using spreadsheet models for toll revenue forecasting don hubbard, pe, aicp senior supervising...
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Using Spreadsheet Models for Using Spreadsheet Models for
Toll Revenue ForecastingToll Revenue Forecasting
Don Hubbard, PE, AICPDon Hubbard, PE, AICP Senior Supervising Planner Senior Supervising Planner
PBPB
Topics CoveredTopics Covered
Why Are New Methodologies Needed?Why Are New Methodologies Needed?
Description of Spreadsheet ModelsDescription of Spreadsheet Models
Advantages & DisadvantagesAdvantages & Disadvantages
A Sample ApplicationA Sample Application
ConclusionsConclusions
Why Are New Methodologies Why Are New Methodologies
Needed?Needed?
Travel is a derived demand …Travel is a derived demand …
so is travel demand forecastingso is travel demand forecasting
The trouble with traffic models …The trouble with traffic models …
Post-project studies have found that traditional Post-project studies have found that traditional 4-step models have a poor record for accuracy 4-step models have a poor record for accuracy for toll roadsfor toll roads
… … and accuracy has not improved over the last and accuracy has not improved over the last thirty yearsthirty years
Models are slow, noisy, cumbersome, opaqueModels are slow, noisy, cumbersome, opaque
Output not focused on issues of highest Output not focused on issues of highest concern to clients (terms of the agreement)concern to clients (terms of the agreement)
Private investors are used to a different kind of analysis tool Private investors are used to a different kind of analysis tool and are less tolerant of 4-Step models than DOTs have beenand are less tolerant of 4-Step models than DOTs have been
What Do Investors Want?What Do Investors Want?
Ability to Ability to test variationstest variations of the things that they have of the things that they have some influence over (toll structure, number of lanes, some influence over (toll structure, number of lanes, duration of contract, exempt classes of vehicles)duration of contract, exempt classes of vehicles)
Ability to perform Ability to perform sensitivity testssensitivity tests of the things they of the things they cannot control cannot control
TransparentTransparent & easy to check & easy to check
FastFast (able to test options during negotiations) (able to test options during negotiations)
Seamless Seamless connection to financialconnection to financial post-processors post-processors
This describes a spreadsheet, This describes a spreadsheet,
not a traditional 4-step modelnot a traditional 4-step model
Description ofDescription of
Spreadsheet ModelsSpreadsheet Models
StructureStructure
Mimics a traditional Mimics a traditional modelmodel
But with simplified But with simplified trips generation & trips generation & distributiondistribution
Primary focus is on Primary focus is on traffic assignment traffic assignment and post-processingand post-processing
Trip Generation & Distribution
Traffic Assignment
Post-Processing
Trip Generation & DistributionTrip Generation & Distribution
Traffic counts are Traffic counts are done for different done for different periods of different periods of different types of daystypes of days
User groups split User groups split out to extent data out to extent data allowsallows
Traffic Counts24-hr 365 days
Off Peak
Mid-Day&Shoulders
PM Peak
# of AM Peak Hours/Day (existing)
Group 3
Group 2
Daily Origin-Destination
Demand User Group 1 (Existing)
Group 3
Group 2
Growth Rate for User Group 1
Group 3
Group 2
Daily O-D Demand for User Group 1 for
Future Study Year
1
2
34
5
6
Off Peak
# of Mid-Day & Shoulder Hours/Day
(existing)
WeekdaysWeekends & Holidays
Off Peak
Mid-Day&Shoulders
PM Peak
Initial Volumes & Numbers of AM Peak
Hours/day (study year)
One set for Weekdaysand a second set for Weekends & Holidays
Growth factors based on population & Growth factors based on population & employment forecasts by catchment areaemployment forecasts by catchment area
Peak SpreadingPeak Spreading
Excess peak period traffic Excess peak period traffic results in longer peakresults in longer peak
Revised traffic then goes Revised traffic then goes to diversion modelto diversion model
Traffic DiversionModel
Peak Spreading Algorithm
6
78
9
10
Off Peak
Mid-Day&Shoulders
PM Peak
Initial Volumes & Numbers of AM Peak
Hours/day (study year)
One set for Weekdaysand a second set for Weekends & Holidays
Off Peak
Mid-Day & Shoulders
PM Peak
Revised Volumes & Numbers of AM Peak
Hours/day
Corridor Capacity
TrafficAssignmentModel
Traffic Profile for AM Peak Period
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
5:30 6:00 6:30 7:00 7:30 8:00 8:30 9:00 9:30 10:00
Time of Day
Tra
ffic
Vo
lum
e p
er H
alf-
Ho
ur
Ajusted Future
Unadjusted Future
Existing
Traffic DiversionTraffic Diversion
Split between tollway & Split between tollway & non-tolled alternative non-tolled alternative based on ratio of costsbased on ratio of costs
Group 3
Group 3
Assume 80:20 Splitin Traffic between
Tollroad & Free Road
Travel Times for each Section
Group 3
Final Traffic Volumesby Section for
Period in Question
Spreadsheet Methodology for Traffic Assignment andRevenue Forecasting for a Single Time Period
Group 2
Origin-Destination Data for User
Group 1
Group 2
SectionalDemand Group 1
New Volumes by Section
Group 2
Toll Costs for Group 1 (minutes)
Group 3
Group 2
DiversionCurve Group 1
Speed-Volume Relationship
Feedback until stable
Tollroad Revenues for Period in Question
Group 3
Group 2
Value of Time for Group 1
Group 3
Group 2
Toll for Group 1
1
2
3
4
6
5
7 8
9
10
11
13
12
Key
Input
Intermediate Step
Output
Free-Flow Speed
Capacity of Competing Routes by Sections
Length of Competing Routes by Sections
Speed - Volume Relationship
0.010.020.030.040.050.060.070.080.0
0 400 800 1200 1600 2000 2400 2800
Traffic Volume
Sp
ee
d
Diversion Curves
0%
20%
40%
60%
80%
100%
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
Difference in Travel Time
% U
sin
g T
ollw
ay
Starts with a seed value Starts with a seed value for the split, then for the split, then iterates assignment to iterates assignment to produce a stable resultproduce a stable result
Post-ProcessingPost-Processing
Outputs from the diversion model are traffic volumes and Outputs from the diversion model are traffic volumes and revenues for each periodrevenues for each period
Off Peak
Study Year Revenues
Midday & Shoulders
PM Peak
AM Peak Period Revenues
Traffic DiversionModel
Off Peak
Midday & Shoulders
PM Peak
# of AM Peak Hours/Year
Number of Weekdays,
Weekend Days & Holidays per Year
12
13
14
15
Off Peak
Midday & Shoulders
PM Peak
AM Peak Period Traffic Volumes
11
LOS Analysis
Capacity Improvement Plans
16
17
The volumes can be fed into The volumes can be fed into LOS analysis and used to LOS analysis and used to forecast when capacity forecast when capacity improvements will be neededimprovements will be needed
Revenues can be Revenues can be aggregated to annual aggregated to annual levels for use in financial levels for use in financial analysesanalyses
Sample SheetSample Sheetfor Single Periodfor Single Period
High Income Cars Low Income Cars Trucks with More Than 2 Axles
Toll = Toll = Toll =
Value of Time = Value of Time = Value of Time =
Demand (vehicle-trips per hour) Demand (vehicle-trips per hour) Demand (vehicle-trips per hour)Free Tollway
1 4 2 3.5 A B C D E F A B C D E F A B C D E F2 4 2 2.0 A - 250 525 416 250 2,500 A - 500 1,050 833 500 5,000 A - 25 53 42 25 2503 4 2 2.5 B - - 59 25 13 488 B - - 118 50 25 975 B - - 6 3 1 494 4 2 2.5 C - - - 59 29 579 C - - - 118 58 1,158 C - - - 6 3 585 4 2 2.5 D - - - - 46 870 D - - - - 93 1,740 D - - - - 5 87
E - - - - - 1,250 E - - - - - 2,500 E - - - - - 125Capacity per Lane = 2,000 F - - - - - - F - - - - - - F - - - - - -Truck PCU Factor = 2.0
Percent of This Group's Demand that Uses Tollway Percent of This Group's Demand that Uses Tollway Percent of This Group's Demand that Uses Tollway
A B C D E F A B C D E F A B C D E FFree Tollway A - 0% 19% 26% 28% 50% A - 0% 6% 12% 15% 35% A - 0% 13% 20% 23% 45%
1 7,574 3,250 30% B - - 3% 13% 19% 49% B - - 0% 3% 8% 30% B - - 1% 8% 14% 42%2 7,991 3,750 32% C - - - 4% 12% 47% C - - - 0% 4% 26% C - - - 2% 8% 39%3 7,833 4,134 35% D - - - - 3% 48% D - - - - 0% 21% D - - - - 2% 37%4 8,447 4,663 36% E - - - - - 0% E - - - - - 0% E - - - - - 0%5 11,099 4,517 29% F - - - - - - F - - - - - - F - - - - - -
Number of Tollway Users from This Group Number of Tollway Users from This Group Number of Tollway Users from This Group
A B C D E F A B C D E F A B C D E FFree Tollway A - 0 102 107 70 1,254 A - 0 64 97 77 1,751 A - 0 7 8 6 112
1 68 69 B - - 2 3 2 237 B - - 0 2 2 292 B - - 0 0 0 202 66 68 C - - - 2 3 275 C - - - 1 2 300 C - - - 0 0 233 67 65 D - - - - 2 419 D - - - - 0 360 D - - - - 0 324 64 59 E - - - - - 0 E - - - - - 0 E - - - - - 05 40 61 F - - - - - - F - - - - - - F - - - - - -
Study Year:Number of Tollway Users Period: Overall Percent Using Tollway Direction:Total Hourly Revenues $1,461$620
2,947
$73734% 25%
Orig
ins
1 2
$105
User Group
Destinations
Orig
ins
$0.50
$18.00
All TripsDestinations
2004
Destinations
AM Peak
$0.25
$12.00
$0.25
$6.00
2,478 209
Destinations
Southbound
All TripsDestinations
Orig
ins
All TripsDestinations Destinations
Orig
ins
All Trips
Orig
ins
Section Length
Orig
ins
All Trips
Orig
ins
Traffic VolumesAll Trips
# of Lanes onDestinations
All Trips
Total
20% 28%
5,635
Section
Orig
ins
Orig
ins
3
SectionSpeed
% Using Tollway
All TripsDestinations
All Trips
Area A
B
C
D
Section1
E
Area F
Inputs for Road Supply
Inputs for Corridor Demand
ResultsSection
2
Section3
Section4
Section5
Free Tollway1 4 2 3.52 4 2 2.03 4 2 2.54 4 2 2.55 4 2 2.5
Capacity per Lane = 2,000Truck PCU Factor = 2.0
Section Length# of Lanes on
Inputs for Road Supply
High Income Cars
Toll =
Value of Time =
Demand (vehicle-trips per hour)
A B C D E FA - 250 525 416 250 2,500B - - 59 25 13 488C - - - 59 29 579D - - - - 46 870E - - - - - 1,250F - - - - - -
$0.25
$12.00
DestinationsAll Trips
Orig
ins
Percent of This Group's Demand that Uses Tollway
A B C D E FA - 0% 19% 26% 28% 50%B - - 3% 13% 19% 49%C - - - 4% 12% 47%D - - - - 3% 48%E - - - - - 0%F - - - - - -
Orig
ins
All TripsDestinations
Free Tollway1 7,574 3,250 30%2 7,991 3,750 32%3 7,833 4,134 35%4 8,447 4,663 36%5 11,099 4,517 29%
Traffic VolumesSection
% Using TollwayFree Tollway
1 68 692 66 683 67 654 64 595 40 61
SectionSpeed
Number of Tollway Users
Overall Percent Using TollwayTotal Hourly Revenues $1,461$620
2,947
$73734% 25%
1 2
$105
User Group
2,478 209
Total
20% 28%
5,635
3
From Master Input Sheet
Assumed Capacity Per Lane 2,000 SECTIONAL VOLUMES AND LOS
Southbound Northbound
Tollway Other Tollway Other Tollway Other Tollway Other Tollway Other Tollway Other2 4 2 4 2 4 2 4 2 4 2 4
Free 3,863 5,593 41% 4,317 7,087 38% 4,699 8,263 36% Free 1,932 2,284 46% 2,324 2,751 46% 2,623 3,110 46%$0.045 2,359 7,098 25% 3,310 8,094 29% 4,100 8,861 32% $0.045 894 3,321 21% 1,076 3,999 21% 1,220 4,513 21%$0.089 1,439 8,018 15% 2,457 8,947 22% 3,494 9,467 27% $0.089 423 3,792 10% 511 4,564 10% 584 5,149 10%$0.134 923 8,534 10% 1,902 9,502 17% 3,008 9,953 23% $0.134 212 4,004 5% 256 4,819 5% 296 5,437 5%$0.150 792 8,664 8% 1,752 9,652 15% 2,867 10,094 22% $0.150 168 4,048 4% 203 4,872 4% 235 5,498 4%
Tollway Other Tollway Other Tollway Other Tollway Other Tollway Other Tollway Other2 4 2 4 2 4 2 4 2 4 2 4
Free 4,141 6,407 39% 4,577 8,042 36% 4,963 9,333 35% Free 2,183 2,930 43% 2,618 3,499 43% 2,937 3,913 43%$0.045 2,556 7,993 24% 3,552 9,067 28% 4,381 9,914 31% $0.045 1,010 4,103 20% 1,212 4,905 20% 1,368 5,483 20%$0.089 1,573 8,975 15% 2,667 9,952 21% 3,775 10,520 26% $0.089 478 4,634 9% 576 5,542 9% 656 6,194 10%$0.134 1,018 9,531 10% 2,086 10,533 17% 3,286 11,009 23% $0.134 239 4,874 5% 289 5,829 5% 332 6,518 5%$0.150 876 9,672 8% 1,929 10,690 15% 3,145 11,150 22% $0.150 190 4,923 4% 229 5,888 4% 265 6,586 4%
Tollway Other Tollway Other Tollway Other Tollway Other Tollway Other Tollway OtherLanes 2 4 2 4 2 4 2 4 2 4 2 4Free 4,246 6,343 40% 4,645 7,795 37% 5,002 8,912 36% Free 2,278 2,874 44% 2,734 3,465 44% 3,067 3,896 44%
$0.045 2,663 7,926 25% 3,660 8,779 29% 4,468 9,447 32% $0.045 1,054 4,098 20% 1,266 4,933 20% 1,430 5,533 21%$0.089 1,659 8,929 16% 2,785 9,654 22% 3,892 10,023 28% $0.089 499 4,653 10% 602 5,598 10% 687 6,276 10%$0.134 1,087 9,501 10% 2,206 10,234 18% 3,420 10,494 25% $0.134 250 4,903 5% 302 5,897 5% 349 6,614 5%$0.150 940 9,648 9% 2,049 10,391 16% 3,283 10,632 24% $0.150 198 4,954 4% 240 5,959 4% 278 6,685 4%
2010 2020 2030Tollway
as %
Tollway as %
Tollway as %
2010
Tollway as %
2020 2030
Tollway as %
Tollway as %
Tollway as %
Tollway as %
2020 2030
Tollway as %
2010 2020 2030
20302010
AM Peak Hour
Tollway as %
Tollway as %
Tollway as %
Tollway as %
Tollway as %
2020
Tollway as %
2010 2020 2030 2010Tollway
as %Tollway
as %Tollway
as %
Section1
Section2
Section3
Section4
Section5
Sample VolumeSample Volume& LOS Output & LOS Output
Color Code Level of Service A, B, or C Level of Service D or E Level of Service F
Tollway Other2 4
Free 4,141 6,407 39%$0.045 2,556 7,993 24%$0.089 1,573 8,975 15%$0.134 1,018 9,531 10%$0.150 876 9,672 8%
2010Tollway
as %SchematicSchematic
Southbound NorthboundSouthbound Northbound
Managing the ProcessManaging the Process
All scenario inputs are All scenario inputs are entered into a single pageentered into a single page
Macros then open other Macros then open other workbooks, process data, workbooks, process data, and closeand close
Summary results then Summary results then copied into master filecopied into master file
Fast, compact resultsFast, compact results
Master FileInputs & Outputs
Traffic Assignment
Traffic Assignment
Traffic Assignment
Traffic Assignment
Traffic Assignment
Traffic Assignment
Traffic Assignment
Traffic Assignment
Model for Each Period, Day, & Year Combo
Traffic Assignment
Post-Processed Results for Each
Study Year
Advantages &Advantages &
DisadvantagesDisadvantages
AdvantagesAdvantages
Often quicker & easier to createOften quicker & easier to create They force you to examine your assumptions, so may They force you to examine your assumptions, so may
be more rigorousbe more rigorous Less noise than traditional models, so more accurate Less noise than traditional models, so more accurate
for small changesfor small changes Can feed directly to/from other models (land use, Can feed directly to/from other models (land use,
financial models) financial models) Better control over the process (for the same reasons Better control over the process (for the same reasons
that airplanes are more maneuverable when not using that airplanes are more maneuverable when not using auto-pilot)auto-pilot)
DisadvantagesDisadvantages
Limited to well-defined corridors with only a few Limited to well-defined corridors with only a few realistic alternative routesrealistic alternative routes
Single-purpose models; cannot replace 4-step Single-purpose models; cannot replace 4-step models for general modeling usemodels for general modeling use
Agencies may be reluctant to accept alternatives Agencies may be reluctant to accept alternatives to a regional model if one existsto a regional model if one exists
Sample Application:Sample Application:
North Luzon ExpresswayNorth Luzon Expressway
Project BackgroundProject Background
Old tollway extending Old tollway extending northwards from Metro northwards from Metro Manila Manila
Leased to private company Leased to private company under an upgrade-operate-under an upgrade-operate-transfer agreementtransfer agreement
Varies from 8-lane freeway Varies from 8-lane freeway in south to 4-lane in south to 4-lane expressway in northexpressway in north
Alternate route is 2-to-4 Alternate route is 2-to-4 lane undivided highwaylane undivided highway
Source: PB AsiaSource: PB AsiaManila
(12 Million)
San Fernando (500,000)
Angeles City (500,000)
Key FeaturesKey Features
50 miles of freeway50 miles of freeway16 interchanges16 interchanges$377 million cost$377 million cost
Need to keep costs down; Need to keep costs down; toll increase politically toll increase politically sensitivesensitive
Needed detailed volume Needed detailed volume forecasts for each ramp to forecasts for each ramp to do “just enough” and do “just enough” and “just in time” upgrading“just in time” upgrading
Urban SectionUrban Section
Rural SectionRural Section
Model RequirementsModel Requirements
Also needed detailed cost Also needed detailed cost and revenue projections to and revenue projections to arrange for various loan arrange for various loan packages packages
Banks required that all Banks required that all assumptions be open to assumptions be open to scrutinyscrutiny
Model must be able to Model must be able to predict, on the spot, the predict, on the spot, the effect of changes in effect of changes in assumptions assumptions
Costs Revenue
$
Background for the NLE Background for the NLE ModelModel
Existing regional lacked Existing regional lacked detail in study corridor detail in study corridor
Ramp volumes varied Ramp volumes varied erratically for different erratically for different study yearsstudy years
Investors unwilling to Investors unwilling to take risks on unreliable take risks on unreliable forecastsforecasts
New Approach - SpreadsheetNew Approach - Spreadsheet
9 months spent trying to fix regional model, 9 months spent trying to fix regional model, only 3 months remained before firm forecasts only 3 months remained before firm forecasts were neededwere needed
Determined that the regional model was unlikely Determined that the regional model was unlikely to produce the needed accuracy within the time to produce the needed accuracy within the time availableavailable
Decided to replace the regional model with a Decided to replace the regional model with a spreadsheet modelspreadsheet model
Trip Generation Trip Generation
O-D table taken from O-D table taken from toll receipts from toll receipts from previous 5 yearsprevious 5 years
Growth rates for Growth rates for each O-D pair were each O-D pair were based on the based on the expected population expected population and employment and employment growth at each endgrowth at each end
Growth of O-D TableGrowth of O-D Table
The existing volumes at The existing volumes at each ramp were then each ramp were then factored up, based on factored up, based on future volumes of the O-D future volumes of the O-D pairs served, to make pairs served, to make “Base Demand”“Base Demand”
Existing
2010
2020
2030
Other Input AssumptionsOther Input Assumptions
0
2 0000
4 0000
6 0000
8 0000
1 E +05
1 E +05
1 E +05
2 E +05
2 E +05
2 E +05
0 0.5 1 1 .5 2 2 .5 3 3 .5 4
Toll
Tra
ffic
Next added:Next added:
- Assumed tolls- Assumed tolls
- Toll sensitivity- Toll sensitivity
- Income assumptions- Income assumptions
Income GrowthIncome Growth
0.1
0.1 2
0.1 4
0.1 6
0.1 8
0.2
0.2 2
0.2 4
0.2 6
1 2 3 4 5 6 7 8 9 1 0
YearIn
com
e
Diversion CurveDiversion Curve
Capacity ConstraintsCapacity Constraints
Explicit capacity constraints Explicit capacity constraints were made for:were made for:
- Receiving capacity of- Receiving capacity of local roads local roads
- Toll plaza capacity- Toll plaza capacity
- Mainline capacity- Mainline capacity
Peak SpreadingPeak Spreading
Separate sheets were done Separate sheets were done for each peak period and for each peak period and for the off-peak period, for the off-peak period, with spillover (peak with spillover (peak spreading) based on spreading) based on conditions during the peak conditions during the peak hourhour
Peak
Off-Peak
Spill-Spill-OverOver
Schedule for UpgradingSchedule for Upgrading
Ramp volumes were Ramp volumes were automatically compared to automatically compared to service thresholdsservice thresholds
Produced an upgrading Produced an upgrading schedule for each of 40+ schedule for each of 40+ rampsramps
RampVolumes
Year UpgradeNeeded
LOS LOS ThresholdThreshold
Financial ResultsFinancial Results
The resulting volumes for The resulting volumes for each ramp-to-ramp pair, each ramp-to-ramp pair, for each vehicle class, for each vehicle class, were converted into annual were converted into annual revenuesrevenues
These were automatically These were automatically fed into the financial fed into the financial spreadsheetsspreadsheets
Volume
Revenue
Annuali-Annuali-zation zation FactorFactor
Application During NegotiationsApplication During Negotiations
The model was able to quickly answer questions like, The model was able to quickly answer questions like, “What happens if the government refuses to approve “What happens if the government refuses to approve toll increases after the first 5 years?”toll increases after the first 5 years?”
?- Traffic increases- Traffic increases- Upgrading needed sooner- Upgrading needed sooner- Revenue/veh decreases- Revenue/veh decreases- Rate of return declines- Rate of return declines
Results of the NLE ModelResults of the NLE Model
The methodology was The methodology was robust and defendablerobust and defendable
The resulting forecasts The resulting forecasts were reasonablewere reasonable 0
50
100
150
200
250
Year
Av
era
ge
Da
ily T
raff
ic (
bo
th d
ire
cti
on
s)
1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015
The client was able to get financing; upgrading now The client was able to get financing; upgrading now underwayunderway
“Asia-Pacific Transport Project of the Year”“Asia-Pacific Transport Project of the Year”Project Finance Magazine Project Finance Magazine (London)(London)
Past Future
ConclusionsConclusions
ConclusionsConclusions
Model types should be considered tools in a toolbox; Model types should be considered tools in a toolbox; different types are needed for different tasksdifferent types are needed for different tasks
There are circumstances where spreadsheet models There are circumstances where spreadsheet models are likely to produce better results than traditional are likely to produce better results than traditional modelsmodels
– Well-defined corridor with limited routesWell-defined corridor with limited routes
– Uncertainties about input assumptions more likely Uncertainties about input assumptions more likely source of error than computational mechanicssource of error than computational mechanics
Don HubbardDon Hubbard
Senior Supervising PlannerSenior Supervising PlannerPBPB
Tel. (916) 567-2555Tel. (916) [email protected]@pbworld.com