rt 236) N 117 105 -11% 1.1 15,266 14,800 -3% 3.8 15,383 14,905 -3% 3.9Duke St (Rt 236) Seminary Road N 122 118 -4% 0.4 15,972 15,781 -1% 1.5 16,094 15,899 -1% 1.5Seminary Road King St (Rt 7) N 126 122 -3% 0.4 16,523 16,588 0% 0.5 16,649 16,711 0% 0.5King St (Rt 7) S. Shirlington Road N 114 126 9% 1.0 14,917 15,972 7% 8.5 15,031 16,097 7% 8.5S. Shirlington Road S. Glebe Road (Rt 120) N 123 165 25% 3.5 16,091 16,509 3% 3.3 16,214 16,674 3% 3.6S. Glebe Road (Rt 120) S. Hayes Street N 108 146 26% 3.4 14,154 13,822 -2% 2.8 14,262 13,968 -2% 2.5S. Hayes Street S. Eads Street N 75 374 80% 19.9 9,825 13,916 29% 37.5 9,900 14,289 31% 39.9S. Eads Street Jefferson Davis Highway (Rt 1) N 75 374 80% 19.9 9,825 13,916 29% 37.5 9,900 14,289 31% 39.9Jefferson Davis Highway (Rt 1) Boundary Channel Drive N 86 329 74% 16.9 11,188 9,688 -15% 14.7 11,274 10,017 -13% 12.2Boundary Channel Drive George Washington Mem. Pkwy N 188 648 71% 22.5 24,542 25,614 4% 6.8 24,730 26,261 6% 9.6Morris Rd Massaponax Church Rd S 775 795 3% 0.7 7,940 8,100 2% 1.8 8,715 8,896 2% 1.9Massaponax Church Rd Jefferson Davis Highway (Rt 1) S 775 795 3% 0.7 7,940 8,100 2% 1.8 8,715 8,896 2% 1.9Jefferson Davis Highway (Rt 1) Germanna Highway (Rt 3) S 945 910 -4% 1.2 9,690 9,650 0% 0.4 10,635 10,560 -1% 0.7Germanna Highway (Rt 3) Warrenton Road (Rt 17) S 1,294 1,176 -10% 3.4 13,257 13,415 1% 1.4 14,551 14,591 0% 0.3Warrenton Road (Rt 17) Centreport Parkway (Rt 627) S 1,105 1,113 1% 0.2 11,324 12,256 8% 8.6 12,429 13,369 7% 8.3Centreport Parkway (Rt 627) Courthouse Road (Rt 630) S 1,202 1,227 2% 0.7 12,318 12,210 -1% 1.0 13,520 13,438 -1% 0.7Courthouse Road (Rt 630) Garrisonville Road (Rt 610) S 1,288 1,255 -3% 0.9 13,200 13,344 1% 1.2 14,488 14,599 1% 0.9Garrisonville Road (Rt 610) Russell Road S 1,458 1,337 -9% 3.2 14,941 16,112 7% 9.4 16,399 17,448 6% 8.1Russell Road Joplin Road (Rt 619) S 1,456 1,319 -10% 3.7 14,922 14,276 -5% 5.3 16,378 15,595 -5% 6.2Joplin Road (Rt 619) Dumfries Road (Rt 234) S 1,130 1,341 16% 6.0 15,193 15,934 5% 5.9 16,323 17,275 6% 7.3Dumfries Road (Rt 234) Dale Blvd (Rt 784) S 1,344 1,348 0% 0.1 17,384 17,996 3% 4.6 18,728 19,344 3% 4.5Dale Blvd (Rt 784) Opitz Blvd S 1,640 1,415 -16% 5.8 21,674 21,078 -3% 4.1 23,314 22,493 -4% 5.4Opitz Blvd Prince William Pkwy(Rt 630/3000) S 1,486 1,415 -5% 1.9 21,828 21,078 -4% 5.1 23,314 22,493 -4% 5.4Prince William Pkwy(Rt 630/3000) Gordon Blvd (Rt 123) S 1,578 1,588 1% 0.2 25,250 24,248 -4% 6.4 26,828 25,836 -4% 6.1Gordon Blvd (Rt 123) Lorton Rd (Rt 642) S 1,739 1,586 -10% 3.7 27,959 27,528 -2% 2.6 29,698 29,114 -2% 3.4Lorton Rd (Rt 642) Fairfax Cnty Pkwy (Rt 7100) S 1,519 1,603 5% 2.1 26,272 26,304 0% 0.2 27,791 27,907 0% 0.7Fairfax Cnty Pkwy (Rt 7100) Franconia-Springfield Parkway S 1,800 1,777 -1% 0.6 26,479 27,982 5% 9.1 28,279 29,758 5% 8.7Franconia-Springfield Parkway Franconia Road (Rt 644) S 1,723 1,777 3% 1.3 26,364 26,538 1% 1.1 28,087 28,314 1% 1.4Franconia Road (Rt 644) Capital Beltway (I-495) S 2,215 1,991 -11% 4.9 33,104 33,338 1% 1.3 35,319 35,330 0% 0.1Capital Beltway (I-495) Edsall Road (Rt 648) S 148 184 19% 2.7 29,552 29,340 -1% 1.2 29,700 29,523 -1% 1.0Edsall Road (Rt 648) Duke St (Rt 236) S 121 101 -21% 2.0 26,063 25,715 -1% 2.2 26,184 25,816 -1% 2.3Duke St (Rt 236) Seminary Road S 141 98 -45% 4.0 26,493 26,768 1% 1.7 26,634 26,865 1% 1.4Seminary Road King St (Rt 7) S 134 80 -67% 5.2 25,988 25,931 0% 0.4 26,122 26,011 0% 0.7King St (Rt 7) S. Shirlington Road S 147 79 -86% 6.4 27,585 25,371 -9% 13.6 27,732 25,450 -9% 14.0S. Shirlington Road S. Glebe Road (Rt 120) S 109 123 12% 1.3 23,055 26,771 14% 23.5 23,164 26,894 14% 23.6S. Glebe Road (Rt 120) S. Hayes Street S 146 102 -43% 3.9 27,931 26,892 -4% 6.3 28,077 26,994 -4% 6.5S. Hayes Street S. Eads Street S 83 294 72% 15.4 20,480 18,625 -10% 13.3 20,563 18,919 -9% 11.7S. Eads Street Jefferson Davis Highway (Rt 1) S 83 349 76% 18.1 18,280 20,356 10% 14.9 18,363 20,705 11% 16.8Jefferson Davis Highway (Rt 1) Boundary Channel Drive S 146 622 76% 24.3 26,608 24,956 -7% 10.3 26,754 25,578 -5% 7.3Boundary Channel Drive George Washington Mem. Pkwy S 231 685 66% 21.2 30,246 33,949 11% 20.7 30,477 34,634 12% 23.0

Sc20135 Sc20135 Sc20135Truck Auto TotalFrom To Dir

92

I-95/I-395 HOT Lane

Appendix A

I-95/I-395 Screenline Summary for Off Peak

Obs Est %Diff GEH Obs Est %Diff GEH Obs Est %Diff GEHMorris Rd Massaponax Church Rd N 891 808 -10% 2.8 4,060 3,970 -2% 1.4 4,951 4,779 -4% 2.5Massaponax Church Rd Jefferson Davis Highway (Rt 1) N 842 808 -4% 1.2 4,109 3,970 -4% 2.2 4,951 4,779 -4% 2.5Jefferson Davis Highway (Rt 1) Germanna Highway (Rt 3) N 959 823 -17% 4.6 4,683 4,382 -7% 4.5 5,642 5,205 -8% 5.9Germanna Highway (Rt 3) Warrenton Road (Rt 17) N 1,320 943 -40% 11.2 6,447 5,878 -10% 7.2 7,767 6,821 -14% 11.1Warrenton Road (Rt 17) Centreport Parkway (Rt 627) N 588 768 23% 6.9 5,292 6,328 16% 13.6 5,880 7,096 17% 15.1Centreport Parkway (Rt 627) Courthouse Road (Rt 630) N 601 743 19% 5.5 5,408 6,015 10% 8.0 6,009 6,758 11% 9.4Courthouse Road (Rt 630) Garrisonville Road (Rt 610) N 640 737 13% 3.7 5,762 5,573 -3% 2.5 6,402 6,310 -1% 1.2Garrisonville Road (Rt 610) Russell Road N 715 759 6% 1.6 6,431 6,330 -2% 1.3 7,145 7,089 -1% 0.7Russell Road Joplin Road (Rt 619) N 689 728 5% 1.5 6,200 6,231 0% 0.4 6,889 6,959 1% 0.8Joplin Road (Rt 619) Dumfries Road (Rt 234) N 747 783 5% 1.3 6,719 6,513 -3% 2.5 7,466 7,297 -2% 2.0Dumfries Road (Rt 234) Dale Blvd (Rt 784) N 669 714 6% 1.7 6,764 6,559 -3% 2.5 7,433 7,273 -2% 1.9Dale Blvd (Rt 784) Opitz Blvd N 656 745 12% 3.4 6,632 8,175 19% 17.9 7,288 8,920 18% 18.1Opitz Blvd Prince William Pkwy(Rt 630/3000) N 728 677 -8% 1.9 7,357 6,738 -9% 7.4 8,085 7,415 -9% 7.6Prince William Pkwy(Rt 630/3000) Gordon Blvd (Rt 123) N 692 726 5% 1.3 7,956 7,518 -6% 5.0 8,648 8,244 -5% 4.4Gordon Blvd (Rt 123) Lorton Rd (Rt 642) N 742 687 -8% 2.0 8,532 8,588 1% 0.6 9,274 9,275 0% 0.0Lorton Rd (Rt 642) Fairfax Cnty Pkwy (Rt 7100) N 728 729 0% 0.0 8,368 8,210 -2% 1.7 9,096 8,939 -2% 1.7Fairfax Cnty Pkwy (Rt 7100) Franconia-Springfield Parkway N 754 776 3% 0.8 8,672 8,276 -5% 4.3 9,426 9,052 -4% 3.9Franconia-Springfield Parkway Franconia Road (Rt 644) N 664 753 12% 3.3 7,640 7,202 -6% 5.1 8,304 7,955 -4% 3.9Franconia Road (Rt 644) Capital Beltway (I-495) N 1,037 949 -9% 2.8 10,486 11,798 11% 12.4 11,523 12,747 10% 11.1Capital Beltway (I-495) Edsall Road (Rt 648) N 231 332 30% 6.0 7,469 7,987 6% 5.9 7,700 8,320 7% 6.9Edsall Road (Rt 648) Duke St (Rt 236) N 269 293 8% 1.4 8,694 7,864 -11% 9.1 8,963 8,156 -10% 8.7Duke St (Rt 236) Seminary Road N 303 299 -2% 0.3 9,810 8,870 -11% 9.7 10,113 9,169 -10% 9.6Seminary Road King St (Rt 7) N 307 328 6% 1.2 9,913 9,746 -2% 1.7 10,220 10,074 -1% 1.5King St (Rt 7) S. Shirlington Road N 281 327 14% 2.6 9,080 9,686 6% 6.3 9,361 10,013 7% 6.6S. Shirlington Road S. Glebe Road (Rt 120) N 309 367 16% 3.1 9,992 10,581 6% 5.8 10,301 10,948 6% 6.3S. Glebe Road (Rt 120) S. Hayes Street N 279 291 4% 0.7 9,011 8,787 -3% 2.4 9,290 9,078 -2% 2.2S. Hayes Street S. Eads Street N 219 385 43% 9.5 7,091 7,390 4% 3.5 7,310 7,775 6% 5.3S. Eads Street Jefferson Davis Highway (Rt 1) N 219 385 43% 9.5 7,091 7,390 4% 3.5 7,310 7,775 6% 5.3Jefferson Davis Highway (Rt 1) Boundary Channel Drive N 218 310 30% 5.6 7,063 5,776 -22% 16.1 7,281 6,086 -20% 14.6Boundary Channel Drive George Washington Mem. Pkwy N 438 524 17% 4.0 14,146 13,917 -2% 1.9 14,584 14,441 -1% 1.2Morris Rd Massaponax Church Rd S 1,012 947 -7% 2.1 4,609 4,773 3% 2.4 5,621 5,719 2% 1.3Massaponax Church Rd Jefferson Davis Highway (Rt 1) S 956 947 -1% 0.3 4,665 4,773 2% 1.6 5,621 5,719 2% 1.3Jefferson Davis Highway (Rt 1) Germanna Highway (Rt 3) S 1,195 981 -22% 6.5 5,836 5,351 -9% 6.5 7,031 6,333 -11% 8.5Germanna Highway (Rt 3) Warrenton Road (Rt 17) S 1,524 1,097 -39% 11.8 7,442 7,107 -5% 3.9 8,966 8,204 -9% 8.2Warrenton Road (Rt 17) Centreport Parkway (Rt 627) S 760 882 14% 4.3 6,836 7,658 11% 9.6 7,596 8,540 11% 10.5Centreport Parkway (Rt 627) Courthouse Road (Rt 630) S 793 929 15% 4.7 7,134 7,847 9% 8.2 7,927 8,777 10% 9.3Courthouse Road (Rt 630) Garrisonville Road (Rt 610) S 837 923 9% 2.9 7,529 7,516 0% 0.2 8,366 8,438 1% 0.8Garrisonville Road (Rt 610) Russell Road S 904 953 5% 1.6 8,135 8,481 4% 3.8 9,039 9,434 4% 4.1Russell Road Joplin Road (Rt 619) S 911 913 0% 0.1 8,198 7,979 -3% 2.4 9,109 8,893 -2% 2.3Joplin Road (Rt 619) Dumfries Road (Rt 234) S 698 987 29% 10.0 8,575 8,920 4% 3.7 9,272 9,906 6% 6.5Dumfries Road (Rt 234) Dale Blvd (Rt 784) S 664 935 29% 9.6 9,009 9,502 5% 5.1 9,673 10,436 7% 7.6Dale Blvd (Rt 784) Opitz Blvd S 738 930 21% 6.6 10,446 10,460 0% 0.1 11,184 11,390 2% 1.9Opitz Blvd Prince William Pkwy(Rt 630/3000) S 623 930 33% 11.0 10,561 10,460 -1% 1.0 11,184 11,390 2% 1.9Prince William Pkwy(Rt 630/3000) Gordon Blvd (Rt 123) S 603 1,000 40% 14.0 12,116 11,510 -5% 5.6 12,719 12,510 -2% 1.9Gordon Blvd (Rt 123) Lorton Rd (Rt 642) S 582 948 39% 13.2 12,464 12,651 1% 1.7 13,046 13,600 4% 4.8Lorton Rd (Rt 642) Fairfax Cnty Pkwy (Rt 7100) S 575 1,015 43% 15.6 13,288 12,819 -4% 4.1 13,863 13,833 0% 0.3Fairfax Cnty Pkwy (Rt 7100) Franconia-Springfield Parkway S 877 1,110 21% 7.4 13,676 13,267 -3% 3.5 14,553 14,376 -1% 1.5Franconia-Springfield Parkway Franconia Road (Rt 644) S 860 1,110 23% 8.0 13,635 13,267 -3% 3.2 14,495 14,376 -1% 1.0Franconia Road (Rt 644) Capital Beltway (I-495) S 892 1,235 28% 10.5 13,170 15,874 17% 22.4 14,062 17,109 18% 24.4Capital Beltway (I-495) Edsall Road (Rt 648) S 251 385 35% 7.5 12,569 11,708 -7% 7.8 12,820 12,093 -6% 6.5Edsall Road (Rt 648) Duke St (Rt 236) S 253 319 21% 3.9 12,644 11,276 -12% 12.5 12,897 11,596 -11% 11.8Duke St (Rt 236) Seminary Road S 317 335 5% 1.0 13,462 13,221 -2% 2.1 13,779 13,556 -2% 1.9Seminary Road King St (Rt 7) S 292 335 13% 2.5 12,769 12,590 -1% 1.6 13,061 12,925 -1% 1.2King St (Rt 7) S. Shirlington Road S 293 330 11% 2.1 12,829 11,997 -7% 7.5 13,122 12,327 -6% 7.0S. Shirlington Road S. Glebe Road (Rt 120) S 220 383 43% 9.4 10,623 13,047 19% 22.3 10,843 13,430 19% 23.5S. Glebe Road (Rt 120) S. Hayes Street S 280 336 17% 3.2 12,556 11,893 -6% 6.0 12,836 12,229 -5% 5.4S. Hayes Street S. Eads Street S 161 306 47% 9.5 9,121 9,481 4% 3.7 9,282 9,787 5% 5.2S. Eads Street Jefferson Davis Highway (Rt 1) S 161 306 47% 9.5 8,857 9,481 7% 6.5 9,018 9,787 8% 7.9Jefferson Davis Highway (Rt 1) Boundary Channel Drive S 300 480 38% 9.1 13,332 12,467 -7% 7.6 13,632 12,947 -5% 5.9Boundary Channel Drive George Washington Mem. Pkwy S 487 480 -1% 0.3 15,736 15,572 -1% 1.3 16,223 16,052 -1% 1.3

Sc30012 Sc30012 Sc30012Truck Auto TotalFrom To Dir

93

I-95/I-395 HOT Lane

Appendix B

Appendix B – Key Road Network Improvements (FAMPO CLRP)

94

I-95/I-395 HOT Lane

Appendix B

PRIMARY SYSTEM(St) Butler Road Rte 212 Rte 1 Castle Rock Widening 4 Widen roadway to 4-lanes.

(Sp) Germanna Highway Rte 3 Gordon Rd. (Rte. 627) Rutherford Dr Widening 6Widen and improve intersections andclose unsignalized crossovers.

(St) Jefferson DavisHighway Rte 1 Widewater Parkway Rte 610 Widening 6 Widen to a six -lane divided facility(St) Jefferson DavisHighway Rte 1 Rte 610 Rte 630 Widening 6 Widen to a six -lane divided facility(St) Jefferson DavisHighway Rte 1 Fredericksburg City

LimitsCourthouse Road (Rte

208) Widening 6 Widen to a six -lane divided facility(St) Jefferson DavisHighway Rte 1 Courthouse Road (Rte

208)Mills Drive (US 17

Bypass) Widening 6 Widen to a six -lane divided facility

(Sp) Mills Drive Rte 17Bypass Rte 1 Rte 2 Widening 4 Widen to a four -lane divided facility

(St) Route 1 River Bridge Rte 1 Rte 17 Business Pr. Anne Street Bridge Replacement 6 Replace with a six-lane bridge

(St) Warrenton Rd Rte 17 I-95 Village Parkway Widening 6Widen and improve roadway to a six-lanedivided facility.

URBAN SYSTEM - CITY OF FREDERICKSBURG

Fall Hill Ave - West City Limits Mary Washington BlvdExt Widening 4

Widen existing roadway to four-lanes witha bicycle path.

Jeff Davis Hwy Rte 1 South City Limits Rte 3 Interchange Widening &Improvements 6 Widen existing roadway to 6-lanes.

Jeff Davis Hwy Rte 1 Germanna Highway (Rt3) Princess Anne Street Widening &Improvements 6 Widen existing roadway to 6-lanes.

William St/Plank Rd Rte 3 Mahone Dr Jeff Davis Hwy Widening &Improvements 6 Widen existing roadway to 6-lanes

SECONDARY SYSTEM - SPOTSYLVANIA COUNTY

Gordon Rd Rte 627 Route 628 Route 620 Widening &Improvements 4 Widen existing roadway to a four lane

facility with sidewalks.

Harrison Rd Rte 620 Route 639 Route 1 Bypass Widening &Improvements 4

Reconstruct existing roadway to a fourlanedivided facility ,including I-95 OvP

Hood Dr Rte 636 Route 208 Route 1 Widening 4 Widen existing roadway to a four-lanedivided facility with sidewalks.

Leavells Rd Rte 639 Route 208 Route 620 Widening 4

Reconstruct existing roadway to a fourlanedivided facility with bicycle &pedestrian facilities.

Massaponax Church Rd Rte 608 Route 628 Route 1 Widening &Improvements 4

Reconstruct existing roadway to afour-lane divided facility withpedestrian facilities

Mine Rd Rte 636 Route 1 Route 638 Widening 4 Widen existing roadway to a fourlanesdivided facility with sidewalks

Morris Rd Rte 606 Route 1 Route 208 Widening &Improvements 4 Widen existing roadway to four-lanes

with a multi-use path.

Mudd Tavern Rd Rte 606 Route 1 I-95 Widening &Improvements 4 Widen existing roadway to four lanes.

Old Plank Rd Rte 610 Route 627 Route 612 Widening 4 Reconstruct existing facility to a fourlanedivided roadway with sidewalksand multi-use paths.

Smith Station Rd Rte 628 Route 608 Route 627 Widening 4Reconstruct roadway to a four-lanedivided facility with sidewalks.

SECONDARY SYSTEM - STAFFORD COUNTYCool Spring Rd Rte 607 Route 218 Route 3 Widening 4 Widen roadway to four-lanes.Courthouse Rd Rte 630 Route 648 I-95 Widening & Improvements 4Deacon Rd Rte 607 Route 218 Route 626 Widening & Improvements 4 Widen to four-lanes.

Garrisonville Rd Rte 610 Route 1 I-95 Sthbnd ramp Widening 8Wide facility to eight-lanes to reduce congestion

Garrisonville Rd Rte 610 I-95 Sthbnd ramp Route 684 Widening 6Widen existing facility to six-lanes to reducecongestion.

Garrisonville Rd Rte 610 Route 684 Route 641 Widening 6Widen existing facility to six-lanes toreduce congestion

Mine Rd (Ext.) Rte 684 Route 630 Route 628 New Construction 4Construct new four-lane roadwayconnecting Garrisonville and RamothChurch Road

Mine Rd (Ext.) Rte 684 Route 628 Route 627 Widening 4 Widen roadway to a four-lane dividedfacility

Shelton Shop Road Rte 648 Route 610 Route 627 Widening 4Widen roadway to four-lane dividedfacility

To Type of Improvement Number of Lanes Description of ImprovementStreet Name Route Number From

95

I-95/I-395 HOT Lane

Appendix C

Appendix C – Key Road Network Improvements (NCRTPB CLRP)

96

I-95/I-395 HOT Lane

Appendix C

Key Road Network Improvements (NCRTPB CLRP)

Jefferson DavisHighway US 1 Joplin Rd I-495 widening - widen to 6, 8 lanes

Interstate -95 I-95 New Construciton and Improvement - interchange reconstruction with access

ramps to I-495Old Mill Road - US 1 Meeres Rd widening 4 widen to 4 lanes

Interstate 95 I-95 New Construciton and Improvement - construct interchange

Interstate 66 I-66 Improvement - spot improvements inside the beltway

Interstate 66 I-66 Reconstruction - reconstruct interchangeInterstate 495 I-495 Reconstruction - reconstruct interchange

Ox Rd VA 123 Braddock Rd Occoquan Regional Park Acess Rd widening 6 widen 6 lanes

Fairfax County Pkwy VA 7100 Construction - interchange at Fair Lakes Parkway Fairfax County Pkwy VA 7100 I-66 Popes Head Rd widening 6 widen to 6 lanes

Street Name Route Number From To Type of Improvement Number of Lanes Description of Improvement

inside the beltway

Interchange at I-495

Interchange at VA 7900

Interchange at US 29Interchange at I-66

interchange at Fair Lakes Parkway

97

I-95/I-395 HOT Lane

Appendix D

Appendix D – I-95/I-395 HOT Ingress and Egress Ramps

98

I-95/I-395 HOT Lane

Appendix D

Southern Section

Version dated: 18-Aug-2008

99

I-95/I-395 HOT Lane

Appendix D

Northern Section

Revised version: 2-Jun-2008

100

I-95/I-395 HOT Lane

Appendix E

Appendix E – Detailed Results from Main Tests

101

I-95/I-395 HOT Lane

Appendix E

Main Tests – Detailed Results

This Appendix contains detailed results from the five main tests carried out. A revenue stream is provided for each sensitivity test, including separate streams for the northern and southern sectors. In addition, summary traffic information has also been included.

Main Tests

• 55mph Throughout • 45mph Throughout • 65mph Throughout • Revenue Optimal Toll Rates • Toll Schedule v1

Transactions

An initial assessment of the number of transactions has been carried out for the 55 mph throughput and revenue optimal toll rates tests. These were based on the premise that a ‘transaction’ is recorded whenever a vehicle is modeled to enter or leave the HOT lane (via any of the possible entry and exit points). This is a simplification to give an indication of the amount of traffic transactions there may be. Future refinement of the independent traffic and revenue forecasts will consider the situation where the number of transactions is directly related to the number of tolling gantries installed, as well as the speed monitoring regime. Transactions for 55 mph throughput and revenue optimal toll rates tests are in this Appendix alongside the other results for those tests. Transaction numbers have not been estimated for other tests.

102

I-95/I-395 HOT Lane

Appendix E

Revenue Stream – 55mph throughput – ALL

Year Unramped HOT Traffic Discounted Throughput @ 55mph, 6 segmentsRevenue Growth & Ramped

Revenue2008 $M post 2030 pa 2008 $M Key assumptions

2012 62.0 26.4 - Growth from 2015 to 2030 interpolated from model-year results2013 66.8 36.9 - Growth rate beyond 2030 assumed decreasing 0.1% per annum to 1%2014 71.6 42.7 - Revenue growth is capped by capacity of individual HOT lane sections2015 91.8 49.4 - Ramp-up of revenue assumed: 60% year 12016 97.2 3.9% 48.5 85% year 22017 102.6 3.8% 47.2 100% year 3 onwards2018 108.0 3.6% 45.6 Adjusted for Southern Section: 86% year 12019 113.4 3.5% 43.9 95% year 22020 118.7 3.4% 42.2 100% year 3 onwards2021 124.1 3.3% 40.5 - Model revenue 2005 prices increased to 2008 prices using CPI-U2022 129.5 3.2% 38.8 194.4 : 2005 Jan-Sep2023 134.9 3.1% 37.0 216.0 : 2008 Jan-Sep2024 140.3 3.0% 35.3 11.1% : 2005 Jan-Sep to 2008 Jan-Sep2025 145.7 2.9% 33.7 - Discount rate to 2008 prices : 9%2026 151.1 2.8% 32.0 - 2015 & 2030 Value of Time escalation based on growth in GDP2027 156.5 2.7% 30.4 0.75 x forecast increase in GDP per capita:2028 161.9 2.7% 28.9 1.0952 : 2005 to 20152029 167.3 2.6% 27.4 1.2989 : 2005 to 20302030 172.7 2.5% 25.9 2031 176.9 2.4% 24.4 2032 181.0 2.3% 22.9 2033 185.0 2.2% 21.5 2034 188.9 2.1% 20.1 2035 192.6 2.0% 18.8 2036 196.3 1.9% 17.6 2037 199.8 1.8% 16.4 2038 203.2 1.7% 15.3 2039 206.5 1.6% 14.3 2040 209.5 1.5% 13.3 2041 212.4 1.4% 12.4 2042 214.9 1.3% 11.5 2043 217.1 1.2% 10.6 2044 219.2 1.1% 9.9 2045 221.1 1.0% 9.1 2046 223.0 1.0% 8.4 2047 224.9 1.0% 7.8 2048 226.7 1.0% 7.2 2049 228.5 1.0% 6.7 2050 230.1 1.0% 6.2 2051 231.7 1.0% 5.7 2052 233.2 1.0% 5.3 2053 234.7 1.0% 4.9 2054 236.1 1.0% 4.5 2055 237.6 1.0% 4.1 2056 239.0 1.0% 3.8 2057 240.5 1.0% 3.5 2058 242.0 1.0% 3.3 2059 243.6 1.0% 3.0 2060 245.1 1.0% 2.8 2061 246.5 1.0% 2.6 2062 248.0 1.0% 2.4 2063 249.5 1.0% 2.2 2064 251.0 1.0% 2.0 2065 252.4 1.0% 1.9 2066 253.9 1.0% 1.7 2067 255.2 1.0% 1.6 2068 256.5 1.0% 1.5 2069 257.8 1.0% 1.3 2070 259.1 1.0% 1.2 2071 260.4 1.0% 1.1 Total 11,647.5 1,047.3

103

I-95/I-395 HOT Lane

Appendix E

Revenue Stream – 55mph throughput – NORTHERN SECTION



2012 62.0 26.4 - Growth from 2015 to 2030 interpolated from model-year results2013 66.8 36.9 - Growth rate beyond 2030 assumed decreasing 0.1% per annum to 1%2014 71.6 42.7 - Revenue growth is capped by capacity of individual HOT lane sections2015 80.6 44.1 - Ramp-up of revenue assumed: 60% year 12016 85.7 3.9% 43.0 85% year 22017 90.8 3.8% 41.8 100% year 3 onwards2018 95.9 3.6% 40.5 Adjusted for Southern Section: 86% year 12019 101.0 3.5% 39.1 95% year 22020 106.1 3.4% 37.7 100% year 3 onwards2021 111.2 3.3% 36.3 - Model revenue 2005 prices increased to 2008 prices using CPI-U2022 116.2 3.2% 34.8 194.4 : 2005 Jan-Sep2023 121.3 3.1% 33.3 216.0 : 2008 Jan-Sep2024 126.4 3.0% 31.8 11.1% : 2005 Jan-Sep to 2008 Jan-Sep2025 131.5 2.9% 30.4 - Discount rate to 2008 prices : 9%2026 136.6 2.8% 29.0 - 2015 & 2030 Value of Time escalation based on growth in GDP2027 141.7 2.7% 27.6 0.75 x forecast increase in GDP per capita:2028 146.8 2.7% 26.2 1.0952 : 2005 to 20152029 151.8 2.6% 24.9 1.2989 : 2005 to 20302030 156.9 2.5% 23.6 2031 160.7 2.4% 22.1 2032 164.5 2.3% 20.8 2033 168.1 2.2% 19.5 2034 171.7 2.1% 18.3 2035 175.2 2.0% 17.1 2036 178.6 1.9% 16.0 2037 181.8 1.8% 14.9 2038 185.0 1.7% 13.9 2039 187.9 1.6% 13.0 2040 190.8 1.5% 12.1 2041 193.5 1.4% 11.3 2042 195.7 1.3% 10.4 2043 197.7 1.2% 9.7 2044 199.6 1.1% 9.0 2045 201.3 1.0% 8.3 2046 203.0 1.0% 7.7 2047 204.7 1.0% 7.1 2048 206.4 1.0% 6.6 2049 208.0 1.0% 6.1 2050 209.5 1.0% 5.6 2051 211.0 1.0% 5.2 2052 212.3 1.0% 4.8 2053 213.6 1.0% 4.4 2054 214.9 1.0% 4.1 2055 216.3 1.0% 3.8 2056 217.6 1.0% 3.5 2057 218.9 1.0% 3.2 2058 220.3 1.0% 3.0 2059 221.6 1.0% 2.7 2060 223.0 1.0% 2.5 2061 224.3 1.0% 2.3 2062 225.7 1.0% 2.1 2063 227.0 1.0% 2.0 2064 228.4 1.0% 1.8 2065 229.7 1.0% 1.7 2066 231.1 1.0% 1.6 2067 232.3 1.0% 1.4 2068 233.5 1.0% 1.3 2069 234.7 1.0% 1.2 2070 235.8 1.0% 1.1 2071 237.0 1.0% 1.0 Total 10,594.0 954.3

104

I-95/I-395 HOT Lane

Appendix E

Revenue Stream – 55mph throughput – SOUTHERN SECTION



2012 - - - Growth from 2015 to 2030 interpolated from model-year results2013 - - - Growth rate beyond 2030 assumed decreasing 0.1% per annum to 1%2014 - - - Revenue growth is capped by capacity of individual HOT lane sections2015 11.1 5.3 - Ramp-up of revenue assumed: 60% year 12016 11.4 3.9% 5.4 85% year 22017 11.7 3.8% 5.4 100% year 3 onwards2018 12.1 3.6% 5.1 Adjusted for Southern Section: 86% year 12019 12.4 3.5% 4.8 95% year 22020 12.7 3.4% 4.5 100% year 3 onwards2021 13.0 3.3% 4.2 - Model revenue 2005 prices increased to 2008 prices using CPI-U2022 13.3 3.2% 4.0 194.4 : 2005 Jan-Sep2023 13.6 3.1% 3.7 216.0 : 2008 Jan-Sep2024 13.9 3.0% 3.5 11.1% : 2005 Jan-Sep to 2008 Jan-Sep2025 14.2 2.9% 3.3 - Discount rate to 2008 prices : 9%2026 14.5 2.8% 3.1 - 2015 & 2030 Value of Time escalation based on growth in GDP2027 14.8 2.7% 2.9 0.75 x forecast increase in GDP per capita:2028 15.1 2.7% 2.7 1.0952 : 2005 to 20152029 15.5 2.6% 2.5 1.2989 : 2005 to 20302030 15.8 2.5% 2.4 2031 16.1 2.4% 2.2 2032 16.5 2.3% 2.1 2033 16.8 2.2% 2.0 2034 17.1 2.1% 1.8 2035 17.4 2.0% 1.7 2036 17.7 1.9% 1.6 2037 18.0 1.8% 1.5 2038 18.3 1.7% 1.4 2039 18.5 1.6% 1.3 2040 18.8 1.5% 1.2 2041 19.0 1.4% 1.1 2042 19.2 1.3% 1.0 2043 19.4 1.2% 1.0 2044 19.6 1.1% 0.9 2045 19.8 1.0% 0.8 2046 19.9 1.0% 0.8 2047 20.1 1.0% 0.7 2048 20.3 1.0% 0.6 2049 20.4 1.0% 0.6 2050 20.6 1.0% 0.6 2051 20.7 1.0% 0.5 2052 20.9 1.0% 0.5 2053 21.0 1.0% 0.4 2054 21.2 1.0% 0.4 2055 21.3 1.0% 0.4 2056 21.5 1.0% 0.3 2057 21.6 1.0% 0.3 2058 21.8 1.0% 0.3 2059 21.9 1.0% 0.3 2060 22.1 1.0% 0.3 2061 22.2 1.0% 0.2 2062 22.3 1.0% 0.2 2063 22.5 1.0% 0.2 2064 22.6 1.0% 0.2 2065 22.7 1.0% 0.2 2066 22.8 1.0% 0.2 2067 22.9 1.0% 0.1 2068 23.0 1.0% 0.1 2069 23.1 1.0% 0.1 2070 23.3 1.0% 0.1 2071 23.4 1.0% 0.1 Total 1,053.5 92.9

105

I-95/I-395 HOT Lane

Appendix E

Segment Summary – 55mph throughput

All prices in 2008 $ Northern Section 2015 2030All traffic flows weighted averages Southern Section AM PM OP AM PM OP

TOLL RATESA Seminary Rd (Rt 420) Eads Street/Pentagon $1.70 $0.61 $0.12 $2.16 $1.60 $0.15B Franconia-Springfield Pkwy (Rt 7900) Seminary Rd (Rt 420) $0.73 $0.85 $0.12 $1.59 $1.74 $0.14C Gordon Blvd (Rt 123) Franconia-Springfield Pkwy (Rt 7900) $0.12 $0.73 $0.12 $0.87 $1.73 $0.14D Garrisonville Rd (Rt 610) Gordon Blvd (Rt 123) $0.12 $0.73 $0.12 $0.87 $2.03 $0.29E Mountain View Rd (Rt 627) Garrisonville Rd (Rt 610) $0.12 $0.12 $0.12 $0.14 $0.14 $0.14F Massaponax Mountain View Rd (Rt 627) $0.12 $0.12 $0.12 $0.14 $0.14 $0.14

TOTAL TOLLS FOR SEGMENTA Seminary Rd (Rt 420) Eads Street/Pentagon $5.98 $2.15 $0.43 $7.60 $5.62 $0.51B Franconia-Springfield Pkwy (Rt 7900) Seminary Rd (Rt 420) $4.97 $5.82 $0.83 $10.81 $11.83 $0.99C Gordon Blvd (Rt 123) Franconia-Springfield Pkwy (Rt 7900) $1.37 $8.19 $1.37 $9.74 $19.43 $1.62D Garrisonville Rd (Rt 610) Gordon Blvd (Rt 123) $2.00 $12.03 $2.01 $14.22 $33.30 $4.76E Mountain View Rd (Rt 627) Garrisonville Rd (Rt 610) $0.93 $0.92 $0.92 $1.10 $1.09 $1.09F Massaponax Mountain View Rd (Rt 627) $1.34 $1.35 $1.35 $1.59 $1.60 $1.60

HOT PAY TRAFFICA Seminary Rd (Rt 420) Eads Street/Pentagon 6,468 6,701 5,494 5,119 5,162 5,859B Franconia-Springfield Pkwy (Rt 7900) Seminary Rd (Rt 420) 5,167 6,883 5,549 3,815 5,583 6,142C Gordon Blvd (Rt 123) Franconia-Springfield Pkwy (Rt 7900) 8,692 5,100 6,263 5,950 3,614 7,259D Garrisonville Rd (Rt 610) Gordon Blvd (Rt 123) 5,931 3,375 4,320 4,231 2,622 4,957E Mountain View Rd (Rt 627) Garrisonville Rd (Rt 610) 6,162 3,527 3,598 6,003 4,303 4,517F Massaponax Mountain View Rd (Rt 627) 5,205 2,113 2,249 6,312 3,118 2,739

HOT ALL TRAFFICA Seminary Rd (Rt 420) Eads Street/Pentagon 11,756 11,252 6,376 11,067 10,314 6,839B Franconia-Springfield Pkwy (Rt 7900) Seminary Rd (Rt 420) 10,370 11,405 6,606 9,807 10,736 7,332C Gordon Blvd (Rt 123) Franconia-Springfield Pkwy (Rt 7900) 13,061 9,068 7,527 11,118 8,246 8,740D Garrisonville Rd (Rt 610) Gordon Blvd (Rt 123) 7,966 5,538 5,339 6,735 5,258 6,182E Mountain View Rd (Rt 627) Garrisonville Rd (Rt 610) 7,207 4,805 4,444 7,315 5,915 5,534F Massaponax Mountain View Rd (Rt 627) 5,727 2,478 2,795 6,954 3,557 3,366

HOT LANE USAGE (V/C)A Seminary Rd (Rt 420) Eads Street/Pentagon 79.3% 77.5% 43.9% 74.7% 71.0% 47.1%B Franconia-Springfield Pkwy (Rt 7900) Seminary Rd (Rt 420) 69.7% 76.4% 44.3% 65.9% 71.9% 49.1%C Gordon Blvd (Rt 123) Franconia-Springfield Pkwy (Rt 7900) 76.0% 51.4% 42.7% 64.7% 46.7% 49.5%D Garrisonville Rd (Rt 610) Gordon Blvd (Rt 123) 63.8% 40.3% 38.8% 54.0% 38.2% 44.9%E Mountain View Rd (Rt 627) Garrisonville Rd (Rt 610) 60.1% 40.3% 37.3% 61.0% 49.6% 46.4%F Massaponax Mountain View Rd (Rt 627) 49.6% 21.5% 24.2% 60.3% 30.8% 29.1%

HOT LANE SPEED (mph) - segment averageA Seminary Rd (Rt 420) Eads Street/Pentagon 57.2 59.5 63.4 59.5 61.4 62.1B Franconia-Springfield Pkwy (Rt 7900) Seminary Rd (Rt 420) 62.5 61.0 63.8 63.3 62.3 62.7C Gordon Blvd (Rt 123) Franconia-Springfield Pkwy (Rt 7900) 60.4 64.6 63.8 63.5 64.7 61.2D Garrisonville Rd (Rt 610) Gordon Blvd (Rt 123) 63.2 64.8 64.0 64.3 64.8 62.5E Mountain View Rd (Rt 627) Garrisonville Rd (Rt 610) 64.4 65.1 64.7 64.5 65.0 63.5F Massaponax Mountain View Rd (Rt 627) 64.5 65.1 65.0 63.2 65.1 64.9

GP TRAFFIC (in direction of operating HOT lanes only)A Seminary Rd (Rt 420) Eads Street/Pentagon 20,456 19,159 8,594 21,695 20,484 8,803B Franconia-Springfield Pkwy (Rt 7900) Seminary Rd (Rt 420) 19,327 21,479 9,657 21,068 22,740 10,114C Gordon Blvd (Rt 123) Franconia-Springfield Pkwy (Rt 7900) 19,047 22,200 9,877 24,565 25,824 11,019D Garrisonville Rd (Rt 610) Gordon Blvd (Rt 123) 13,387 16,683 8,645 17,333 19,444 9,757E Mountain View Rd (Rt 627) Garrisonville Rd (Rt 610) 11,842 12,964 7,600 16,446 16,043 9,039F Massaponax Mountain View Rd (Rt 627) 10,386 14,593 7,184 14,218 18,115 8,929

106

I-95/I-395 HOT Lane

Appendix E

Cumulative Travel Times – 55mph throughput

2015 AM Peak (northbound)

0

20

40

60

80

100

120

140

26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1

Location (not to scale)

Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes

2015 PM Peak (southbound)

0

20

40

60

80

100

120

140

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes


0

20

40

60

80

100

120

140

26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes

2015 Off-Peak (southbound)

0

20

40

60

80

100

120

140

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes


0

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes


0

20

40

60

80

100

120

140

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes

Note: Locations are segment numbers as in Figure 8.1

107

I-95/I-395 HOT Lane

Appendix E

Transactions – 55mph throughput

Year Annual Unramped Transaction (000's)

Segment A Segment B Segment C Segment D Segment E Segment F Total

2012 17,480 15,340 11,820 18,180 - - 62,820 2013 17,440 15,340 11,790 18,050 - - 62,620 2014 17,400 15,340 11,760 17,920 - - 62,420 2015 17,210 15,230 11,660 17,140 2,650 10,140 74,030 2016 17,170 15,230 11,630 17,010 2,730 10,320 74,090 2017 17,120 15,220 11,610 16,880 2,810 10,500 74,140 2018 17,080 15,220 11,580 16,750 2,890 10,670 74,190 2019 17,030 15,220 11,560 16,620 2,970 10,850 74,250 2020 16,990 15,220 11,530 16,490 3,050 11,030 74,310 2021 16,950 15,210 11,510 16,360 3,130 11,210 74,370 2022 16,900 15,210 11,480 16,230 3,210 11,390 74,420 2023 16,860 15,210 11,460 16,110 3,290 11,560 74,490 2024 16,810 15,210 11,430 15,980 3,370 11,740 74,540 2025 16,770 15,200 11,410 15,850 3,450 11,920 74,600 2026 16,730 15,200 11,380 15,720 3,530 12,100 74,660 2027 16,680 15,200 11,360 15,590 3,610 12,280 74,720 2028 16,640 15,200 11,330 15,460 3,690 12,450 74,770 2029 16,590 15,190 11,310 15,330 3,770 12,630 74,820 2030 16,550 15,190 11,280 15,200 3,850 12,810 74,880 2031 16,950 15,560 11,550 15,570 3,940 13,120 76,690 2032 17,340 15,920 11,820 15,930 4,030 13,420 78,460 2033 17,720 16,270 12,080 16,280 4,120 13,720 80,190 2034 18,090 16,610 12,330 16,620 4,210 14,010 81,870 2035 18,450 16,940 12,580 16,950 4,290 14,290 83,500 2036 18,800 17,260 12,820 17,270 4,370 14,560 85,080 2037 19,140 17,570 13,050 17,580 4,450 14,820 86,610 2038 19,460 17,870 13,270 17,880 4,530 15,070 88,080 2039 19,770 18,160 13,480 18,170 4,600 15,310 89,490 2040 20,070 18,430 13,680 18,440 4,670 15,540 90,830 2041 20,350 18,690 13,870 18,700 4,730 15,760 92,100 2042 20,580 18,900 14,030 18,910 4,780 15,940 93,140 2043 20,790 19,100 14,180 19,110 4,830 16,110 94,120 2044 20,990 19,280 14,310 19,290 4,880 16,260 95,010 2045 21,170 19,450 14,430 19,460 4,920 16,400 95,830 2046 21,350 19,620 14,550 19,630 4,960 16,540 96,650 2047 21,530 19,790 14,670 19,800 5,000 16,680 97,470 2048 21,700 19,950 14,790 19,960 5,040 16,820 98,260 2049 21,870 20,110 14,910 20,120 5,080 16,950 99,040 2050 22,030 20,250 15,020 20,260 5,120 17,070 99,750 2051 22,180 20,390 15,120 20,400 5,150 17,190 100,430 2052 22,330 20,530 15,220 20,540 5,180 17,300 101,100 2053 22,470 20,660 15,320 20,670 5,210 17,410 101,740 2054 22,610 20,790 15,410 20,800 5,240 17,520 102,370 2055 22,750 20,920 15,510 20,930 5,270 17,630 103,010 2056 22,890 21,050 15,610 21,060 5,300 17,740 103,650 2057 23,030 21,180 15,710 21,190 5,330 17,850 104,290 2058 23,170 21,310 15,810 21,320 5,360 17,960 104,930 2059 23,320 21,440 15,910 21,450 5,390 18,070 105,580 2060 23,470 21,580 16,010 21,590 5,420 18,180 106,250 2061 23,610 21,710 16,100 21,720 5,450 18,290 106,880 2062 23,750 21,840 16,200 21,850 5,480 18,400 107,520 2063 23,890 21,970 16,300 21,980 5,510 18,510 108,160 2064 24,030 22,100 16,400 22,110 5,540 18,620 108,800 2065 24,170 22,230 16,500 22,240 5,570 18,730 109,440 2066 24,310 22,360 16,600 22,370 5,600 18,840 110,080 2067 24,440 22,480 16,690 22,490 5,630 18,940 110,670 2068 24,560 22,590 16,770 22,600 5,660 19,040 111,220 2069 24,680 22,700 16,850 22,710 5,690 19,140 111,770 2070 24,800 22,810 16,930 22,820 5,720 19,230 112,310 2071 24,920 22,920 17,010 22,930 5,750 19,330 112,860

Note: Transactions estimated from traffic entering and leaving the HOT lanes via entry/exit points

108

I-95/I-395 HOT Lane

Appendix E





109

I-95/I-395 HOT Lane

Appendix E





110

I-95/I-395 HOT Lane

Appendix E





111

I-95/I-395 HOT Lane

Appendix E










112

I-95/I-395 HOT Lane

Appendix E



0

20

40

60

80

100

120

140

26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes


0

20

40

60

80

100

120

140

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes


0

20

40

60

80

100

120

140

26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes


0

20

40

60

80

100

120

140

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes


0

20

40

60

80

100

120

140

160

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes


0

20

40

60

80

100

120

140

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes


113

I-95/I-395 HOT Lane

Appendix E





114

I-95/I-395 HOT Lane

Appendix E





115

I-95/I-395 HOT Lane

Appendix E





116

I-95/I-395 HOT Lane

Appendix E





HOT PAY TRAFFICA Seminary Rd (Rt 420) Eads Street/Pentagon 2,213 2,509 4,235 1,347 1,436 3,982B Franconia-Springfield Pkwy (Rt 7900) Seminary Rd (Rt 420) 1,608 2,447 3,981 865 1,525 3,754C Gordon Blvd (Rt 123) Franconia-Springfield Pkwy (Rt 7900) 3,385 1,703 3,792 1,514 879 3,636D Garrisonville Rd (Rt 610) Gordon Blvd (Rt 123) 1,927 1,292 2,387 1,137 690 1,939E Mountain View Rd (Rt 627) Garrisonville Rd (Rt 610) 2,675 2,223 2,388 1,802 2,104 1,953F Massaponax Mountain View Rd (Rt 627) 3,027 1,477 1,401 2,404 1,925 1,180





117

I-95/I-395 HOT Lane

Appendix E



0

20

40

60

80

100

120

140

26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes


0

20

40

60

80

100

120

140

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes


0

20

40

60

80

100

120

140

26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes


0

20

40

60

80

100

120

140

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes


0

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes


0

20

40

60

80

100

120

140

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes


118

I-95/I-395 HOT Lane

Appendix E

Revenue Stream – Revenue Optimal Toll Rates – ALL

Year Unramped HOT Traffic Discounted Revenue optimised, 6 segmentsRevenue Growth & Ramped



119

I-95/I-395 HOT Lane

Appendix E

Revenue Stream – Revenue Optimal Toll Rates – NORTHERN SECTION




120

I-95/I-395 HOT Lane

Appendix E

Revenue Stream – Revenue Optimal Toll Rates – SOUTHERN SECTION




121

I-95/I-395 HOT Lane

Appendix E

Segment Summary – Revenue Optimal Toll Rates









122

I-95/I-395 HOT Lane

Appendix E

Cumulative Travel Times – Revenue Optimal Toll Rates


0

20

40

60

80

100

120

140

26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes


0

20

40

60

80

100

120

140

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes


0

20

40

60

80

100

120

140

26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes


0

20

40

60

80

100

120

140

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes


0

20

40

60

80

100

120

140

160

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes


0

20

40

60

80

100

120

140

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes


123

I-95/I-395 HOT Lane

Appendix E

Transactions – Revenue Optimal Toll Rates

Year Annual Unramped Transaction (000's)

Segment A Segment B Segment C Segment D Segment E Segment F Total

2012 15,710 13,860 10,630 15,770 - - 55,970 2013 15,790 13,990 10,770 15,970 - - 56,520 2014 15,870 14,120 10,910 16,170 - - 57,070 2015 15,870 14,080 10,900 15,280 2,450 8,040 66,620 2016 15,950 14,210 11,040 15,470 2,540 8,260 67,470 2017 16,040 14,340 11,180 15,660 2,640 8,480 68,340 2018 16,120 14,480 11,330 15,840 2,730 8,700 69,200 2019 16,200 14,610 11,470 16,030 2,830 8,910 70,050 2020 16,290 14,740 11,610 16,220 2,920 9,130 70,910 2021 16,370 14,870 11,750 16,410 3,020 9,350 71,770 2022 16,450 15,000 11,890 16,600 3,110 9,570 72,620 2023 16,540 15,140 12,040 16,780 3,210 9,790 73,500 2024 16,620 15,270 12,180 16,970 3,300 10,010 74,350 2025 16,700 15,400 12,320 17,160 3,400 10,230 75,210 2026 16,790 15,530 12,460 17,350 3,490 10,450 76,070 2027 16,870 15,660 12,600 17,540 3,590 10,660 76,920 2028 16,950 15,800 12,750 17,720 3,680 10,880 77,780 2029 17,040 15,930 12,890 17,910 3,780 11,100 78,650 2030 17,120 16,060 13,030 18,100 3,870 11,320 79,500 2031 17,570 16,480 13,370 18,570 3,970 11,620 81,580 2032 18,000 16,880 13,700 19,020 4,070 11,900 83,570 2033 18,410 17,260 14,010 19,450 4,160 12,170 85,460 2034 18,810 17,640 14,320 19,870 4,250 12,440 87,330 2035 19,190 17,990 14,610 20,270 4,330 12,690 89,080 2036 19,540 18,320 14,880 20,640 4,410 12,920 90,710 2037 19,890 18,640 15,140 21,010 4,490 13,150 92,320 2038 20,200 18,930 15,380 21,340 4,560 13,360 93,770 2039 20,500 19,210 15,610 21,660 4,630 13,560 95,170 2040 20,770 19,460 15,810 21,940 4,690 13,740 96,410 2041 21,030 19,700 16,000 22,210 4,750 13,910 97,600 2042 21,270 19,930 16,190 22,470 4,810 14,070 98,740 2043 21,500 20,140 16,360 22,710 4,860 14,220 99,790 2044 21,710 20,340 16,520 22,930 4,910 14,360 100,770 2045 21,890 20,510 16,660 23,120 4,950 14,480 101,610 2046 22,060 20,670 16,790 23,300 4,990 14,590 102,400 2047 22,210 20,810 16,910 23,460 5,020 14,690 103,100 2048 22,350 20,940 17,020 23,610 5,050 14,780 103,750 2049 22,490 21,070 17,130 23,760 5,080 14,870 104,400 2050 22,630 21,200 17,240 23,910 5,110 14,960 105,050 2051 22,780 21,340 17,350 24,060 5,140 15,060 105,730 2052 22,930 21,480 17,460 24,210 5,170 15,160 106,410 2053 23,070 21,610 17,570 24,360 5,200 15,250 107,060 2054 23,210 21,740 17,680 24,510 5,230 15,340 107,710 2055 23,350 21,870 17,780 24,650 5,260 15,430 108,340 2056 23,480 22,000 17,880 24,790 5,290 15,520 108,960 2057 23,620 22,130 17,980 24,930 5,320 15,610 109,590 2058 23,760 22,260 18,080 25,070 5,350 15,700 110,220 2059 23,900 22,390 18,190 25,220 5,380 15,790 110,870 2060 24,040 22,520 18,300 25,370 5,410 15,880 111,520 2061 24,180 22,650 18,410 25,520 5,440 15,970 112,170 2062 24,320 22,780 18,520 25,670 5,470 16,060 112,820 2063 24,460 22,910 18,630 25,820 5,500 16,160 113,480 2064 24,610 23,050 18,740 25,970 5,530 16,260 114,160 2065 24,760 23,190 18,850 26,120 5,560 16,360 114,840 2066 24,910 23,330 18,960 26,280 5,590 16,460 115,530 2067 25,050 23,460 19,060 26,430 5,620 16,550 116,170 2068 25,190 23,590 19,160 26,570 5,650 16,640 116,800 2069 25,330 23,720 19,260 26,710 5,680 16,730 117,430 2070 25,470 23,850 19,360 26,850 5,710 16,820 118,060 2071 25,600 23,980 19,460 26,990 5,740 16,910 118,680

Note: Transactions estimated from traffic entering and leaving the HOT lanes via entry/exit points

124

I-95/I-395 HOT Lane

Appendix E

Revenue Stream – Toll Schedule v1 – ALL

Year Unramped HOT Traffic Discounted Toll Schedule v1, 6 segmentsRevenue Growth & Ramped



125

I-95/I-395 HOT Lane

Appendix E

Revenue Stream – Toll Schedule v1 – NORTHERN SECTION




126

I-95/I-395 HOT Lane

Appendix E

Revenue Stream – Toll Schedule v1 – SOUTHERN SECTION




127

I-95/I-395 HOT Lane

Appendix E

Segment Summary – Toll Schedule v1









128

I-95/I-395 HOT Lane

Appendix E

Cumulative Travel Times – Toll Schedule v1


0

20

40

60

80

100

120

140

26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes


0

20

40

60

80

100

120

140

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes


0

20

40

60

80

100

120

140

26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes


0

20

40

60

80

100

120

140

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes


0

20

40

60

80

100

120

140

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes


0

20

40

60

80

100

120

140

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26


Cum

ulat

ive

Jour

ney

Tim

es (m

ins)

GP Lanes HOT Lanes


129

I-95/I-395 HOT Lane

Appendix F

Appendix F – Sensitivity Tests – Detailed Results

130

I-95/I-395 HOT Lane

Appendix F

Sensitivity Tests – Detailed Results

This Appendix contains detailed results from the six sensitivity tests carried out, including the options within each sensitivity. A revenue stream is provided for each test, including separate streams for the northern and southern sectors.

Sensitivity Tests

• Test 1a – Low Background Growth (-2yrs 2015) (-5yrs 2030) • Test 1b – High Background Growth (+2yrs 2015) (+5yrs 2030) • Test 2a – Low Ramp Up • Test 2b – High Ramp Up • Test 3a – Change VOC (+13%) Fuel (+20%) • Test 3b – Change VOC (-13%) Fuel (-20%) • Test 4a – Change VOT (-20%) • Test 4b – Change VOT (+20%) • Test 5a – Slugs/HOV3+/Transit • Test 5b – Slugs/HOV3+/Transit • Test 6a – Annual Expansion Factor – Low • Test 6b – Annual Expansion Factor – Medium • Test 6c – Annual Expansion Factor – High

131

I-95/I-395 HOT Lane

Appendix F

Revenue Stream – Test 1a – ALL

Year Unramped HOT Traffic Discounted Toll Schedule v1, 6 segments. LOW background growthRevenue Growth & Ramped



132

I-95/I-395 HOT Lane

Appendix F

Revenue Stream – Test 1a – NORTHERN SECTION




133

I-95/I-395 HOT Lane

Appendix F

Revenue Stream – Test 1a – SOUTHERN SECTION




134

I-95/I-395 HOT Lane

Appendix F

Revenue Stream – Test 1b – ALL

Year Unramped HOT Traffic Discounted Toll Schedule v1, 6 segments. HIGH background growthRevenue Growth & Ramped



135

I-95/I-395 HOT Lane

Appendix F

Revenue Stream – Test 1b – NORTHERN SECTION




136

I-95/I-395 HOT Lane

Appendix F

Revenue Stream – Test 1b – SOUTHERN SECTION




137

I-95/I-395 HOT Lane

Appendix F


Year Unramped HOT Traffic Discounted Toll Schedule v1, 6 segments - LOW ramp-upRevenue Growth & Ramped


2012 71.6 30.4 - Growth from 2015 to 2030 interpolated from model-year results2013 76.8 34.9 - Growth rate beyond 2030 assumed decreasing 0.1% per annum to 1%2014 81.9 39.1 - Revenue growth is capped by capacity of individual HOT lane sections2015 103.3 50.6 - Ramp-up of revenue assumed: 60% year 12016 109.5 4.0% 54.2 70% year 22017 115.6 3.8% 52.8 80% year 32018 121.8 3.7% 51.2 90% year 42019 128.0 3.6% 49.6 100% year 5 onwards2020 134.1 3.4% 47.7 Adjusted for Southern Section: 86% year 12021 140.3 3.3% 45.8 90% year 22022 146.5 3.2% 43.8 93% year 32023 152.7 3.1% 41.9 97% year 42024 158.8 3.0% 40.0 100% year 5 onwards2025 165.0 2.9% 38.1 - Model revenue 2005 prices increased to 2008 prices using CPI-U2026 171.2 2.9% 36.3 194.4 : 2005 Jan-Sep2027 177.3 2.8% 34.5 216.0 : 2008 Jan-Sep2028 183.5 2.7% 32.7 11.1% : 2005 Jan-Sep to 2008 Jan-Sep2029 189.7 2.6% 31.0 - Discount rate to 2008 prices : 9%2030 195.8 2.6% 29.4 - 2015 & 2030 Value of Time escalation based on growth in GDP2031 200.5 2.5% 27.6 0.75 x forecast increase in GDP per capita:2032 205.0 2.4% 25.9 1.0952 : 2005 to 20152033 209.2 2.3% 24.3 1.2989 : 2005 to 20302034 213.1 2.2% 22.7 2035 217.0 2.1% 21.2 2036 220.6 2.0% 19.8 2037 223.9 1.9% 18.4 2038 227.0 1.8% 17.1 2039 229.8 1.7% 15.9 2040 232.6 1.6% 14.8 2041 235.0 1.5% 13.7 2042 237.4 1.4% 12.7 2043 239.6 1.3% 11.7 2044 241.6 1.2% 10.9 2045 243.4 1.1% 10.0 2046 245.1 1.0% 9.3 2047 246.8 1.0% 8.6 2048 248.5 1.0% 7.9 2049 250.2 1.0% 7.3 2050 251.9 1.0% 6.7 2051 253.4 1.0% 6.2 2052 255.0 1.0% 5.8 2053 256.5 1.0% 5.3 2054 258.1 1.0% 4.9 2055 259.7 1.0% 4.5 2056 261.2 1.0% 4.2 2057 262.7 1.0% 3.9 2058 264.2 1.0% 3.6 2059 265.8 1.0% 3.3 2060 267.2 1.0% 3.0 2061 268.6 1.0% 2.8 2062 269.9 1.0% 2.6 2063 271.2 1.0% 2.4 2064 272.4 1.0% 2.2 2065 273.7 1.0% 2.0 2066 274.9 1.0% 1.9 2067 276.2 1.0% 1.7 2068 277.4 1.0% 1.6 2069 278.7 1.0% 1.5 2070 279.9 1.0% 1.3 2071 281.1 1.0% 1.2 Total 12,869.2 1,156.1

138

I-95/I-395 HOT Lane

Appendix F





139

I-95/I-395 HOT Lane

Appendix F




2012 - - - Growth from 2015 to 2030 interpolated from model-year results2013 - - - Growth rate beyond 2030 assumed decreasing 0.1% per annum to 1%2014 - - - Revenue growth is capped by capacity of individual HOT lane sections2015 13.8 6.5 - Ramp-up of revenue assumed: 60% year 12016 14.6 4.0% 6.6 70% year 22017 15.5 3.8% 6.7 80% year 32018 16.4 3.7% 6.7 90% year 42019 17.3 3.6% 6.7 100% year 5 onwards2020 18.2 3.4% 6.5 Adjusted for Southern Section: 86% year 12021 19.1 3.3% 6.2 90% year 22022 19.9 3.2% 6.0 93% year 32023 20.8 3.1% 5.7 97% year 42024 21.7 3.0% 5.5 100% year 5 onwards2025 22.6 2.9% 5.2 - Model revenue 2005 prices increased to 2008 prices using CPI-U2026 23.5 2.9% 5.0 194.4 : 2005 Jan-Sep2027 24.4 2.8% 4.7 216.0 : 2008 Jan-Sep2028 25.3 2.7% 4.5 11.1% : 2005 Jan-Sep to 2008 Jan-Sep2029 26.1 2.6% 4.3 - Discount rate to 2008 prices : 9%2030 27.0 2.6% 4.1 - 2015 & 2030 Value of Time escalation based on growth in GDP2031 27.7 2.5% 3.8 0.75 x forecast increase in GDP per capita:2032 28.3 2.4% 3.6 1.0952 : 2005 to 20152033 28.9 2.3% 3.4 1.2989 : 2005 to 20302034 29.5 2.2% 3.1 2035 30.1 2.1% 2.9 2036 30.6 2.0% 2.7 2037 31.2 1.9% 2.6 2038 31.7 1.8% 2.4 2039 32.2 1.7% 2.2 2040 32.6 1.6% 2.1 2041 33.0 1.5% 1.9 2042 33.4 1.4% 1.8 2043 33.8 1.3% 1.7 2044 34.2 1.2% 1.5 2045 34.5 1.1% 1.4 2046 34.8 1.0% 1.3 2047 35.1 1.0% 1.2 2048 35.4 1.0% 1.1 2049 35.7 1.0% 1.0 2050 36.0 1.0% 1.0 2051 36.3 1.0% 0.9 2052 36.6 1.0% 0.8 2053 36.9 1.0% 0.8 2054 37.2 1.0% 0.7 2055 37.5 1.0% 0.7 2056 37.8 1.0% 0.6 2057 38.2 1.0% 0.6 2058 38.5 1.0% 0.5 2059 38.8 1.0% 0.5 2060 39.2 1.0% 0.4 2061 39.5 1.0% 0.4 2062 39.7 1.0% 0.4 2063 40.0 1.0% 0.3 2064 40.3 1.0% 0.3 2065 40.6 1.0% 0.3 2066 40.8 1.0% 0.3 2067 41.1 1.0% 0.3 2068 41.4 1.0% 0.2 2069 41.7 1.0% 0.2 2070 42.0 1.0% 0.2 2071 42.3 1.0% 0.2 Total 1,801.2 143.2

140

I-95/I-395 HOT Lane

Appendix F


Year Unramped HOT Traffic Discounted Toll Schedule v1, 6 segments - HIGH ramp-upRevenue Growth & Ramped



141

I-95/I-395 HOT Lane

Appendix F





142

I-95/I-395 HOT Lane

Appendix F





143

I-95/I-395 HOT Lane

Appendix F


Year Unramped HOT Traffic Discounted Test 3a VOC (+13%) Fuel (+20%), 6 segmentsRevenue Growth & Ramped



144

I-95/I-395 HOT Lane

Appendix F





145

I-95/I-395 HOT Lane

Appendix F





146

I-95/I-395 HOT Lane

Appendix F


Year Unramped HOT Traffic Discounted Test 3b VOC (-13%) Fuel (-20%), 6 segmentsRevenue Growth & Ramped



147

I-95/I-395 HOT Lane

Appendix F





148

I-95/I-395 HOT Lane

Appendix F





149

I-95/I-395 HOT Lane

Appendix F


Year Unramped HOT Traffic Discounted Test 4a VOT (-20%), 6 segmentsRevenue Growth & Ramped



150

I-95/I-395 HOT Lane

Appendix F





151

I-95/I-395 HOT Lane

Appendix F





152

I-95/I-395 HOT Lane

Appendix F


Year Unramped HOT Traffic Discounted Test 4b VOT (+20%), 6 segmentsRevenue Growth & Ramped



153

I-95/I-395 HOT Lane

Appendix F





154

I-95/I-395 HOT Lane

Appendix F





155

I-95/I-395 HOT Lane

Appendix F


Year Unramped HOT Traffic Discounted Toll Schedule v1, 6 segments - Test 5aRevenue Growth & Ramped



156

I-95/I-395 HOT Lane

Appendix F





157

I-95/I-395 HOT Lane

Appendix F





158

I-95/I-395 HOT Lane

Appendix F


Year Unramped HOT Traffic Discounted Toll Schedule v1, 6 segments - Test 5bRevenue Growth & Ramped



159

I-95/I-395 HOT Lane

Appendix F





160

I-95/I-395 HOT Lane

Appendix F





161

I-95/I-395 HOT Lane

Appendix F


Year Unramped HOT Traffic Discounted Toll Schedule v1, 6 seg - sensitivity - annualisation = 290Revenue Growth & Ramped



162

I-95/I-395 HOT Lane

Appendix F





163

I-95/I-395 HOT Lane

Appendix F





164

I-95/I-395 HOT Lane

Appendix F





165

I-95/I-395 HOT Lane

Appendix F





166

I-95/I-395 HOT Lane

Appendix F





167

I-95/I-395 HOT Lane

Appendix F

Revenue Stream – Test 6c – ALL




168

I-95/I-395 HOT Lane

Appendix F

Revenue Stream – Test 6c – NORTHERN SECTION




169

I-95/I-395 HOT Lane

Appendix F

Revenue Stream – Test 6c – SOUTHERN SECTION




170

I-95/I-395 HOT Lane

Appendix G

Appendix G – Sensitivity of Traffic Model Forecasts to Fuel Prices

171

I-95/I-395 HOT Lane

Appendix G

Sensitivity of Traffic Model Forecasts to Fuel Prices

(1) Fuel Price Projections

Energy Information Administration (EIA) provides the official energy statistics from the US Government. In a June 2008 report their 2008 to 2030 projections were as follows:

Retail prices for petroleum products largely follow changes in crude oil prices. In the AEO2008 reference case, the world oil price path reaches a low of $57 per barrel in 2016 and then increases to about $70 in 2030 (2006 dollars). The U.S. average motor gasoline price follows the same trend, falling to $2.19 per gallon in 2016 before rising to $2.45 in 2030.

In the high price case, with the price of imported crude oil rising to $119 per barrel (2006 dollars) in 2030, the average price of U.S. motor gasoline increases rapidly, to $3.06 per gallon in 2016 and $3.52 per gallon in 2030. In the low price case, gasoline prices decline to a low of $1.74 per gallon in 2016, increase slowly through the early 2020s, and level off at about $1.84 per gallon through 2030 (Figure 92).

Because changes from the reference case assumptions for economic growth rates have less pronounced effects on motor gasoline prices than do changes in oil price assumptions, the average prices for U.S. motor gasoline in the high and low economic growth cases are close to those in the reference case. In the high growth case, the average gasoline price falls to a low of $2.24 per gallon in 2016 and then rises to $2.59 per gallon in 2030. In the low growth case, the average price reaches a low of $2.16 per gallon in 2017, followed by an increase to $2.32 per gallon in 2030.

172

I-95/I-395 HOT Lane

Appendix G

In November 2008, EIA revised their 2030 oil price forecast to $120 per barrel ($2007) when oil prices were around $50 per barrel. In December 2008, the EIA reissued their 2009 oil and gasoline forecasts at $51 per barrel and $2.03 per gallon (regular-grade gasoline).

In November 2008, respected industry analyst Charlie Maxwell (Weeden & Co.) predicted short-term oil prices in the $40 per barrel range, remaining flat and then rising to $70. However, two months earlier he was calling for short term prices in the $75-$115 range, rising to $250 per barrel by 2015.

The above discussion highlights the current uncertainty and fluctuations in oil and fuel price forecasts. However, it would appear that there is consensus that prices are likely to rise in the future. Assuming a $70 per barrel in 2010 the current IEA 2030 forecast of $120 per barrel represents a 70% increase (or approx. 40% increase by 2020 based on interpolation).

The June 2007 Energy Bill passed by the US Senate requires average fuel economy of 35 mpg for new cars, pickup trucks and SUV’s by 2020, representing a 40% improvement in fuel efficiency over 2008 levels (automakers are currently required to meet an average of 27.5mpg for cars and 22.2 mpg for SUV’s and small trucks).

In summary, the future of fuel prices is uncertain, but it would seem prudent to assume that fuel prices could increase in real terms in the future. This will be offset somewhat by improved fuel efficiency technology. Due to the uncertainty surrounding future fuel prices and the impacts of fuel efficiency, we recommend a central assumption that vehicle operating costs (VOC) will remain constant in real terms. Note that for modeling purposes, the variable VOC is used, which includes fuel, oil, maintenance and tires. In our risk analysis we will examine low and high VOC scenarios of ±13% around the central assumption (this represents a 20% change in fuel costs). In our view, both an upside and downside VOC assumption appears prudent in the current market.

(2) Modeling the Impact of Fuel Price Increases

The future year trip matrices in the traffic model are based on factoring base year matrices with the regional model’s trip generation rates and then using a Fratar process to redistribute the future year trips. This process effectively assumes minimum change to current travel conditions (allowing for projected development growth) and no account is taken of any changes to specific travel costs arising from new infrastructure or more generally from events such as real increases in fuel prices.

173

I-95/I-395 HOT Lane

Appendix G

The simplest method for undertaking a test to model the impact of fuel price increases is through an elasticity approach, whereby each cell in the future year trip matrix is adjusted as follows:

E

ijBASE

ijNEWijBASEijNEW C

CTT

×=

Where TijNEW is the trip total from i to j under the new fuel prices, TijBASE is the trip total under the current base year fuel price CijNEW is the new fuel price from i to j CijBASE is the base year fuel price E is the elasticity of trips to fuel prices

Note that CijNEW and CijBASE are not the total cost of travel but only the fuel price component of this cost.

Values of ‘E’ have been determined by many studies. Most of these values seem to broadly agree around the following projections from Phil Goodwin, Joyce Dargay and Mark Hanly in “Review of Income and Price Elasticities in the Demand for Road Traffic”. They project that following a 10% real increase in fuel costs:

• Trips fall by 1% in short term and 3% in the longer term (after 5 years) • Demand for fuel falls by 2.5% in short term and 6% in longer term • Fuel efficiency increases by 1.5% in short term and 4% in longer term • Car ownership falls by <1% in short term and 2.5% in longer term.

Using this elasticity approach, the key result in terms of the sensitivity test is the fall in trips. An appropriate implied elasticity of trips is -0.3 against base fuel price conditions.

(3) Fuel Price Sensitivity Test

We have modeled the impact on trip lengths and trip totals using an elasticity model approach, with an assumed fuel price elasticity of -0.3 in a range of higher fuel prices of up to ±20%. The change in fuel type is covered by the projections from the Energy Information Administration.

Another issue also arises when considering the vehicle operating cost (VOC) sensitivity tests. Specifically, how would any affect on trip lengths and/or number of trips brought about by a change in fuel type be captured? For example, by 2030, increased use of fuel additives and/or alternative fuel would change the cost structure of VOC. In doing this, it is useful to

174

I-95/I-395 HOT Lane

Appendix G

consider how the upper and lower limits for VOC sensitivities compare with the range for values of time (VOT) in northern Virginia.

Future changes in VOT reflect some function of real increases in GDP per capita. Typically, economists tend to assume that there is a relationship between these two with the increase in VOT put at around 0.5 to 1.0 times the real increase in GDP/capita. An elasticity of VOT to GDP/capita of 0.75 has been assumed for this study. Virginia’s population is projected to increase at close to 1% pa (2010-2030) and assuming real GDP growth from 2010 is around 2.5% pa our projections for GDP/capita in northern Virginia are for a real increase of approximately 30% to 2030. On the basis of a 0.75 elasticity of VOT to GDP/capita, the likely real increase in VOT is approximately 20% (2005 to 2015) and 45% (2005 to 2030).

175

I-95/I-395 HOT Lane

Appendix H

Appendix H – Risk Analysis methodologies

176

I-95/I-395 HOT Lane

Appendix H

Risk Analysis methodologies

Risk Analysis

All forecasts are inherently subject to degree of uncertainty, related to which a level of risk can be associated with the likelihood that a forecast will be met. The risks associated with traffic forecasts derived from a traffic model (and their associated toll revenue estimates) can be ascribed to three main sources of risk:

• Independent assumptions input used in the traffic models, or factors that are directly applied to the model generated traffic or revenue estimates (such as assumptions for land-use developments, changes to other transport infrastructure or transport policies, background traffic growth, ramp-up profiles, etc);

• Processes or parameters within the traffic models which in turn affect their outputs (such as methodological and data processing issues, choice of model parameters, modelling errors, etc);

• Data from which the initial base year models are derived (such as potential data omissions, low sampling rates or other inadequacies, actual data errors, etc).

It is perhaps arguable whether it is possible to prepare a rigorously quantified risk analysis for traffic forecasts, as the range of assumptions included within the preparation of many forecasts can be so extensive, and the distribution of risk around each of these factors so little understood, that any such results could well appear (and, in fact, be) spurious in their accuracy.

However, it is often the case that many of the input assumptions used in the traffic forecasting process have little effect on the resultant forecasts and can largely be ignored from the risk process, and that the distribution of risk around the key issues is often generally better understood than the equivalent distributions around the more peripheral issues. Nevertheless, there is clearly a strong imperative to understand the likely relative levels of risk associated with such variables and the risk analysis process aims to do this. Note that there still remains the need for some degree of caution in the interpretation and use of the results that are obtained from the risk analysis process.

Methodologies

Two approaches are available to incorporate these risks into the traffic forecasts:

• Deterministic scenarios; and • The Monte Carlo approach.

Both approaches aim to give a range, rather than a single revenue line.

177

I-95/I-395 HOT Lane

Appendix H

Scenario-based approach

Deterministic scenarios take a series of input assumptions with a given theme to calculate revenue that would be expected under a particular ‘case’. For instance, an ‘aggressive’ case, would simply use more optimistic values for all input assumptions, while an alternative more pessimistic forecast would take conservative input assumptions.

The advantage of this approach is the transparency of the input assumptions, and results show a given outcome for a given set of inputs. Since each input assumption is explicitly defined it is possible to avoid double counting of risk where inputs are correlated. Several scenarios can be created with varying degrees of optimism/conservatism in the inputs.

A key drawback of this approach is the increased difficulty in assigning probabilities that each scenario may occur. In addition, each scenario still results in a single revenue line, which implies a certainty of the revenues in each scenario that is not necessarily possible. Many scenarios can be created using this approach, but in doing so tends towards the Monte Carlo approach (see below).

Monte Carlo Approach

This approach attempts to incorporate all the potential sources of error in the forecast at once, resulting in thresholds with given probability that out-turn revenue will exceed this level. The revenue impacts of each input are taken individually, and by allocating distributions to each input it is possible to combine the impacts of thousands of input permutations with the probability of each of these permutations. The overall probability distribution of revenue forecasts is thus generated.

The probability distribution is popular with lenders as they can decide to lend money based on revenues which have a (say) 90% probability of being equalled or exceeded. It allows the risk on all the input assumptions to be assessed simultaneously, including risks not explicitly included in the model such as the count data and model robustness.

However, it is possible to double count risk if inputs are correlated and so it is necessary to ensure each input is tested in isolation to produce the effect of the input variables. This is the inherent reasoning behind the sensitivity testing process carried out. Furthermore, the overall probability distribution of the revenues is heavily dependent on the probability distribution of each input, and it is unlikely this will be known with great certainty itself.

The Monte Carol approach has been used in the risk analysis of the traffic and revenue forecasts in this study.

178

I-95/I-395 HOT Lane

Appendix I

Appendix I – 90% & 10% Confidence Intervals – Detailed Results

179

I-95/I-395 HOT Lane

Appendix I

Sensitivity Tests – Detailed Results

This Appendix contains detailed results for 90% and 10% Confidence Intervals. A revenue stream is provided for each, including separate streams for the northern and southern sectors.

180

I-95/I-395 HOT Lane

Appendix I

Revenue Stream – 90% Confidence – ALL

Year Unramped HOT Traffic Discounted Toll Schedule v1, 6 segments - 90% confidenceRevenue Growth & Ramped


2012 61.5 26.1 - Growth from 2015 to 2030 interpolated from model-year results2013 65.9 30.0 - Growth rate beyond 2030 assumed decreasing 0.1% per annum to 1%2014 70.3 33.5 - Revenue growth is capped by capacity of individual HOT lane sections2015 88.7 43.4 - Ramp-up of revenue assumed: 60% year 12016 93.7 3.8% 46.4 70% year 22017 98.7 3.6% 45.0 80% year 32018 103.8 3.5% 43.6 90% year 42019 108.8 3.4% 42.2 100% year 5 onwards2020 113.9 3.3% 40.5 Adjusted for Southern Section: 86% year 12021 118.9 3.2% 38.8 90% year 22022 124.0 3.1% 37.1 93% year 32023 129.0 3.0% 35.4 97% year 42024 134.1 2.9% 33.8 100% year 5 onwards2025 139.1 2.8% 32.1 - Model revenue 2005 prices increased to 2008 prices using CPI-U2026 144.1 2.7% 30.6 194.4 : 2005 Jan-Sep2027 149.2 2.7% 29.0 216.0 : 2008 Jan-Sep2028 154.2 2.6% 27.5 11.1% : 2005 Jan-Sep to 2008 Jan-Sep2029 159.3 2.5% 26.1 - Discount rate to 2008 prices : 9%2030 164.3 2.5% 24.7 - 2015 & 2030 Value of Time escalation based on growth in GDP2031 168.0 2.4% 23.2 0.75 x forecast increase in GDP per capita:2032 171.7 2.3% 21.7 1.0952 : 2005 to 20152033 175.1 2.2% 20.3 1.2989 : 2005 to 20302034 178.3 2.1% 19.0 2035 181.3 2.0% 17.7 2036 184.2 1.9% 16.5 2037 186.8 1.8% 15.3 2038 189.3 1.7% 14.3 2039 191.6 1.6% 13.2 2040 193.7 1.5% 12.3 2041 195.7 1.4% 11.4 2042 197.5 1.3% 10.5 2043 199.2 1.2% 9.8 2044 200.7 1.1% 9.0 2045 202.1 1.0% 8.3 2046 203.5 1.0% 7.7 2047 204.9 1.0% 7.1 2048 206.3 1.0% 6.6 2049 207.7 1.0% 6.1 2050 209.2 1.0% 5.6 2051 210.6 1.0% 5.2 2052 212.0 1.0% 4.8 2053 213.3 1.0% 4.4 2054 214.6 1.0% 4.1 2055 215.9 1.0% 3.8 2056 217.2 1.0% 3.5 2057 218.5 1.0% 3.2 2058 219.8 1.0% 3.0 2059 221.1 1.0% 2.7 2060 222.3 1.0% 2.5 2061 223.6 1.0% 2.3 2062 224.9 1.0% 2.1 2063 226.0 1.0% 2.0 2064 227.0 1.0% 1.8 2065 228.1 1.0% 1.7 2066 229.1 1.0% 1.5 2067 230.2 1.0% 1.4 2068 231.2 1.0% 1.3 2069 232.3 1.0% 1.2 2070 233.3 1.0% 1.1 2071 234.4 1.0% 1.0 Total 10,753.3 976.0

181

I-95/I-395 HOT Lane

Appendix I

Revenue Stream – 90% Confidence – NORTHERN SECTION



2012 61.5 26.1 - Growth from 2015 to 2030 interpolated from model-year results2013 65.9 30.0 - Growth rate beyond 2030 assumed decreasing 0.1% per annum to 1%2014 70.3 33.5 - Revenue growth is capped by capacity of individual HOT lane sections2015 76.8 37.8 - Ramp-up of revenue assumed: 60% year 12016 81.2 3.8% 40.7 70% year 22017 85.5 3.6% 39.4 80% year 32018 89.8 3.5% 37.9 90% year 42019 94.1 3.4% 36.5 100% year 5 onwards2020 98.4 3.3% 35.0 Adjusted for Southern Section: 86% year 12021 102.8 3.2% 33.5 90% year 22022 107.1 3.1% 32.0 93% year 32023 111.4 3.0% 30.6 97% year 42024 115.7 2.9% 29.1 100% year 5 onwards2025 120.0 2.8% 27.7 - Model revenue 2005 prices increased to 2008 prices using CPI-U2026 124.4 2.7% 26.4 194.4 : 2005 Jan-Sep2027 128.7 2.7% 25.0 216.0 : 2008 Jan-Sep2028 133.0 2.6% 23.7 11.1% : 2005 Jan-Sep to 2008 Jan-Sep2029 137.3 2.5% 22.5 - Discount rate to 2008 prices : 9%2030 141.6 2.5% 21.3 - 2015 & 2030 Value of Time escalation based on growth in GDP2031 144.8 2.4% 20.0 0.75 x forecast increase in GDP per capita:2032 147.9 2.3% 18.7 1.0952 : 2005 to 20152033 150.9 2.2% 17.5 1.2989 : 2005 to 20302034 153.6 2.1% 16.3 2035 156.2 2.0% 15.2 2036 158.7 1.9% 14.2 2037 160.8 1.8% 13.2 2038 162.9 1.7% 12.3 2039 164.8 1.6% 11.4 2040 166.6 1.5% 10.6 2041 168.2 1.4% 9.8 2042 169.7 1.3% 9.1 2043 171.1 1.2% 8.4 2044 172.4 1.1% 7.7 2045 173.5 1.0% 7.2 2046 174.7 1.0% 6.6 2047 175.8 1.0% 6.1 2048 177.0 1.0% 5.6 2049 178.2 1.0% 5.2 2050 179.4 1.0% 4.8 2051 180.6 1.0% 4.4 2052 181.7 1.0% 4.1 2053 182.7 1.0% 3.8 2054 183.7 1.0% 3.5 2055 184.8 1.0% 3.2 2056 185.8 1.0% 3.0 2057 186.9 1.0% 2.7 2058 187.9 1.0% 2.5 2059 188.9 1.0% 2.3 2060 189.9 1.0% 2.1 2061 190.9 1.0% 2.0 2062 191.9 1.0% 1.8 2063 192.8 1.0% 1.7 2064 193.6 1.0% 1.6 2065 194.4 1.0% 1.4 2066 195.2 1.0% 1.3 2067 196.0 1.0% 1.2 2068 196.8 1.0% 1.1 2069 197.6 1.0% 1.0 2070 198.4 1.0% 0.9 2071 199.2 1.0% 0.9 Total 9,252.3 855.5

182

I-95/I-395 HOT Lane

Appendix I

Revenue Stream – 90% Confidence – SOUTHERN SECTION




183

I-95/I-395 HOT Lane

Appendix I

Revenue Stream – 10% Confidence – ALL




184

I-95/I-395 HOT Lane

Appendix I

Revenue Stream – 10% Confidence – NORTHERN SECTION




185

I-95/I-395 HOT Lane

Appendix I

Revenue Stream – 10% Confidence – SOUTHERN SECTION




€¦ · halcrow, inc. 4190 lougheed highway, suite 508 burnaby, bc v5c 6a8 canada halcrow, inc....

Documents