cali vissim

7/31/2019 Cali Vissim

1/92

i

Development of VISSIM Base Model for Northern

Virginia (NOVA) Freeway System

By:

Srividya Santhanam

Byungkyu (Brian) Park

Research Report No. UVACTS-13-0-124

June 2008


2/92

ii

A Research Project Report

For the Northern Region Operations

Virginia Department of Transportation (VDOT)

Srividya Santhanam

Department of Civil Engineering`

Email: [email protected]

Dr. Byungkyu (Brian) Park

Department of Civil Engineering

Email: [email protected]

Center for Transportation Studiesat the University of Virginia produces outstanding transportationprofessionals, innovative research results and provides important public service. The Center for

Transportation Studies is committed to academic excellence, multi-disciplinary research and to developing

state-of-the-art facilities. Through a partnership with the Virginia Department of Transportations (VDOT)Research Council (VTRC), CTS faculty hold joint appointments, VTRC research scientists teach

specialized courses, and graduate student work is supported through a Graduate Research Assistantship

Program. CTS receives substantial financial support from two federal University Transportation Center

Grants: the Mid-Atlantic Universities Transportation Center (MAUTC), and through the National ITS

Implementation ResearchCenter (ITS Center). Other related research activities of the faculty includefunding through FHWA, NSF, US Department of Transportation, VDOT, other governmental agencies and

private companies.

Disclaimer:The contents of this report reflect the views of the authors, who are responsible for the factsand the accuracy of the information presented herein. This document is disseminated under the

sponsorship of the Department of Transportation, University Transportation Centers Program, intheinterest of information exchange. The U.S. Government assumes no liability for the contents or use

thereof.

CTS Website Center for Transportation Studieshttp://cts.virginia.edu University of Virginia

351 McCormick Road, P.O. Box 400742

Charlottesville, VA 22904-4742434.924.6362


3/92

iii

1. Report No. UVACTS-13-0-124 2. Government Accession No. 3. Recipients Catalog No.

4. Title and Subtitle 5. Report Date

Development of VISSIM Model for Northern Virginia (NOVA) Freeway System June 2008

6. Performing Organization Code

7. Author(s) 8. Performing Organization Report No.

Srividya Santhanam and Byungkyu (Brian) Park UVACTS-13-0-124

9. Performing Organization and Address 10. Work Unit No. (TRAIS)

Center for Transportation Studies

University of Virginia 11. Contract or Grant No.

PO Box 400742

Charlottesville, VA 22904-7472

12. Sponsoring Agencies' Name and Address 13. Type of Report and Period Covered

Northern Region Operations

Virginia Department of Transportation

Final Report

14. Sponsoring Agency Code

15. Supplementary Notes

16. Abstract

This project aims at providing the Northern Region Operations (NRO) staff with a microscopic traffic simulation model for

major Northern Virginia (NOVA) Freeway system using VISSIM. The network includes 4 Interstate Highways (I-66, I-95, I-395, and

I-495) and a State Highway 267. This report provides details on the tasks of network building, O-D estimation and model calibration.

The O-D matrices were estimated on the basis of traffic counts obtained from video cameras, sensors, and average annual daily traffic

using QUEENSOD method. Latin Hypercube experimental design approach was used for the calibration. The project deliverables to

the Northern Region Operations include calibrated network for the NOVA freeway system in VISSIM program along with O-D tables

and base measures of effectiveness.

17 Key Words 18. Distribution Statement

Microscopic traffic simulation model, O-D Estimation, Calibration, LatinHypercube experimental design, Measures of Effectiveness (MOE)

No restrictions. This document is available to thepublic.


4/92

iv

Abstract

This project aims at providing the Northern Region Operations (NRO) staff with a

microscopic traffic simulation model for major Northern Virginia (NOVA) Freeway

system using VISSIM. The network includes 4 Interstate Highways (I-66, I-95, I-395,

and I-495) and a State Highway 267. This report provides details on the tasks of network

building, O-D estimation and model calibration. The O-D matrices were estimated on the

basis of traffic counts obtained from video cameras, sensors, and average annual daily

traffic using QUEENSOD method. Latin Hypercube experimental design approach was

used for the calibration. The project deliverables to the Northern Region Operations

include calibrated network for the NOVA freeway system in VISSIM program along with

O-D tables and base measures of effectiveness.


5/92

v

Table of Contents

Abstract.............................................................................................................................. ivTable of Contents................................................................................................................ v

List of Figures .................................................................................................................... vi

List of Tables .................................................................................................................... vii

Introduction......................................................................................................................... 1

Purpose and Scope.......................................................................................................... 2

Organization of the Report.............................................................................................. 2

Chapter 1: Network Coding................................................................................................ 3

1.1 Parking Lots and Nodes............................................................................................ 31.2 Vehicle Types and Traffic Composition................................................................... 41.3 HOV Lanes and Link/Connector Costs .................................................................... 5

1.4 Toll Roads and Stop Signs........................................................................................ 8

1.5 Driving Behavior Parameters Based on Link Type .................................................. 9

Chapter 2: O-D Estimation ............................................................................................... 11

2.1 Data Collection ....................................................................................................... 11

2.1.1 Data from Detectors......................................................................................... 11

2.1.3 Extrapolation of Missing Data ......................................................................... 21

2.2 O-D Estimation Using QUEENSOD...................................................................... 242.3 Measures of Effectiveness Data Collection......................................................... 27

Chapter 3: Simulation Model Calibration......................................................................... 28

3.1 Introduction............................................................................................................. 283.2 Latin Hypercube Sampling ..................................................................................... 29

3.3 Experimental Design Results.................................................................................. 30

3.3.1 Simulation Model Setup .................................................................................. 313.3.2 Initial Evaluation.............................................................................................. 31

3.3.3 Initial Calibration Latin Hypercube Design (LHD) for Driving Behavior... 363.3.3.1 Experimental Design Latin Hypercube Sampling ................................. 38

3.3.3.2 Multiple Runs............................................................................................ 38

3.3.3.3 Parameter Set Selection ............................................................................ 38

Chapter 5: Measures of Effectiveness from the Calibrated Model................................... 47

5.1 Counts and Speeds.................................................................................................. 47

5.2 Travel Time and Delays.......................................................................................... 485.3 Density .................................................................................................................... 49

Conclusions and Recommendations ................................................................................. 51References......................................................................................................................... 52

Appendix A....................................................................................................................... 53

Appendix B ....................................................................................................................... 81


6/92

vi

List of Figures

Figure 2.Northern Virginia (NOVA) Freeway network (source: Google map) ................. 1

Figure 3. I-66 EB Mainline Flow Profiles ........................................................................ 21

Figure 4. I-66 EB On Ramp Flow Profiles ....................................................................... 22

Figure 5. I-66 EB Off Ramp Flow Profiles....................................................................... 22Figure 6. Flow Chart for Calibration Using Latin Hypercube Experimental Design....... 28

Figure 7.Two-dimensional representation of a LHS of size 5 for X1 and X2 ................... 30

Figure 8. I-66EB Network ................................................................................................ 31

Figure 9. Simulated TT Vs Observed TT (6:00-6:30 AM)-Default Parameters............... 32Figure 10. Simulated TT Vs Observed TT (6:30-7:00 AM)-Default Parameters............. 33

Figure 11. Simulated TT Vs Observed TT (7:30-8:00 AM)-Default Parameters............. 33

Figure 12. Simulated Counts Vs Detector Counts for Station 2-Default Parameters....... 34

Figure 13. Simulated Speed Vs Detector Speed for Station 2-Default Parameters.......... 34

Figure 14. Simulated Counts Vs Detector Counts for Station 10-Default Parameters..... 35Figure 15. Simulated Speed Vs Detector Speed for Station 10-Default Parameters........ 35

Figure 16.Simulated TT Vs Sample Case......................................................................... 39

Figure 17. Simulated TT Vs CC1 ..................................................................................... 40

Figure 18. Simulated TT Vs Observed TT (6:00-6:30 AM)-C98 (LHD_1)..................... 41

Figure 19. Simulated TT Vs Observed TT (6:30-7:00 AM)-C98 (LHD_1)..................... 42

Figure 20. Simulated TT Vs Observed TT (7:30-8:00 AM)-C98 (LHD_1)..................... 42Figure 21. Simulated Counts Vs Detector Counts for Station 2 C98 (LHD_1) ............ 43

Figure 22. Simulated Speed Vs Detector Speed for Station 2 C98 (LHD_1)................ 43Figure 23. Simulated Counts Vs Detector Counts for Station 10 C98 (LHD_1) .......... 44Figure 24. Simulated Speed Vs Detector Speed for Station 10 C98 (LHD_1).............. 44

Figure 1.A print screen of Step 1 in usage of Lane Closure.exe ...................................... 81

Figure 2.A print screen of Steps 4-7 in Usage of Lane Closure.exe................................. 83

Figure 3.A print screen of Functioning of Lane Closure.exe ........................................... 84

Figure 4.A print screen of RSZ Data Reduction.exe ........................................................ 85


7/92

vii

List of Tables

Table 1.Cost Coefficients for each Traffic Composition.................................................... 7Table 2.Connector Costs..................................................................................................... 7

Table 3. HOV Schedule for Corridors in NOVA Network ................................................ 8

Table 4.Speed and Deceleration values used for lanes on SH 267..................................... 9Table 5.Detector Data Quality Summery by Interstate..................................................... 12

Table 6. I-66 EB Volume Data from Detectors ................................................................ 16Table 7. I-66 EB Volume Data from Videos .................................................................... 16

Table 8. I-66 WB Volume Data from Detectors............................................................... 17

Table 9. I-66 WB Volume Data from Videos................................................................... 17

Table 10. I-95 NB Volume Data from Detectors.............................................................. 18

Table 11. I-95 NB Volume Data from Videos.................................................................. 18

Table 12. I-395 NB Volume Data from Detectors............................................................ 19

Table 13. I-395 NB Volume Data from Videos................................................................ 19

Table 14. I-95 SB Volume Data from Detectors .............................................................. 20Table 15. I-395 SB Volume Data from Detectors ............................................................ 20

Table 16. Extrapolation of Missing Data.......................................................................... 23

Table 17. Details of OD Matrices for each Freeway ........................................................ 25

Table 18. Relative Error for Simulations using Default Parameters ................................ 36

Table 19.Relative Error for Simulations using C98 and Default Parameters................... 40Table 20. C98 and Default Parameters ............................................................................. 45

Table 21. Stations defined for Simulated Counts and Speeds .......................................... 48

Table 22. Travel Time Sections Defined in the Network ................................................. 49Table 23. Link IDs/Locations for Density ........................................................................ 50

Table 1.Simulated Counts for I-66 EB ............................................................................. 53

Table 2.Simulated Speeds for I-66 EB ............................................................................. 54

Table 3.Simulated Travel Times for I-66 EB ................................................................... 55

Table 4.Simulated Densities for I-66 EB.......................................................................... 57

Table 5.Simulated Delays for I-66 EB.............................................................................. 58

Table 6.Simulated Counts for I-66 WB............................................................................ 59

Table 7.Simulated Speeds for I-66 WB............................................................................ 61Table 8.Simulated Travel times for I-66 WB ................................................................... 62

Table 9.Simulated Densities for I-66 WB......................................................................... 63

Table 10.Simulated Delays for I-66 WB .......................................................................... 65

Table 11.Simulated Counts for I-95 and I-395 NB........................................................... 66Table 12.Simulated Speeds for I-95 and I-395 NB........................................................... 67

Table 13.Simulated Travel Times for I-95 and I-395 NB ................................................ 68

Table 14.Simulated Densities for I-95 and I-395 NB....................................................... 70

Table 15.Simulated Delays for I-95 and I-395 NB........................................................... 71

Table 16.Simulated Counts for I-95 and I-395 SB ........................................................... 73Table 17.Simulated Speeds for I-95 and I-395 SB........................................................... 74

Table 18.Simulated Travel times for I-95 and I-395 SB .................................................. 75

Table 19.Simulated Densities for I-95 and I-395 SB........................................................ 77

Table 20.Simulated Delays for I-95 and I-395 SB ........................................................... 79


8/92

1

Final Report

Development of VISSIM Base Model for Northern Virginia (NOVA)Freeway System

June 2008

Dr. Byungkyu Brian Park and Srividya Santhanam

Traffic Operations Laboratory

Center for Transportation Studies

University of Virginia

IntroductionThe purpose of this project is to provide Northern Region Operations (NRO) staff

with a microscopic traffic simulation model for major Northern Virginia (NOVA)

Freeway network using VISSIM. These freeways include four Interstate Highways (I-66,

I-495, I-395, and I-95) and a State Highway (SH 267). This network shown in Figure 1 is

referred to NOVA network in the remainder of the report.

Figure 1.Northern Virginia (NOVA) Freeway network (source: Google map)


9/92

2

Purpose and Scope

The project deliverables to the Northern Region Operations (NRO) staff are

calibrated network for the NOVA freeway system in VISSIM program along with O-D

tables and base measures of effectiveness. The main tasks involved in completing the

project were: (1) network building, (2) OD estimation and (3) simulation model

calibration.

Organization of the Report

The following chapters of this report describe the details on the important aspects

of each task. The first chapter explains the network coding efforts in VISSIM and some

important characteristics of the NOVA network. The second chapter presents the data

collection and the O-D estimation efforts that were carried out for the project. The third

chapter focuses on the procedure adopted for the calibration of the VISSIM freeway

network model. The base measures of effectiveness for each of the corridors of the

NOVA network have been tabulated in the Appendix A. Furthermore some guidelines for

the usage of applications developed for the model have also been presented in the

Appendix B.


10/92

3

Chapter 1: Network Coding

Building an accurate VISSIM model from scratch requires scaled maps showing

the real network in detail. In order to replicate the modeled infrastructure network to

scale, aerial photos (which served as base maps) were obtained and used as background

images and VISSIM network was traced exactly according to the scaled maps.

Dynamic Assignment (DA) module was chosen to model the route choice

behavior of drivers for the freeways. As such, the specification of Origin Destination

matrices are needed for input flows. To define travel demand using an OD matrix, the

area to be simulated is divided into sub-areas called zones and to model the points

where vehicles actually enter or leave the road network, a network element called

parking lot is created. To reduce the complexity of the network, parts of the network

where paths could diverge are defined using network element node.

The following sections list a few important aspects of the network.

1.1 Parking Lots and Nodes

a) There are two types of Parking Lots that can be used in DA module of VISSIM

Abstract Parking Lot and Zone Connector. Abstract parking lots are used if the

road network is detailed enough to represent actual parking lot. The vehicles

approaching abstract parking lot slow down until they come to a stop at the

middle of the parking lot. Their capacity is limited to 700 vehicles per hour per

lane. When Zone Connectors are used, entering vehicles do not slow down and

are just removed from the network as they reach the middle of the parking lot.

Thus the entry capacity of a zone connector is not restricted and this type is


11/92

4

appropriate to model origin and destination points where traffic enters or exits the

network without using real parking. Hence Zone Connectors were used to model

the entry and exit points for the NOVA network.

b) As mentioned earlier, nodes are created where paths diverge or merge. It is

imperative to include the connectors at diverging and merging paths of the

network within a single node.

c) From the information given by the users definition of nodes, VISSIM builds an

abstract network graph consisting of edges (which distinguish them from the

links of the basic VISSIM network) when the Dynamic Assignment is started.

The edges are the basic building blocks of the routing search. For all the edges

travel times and costs are computed from the simulation and they are used in the

route choice model.

1.2 Vehicle Types and Traffic Composition

In VISSIM, vehicles that share common vehicle performance attributes are added

into a single group, categorizing vehicle population into vehicle types. Each vehicle type

is defined with several attributes such as vehicle model, minimum and maximum

acceleration/deceleration, weight, length, etc. Based on the characteristics of each

corridor of the NOVA network, 3 main vehicle types were created GP, HOV and HGV.

The GP type represented vehicles with a single occupant and those that used the General

Purpose lanes on the Interstate Highways I-66, I-95 and I-395 of the NOVA network. The

HOV type represented vehicles with 2 or more occupants on I-66; vehicles with 3 or

more occupants on I-95 and I-395; thus making them eligible to use the HOV lane on the


12/92

5

corresponding corridors of the NOVA network (the HOV lanes are being operated during

the peak hours). The HOV lane schedule for each corridor is listed in

Table 3. The specifications and characteristics for these two types were identical

to those of the default CAR type in VISSIM. The HGV type represented the heavy

vehicles on the network with vehicle type characteristics of default HGV type in

VISSIM.

In VISSIM, the vehicle mix of each network entrance flow for the network is

defined by traffic composition. For example, three traffic compositions were defined for

the I-66 EB network: GP for general purpose lane use (100 % GP type), HOV for those

eligible to use HOV lane (100% HOV type) and HV for general purpose lane use (100 %

HGV type).

1.3 HOV Lanes and Link/Connector Costs

Some interchanges on I-66 of the NOVA network contain auxiliary lanes and it

was observed during test simulations that vehicles chose the auxiliary lanes instead of the

mainline path causing congestion in the nearby areas and eventually reducing the

vehicular speeds. This undesired behavior during simulation was rectified by adding costs

to the links containing the auxiliary lanes. Travel time, travel distance, and financial cost

(e.g., tolls) are the factors that influence route choice in VISSIM. The General Cost for

all edges is computed as a weighted sum:

General Cost = * travel time + * travel distance + * financial cost (1)


13/92

6

The coefficients , , can be defined by the user for a particular vehicle class.

Thus by adding financial cost on appropriate links, the route choice of the vehicles can be

altered as desired during the simulation.

As noted, the Outside Beltway section of I-66 features a High Occupancy Vehicle

(HOV) lane (not barrier separated, open to HOV 2+ vehicles during the peak hours) and

Hard Shoulders (open to all vehicles during peak hours). Simulation of this feature

requires lane closure/open by time of day (simulation time) which VISSIM does not

currently provide directly. Taking advantage of the VISSIM COM Interface Module, a

separate application was developed in Visual C++ to implement this Lane Closure

Facility. HOV only restrictions were enforced by creating separate vehicle type for the

HOV eligible vehicles, and by closing the HOV only lanes to all non-HOV types.

Just closing relevant lanes alone did not necessarily encourage HOV eligible

vehicles to use the HOV lane. The DA in VISSIM needs to identify the HOV and

General Purpose (GP) lanes as two separate routes in order to assign a realistic proportion

of HOV eligible vehicles on the HOV lane/route. Since I-66 EB network is characterized

by HOV lanes that are not barrier separated from GP lanes, the only way to make

VISSIM identify it as separate path/route was by defining separate connectors and adding

appropriate costs for these connectors. Hence separate connectors were defined between

the upstream and downstream links consisting of the HOV lane while separate connectors

were defined for the same links consisting of the GP lanes.

A separate link cost coefficient was assigned to each vehicle type. Thus, based on the

general cost computed using the cost coefficients and the link/connector costs assigned in

the network, vehicle route choice became available in VISSIM. During the verification

process undesired lane change behavior by HOV eligible vehicles was often observed. As

vehicles moved from upstream link to downstream links, it was observed that the HOV

eligible vehicles used the GP lane connector rather than using the HOV lane connector,


14/92

7

causing congestion at several nodes. To correct for this undesired vehicle movement,

costs were introduced to GP lane connectors. Assigning cost coefficient to HOV

vehicle type alone (assigning 0= to all other vehicle types) made the GP connector

costs applicable to only HOV vehicles. Thus, magnitudes of the cost coefficients and

connector costs were chosen in a manner that made the path with the HOV lane as the

minimum-cost route available to HOV-type vehicles. The magnitudes of the costs and the

coefficient values assigned in the model were solely based on watching animations for

avoiding unrealistic lane change behavior by HOV eligible vehicles. Based on watching

animations, assigning 500= for HOV vehicle type was suitable for realistic lane

change behavior. Table 1 lists the cost coefficients associated with each trafficcomposition and

Table 2 summarizes the connector costs defined in the network.

Table 1.Cost Coefficients for each Traffic Composition

Traffic Composition/ CostCoefficients

GP HV HOV

1 1 1

0 0 0 0 0 500

Table 2.Connector Costs

Connector Type Associated Costs

GP Lane Connector 5/ mile

HOV Lane Connector 0/ mile


15/92

8

For this study, it is assumed that the cost coefficient values and connector costs

were well calibrated based on watching animations. Thus, these local parameters were

not considered during the entire network model calibration.

For I-95 and I-395 which feature Reversible High Occupancy (RHOV) lanes,

similar strategy of closing and opening links and lanes was used. In this case HOV 3+

vehicles were eligible to use the HOV lanes during the peak hours for each direction (NB

during morning peak, and SB during evening peak).

Table 3 lists the HOV schedule for the freeways in consideration.

Table 3. HOV Schedule for Corridors in NOVA Network

Freeway/Direction HOV Eligibility Hours of Operation

I-66 EB (Outside Beltway) HOV 2+ 5:30 9:30

I-66 EB (Inside Beltway) Only HOV 2+ 6:30 9:00

I-66 WB (Outside Beltway) HOV 2+ 15:00 19:00

I-66 WB (Inside Beltway) Only HOV 2+ 15:30 18:30

I-95 and I-395 NB HOV 3+ 6:00 9:00

I-95 and I-395 SB HOV 3+ 15:30 18:00

1.4 Toll Roads and Stop Signs

SH 267 consists of toll roads and toll free roads. Most of the toll booths are

located on the on- and off-ramps while a few of them are located on the main lines. Stop

signs have been defined in the network to imitate the toll booths that have cash/credit

card service as they prevent multiple vehicle entrance at the same time. Dwell times for

these stop signs follow a normal distribution N (8, 1) for vehicles using Cash lanes. That

is, the time distribution has a mean of 8 seconds and standard deviation of 1. For vehicles

using Smart tag lanes, the stop signs follow a normal distribution N (3, 1). Furthermore,

reduced speed areas were defined on certain lanes of SH 267. Table 4 gives the details of

the speeds and deceleration values used for these lanes.


16/92

9

Table 4.Speed and Deceleration values used for lanes on SH 267

Name of the RoadNumber of Dedicated

Smart Card lanes

Minimum

Speed (mph)

Maximum

Speed (mph)

Deceleratio

n (ft/s2)

DTR 7 30 40 6.562

Dulles Greenway 4 7 15 6.562

It must be noted that the speed and deceleration values have been assumed based

on the information available at the Toll road websites.

1.5 Driving Behavior Parameters Based on Link Type

In VISSIM, driving behavior is associated to each link by its link type. Though it

is possible to define a different driving behavior parameter set for each vehicle type

within the same link, it has been assumed in this project that rather than vehicle type,

these parameters are associated to the vehicle position in the network. For example, it

was observed that some on- ramps on the NOVA network had heavy demand during the

morning peaks and in reality the mainline vehicles on the right most lanes in the

acceleration/merge link tend to slow down and yield to the vehicles from the on ramp.

Thus, driving behavior of vehicles from the on-ramp may be different from those on the

mainline near the merge section.

In order to generate this behavior the on-ramp and acceleration links were defined

as separate type enabling to define and control driving behavior parameters in a manner

that would make the drivers from the on-ramps more aggressive than the ones on the

right most lane on the mainline. Some factors that could be adjusted to increase/decrease

aggressiveness of drivers are the -1 ft/s2 per distance reductions factor, accepted

deceleration, safety distance reduction factor (SDRF) which are available in VISSIM

under the Lane Change parameters of the Driving Behavior section. Based on assistance


17/92

10

from PTV America staff and test runs, it was observed that altering SDRF values

influenced the aggressiveness of the drivers, at least increased aggressiveness. By

defining different link types for the on-ramp acceleration lanes and lowering the SDRF

values for these links (to around 0.2 against the default value of 0.6), it was possible to

generate realistic merge behavior on the freeway.


18/92

11

Chapter 2: O-D Estimation

One of key elements in the microscopic simulation modeling process is to

estimate OD table. OD tables can be either obtained from conventional household

surveys or estimated using sensor counts from freeway mainlines and on- and off- ramps.

Given that the cost of collecting OD information from field is so expensive, the OD

tables for this project were estimated using sensor counts. However, since most of

available sensors were located on mainline freeway, there was a need to collect on- and

off-ramp volumes, especially where no sensors are available. Once the mainline and ramp

volume counts were obtained, QUEENSOD method was used in the estimation of OD

tables. For the NOVA project, it was required to look into a 15 hour time period (5 AM to

8 PM), the OD demands were estimated by each hour and a total of 15 OD tables were

developed for each freeway and direction.

2.1 Data Collection

The link volumes required to carry out the OD estimation for all the freeways in

the NOVA region were collected through the three main sources Detector information

through the STL Database; Video images accessed at http://vds.trafficland.com and

AADT data accessed from the VDOT website.

2.1.1 Data from Detectors

Mainline volume counts for the freeway were extracted from the STL database. In

order to do this, it was necessary to know the detector locations and their quality. Initial

queries were made to retrieve the location information of the detectors such as Interstate

Direction, Type of Link (Mainline/Off Ramp/On Ramp), and Milepost. For the purpose

of identifying good detectors, real time screening tests value were used [1, 2]. Real time


19/92

12

screening tests values are characteristic to each detector based on the volume and speed

data that the detector reports, giving a measure of whether the data reported by the

detector is reasonable or not. Table 5 summarizes the detectors that were identified as

good ones based on the initial queries.

Table 5.Detector Data Quality Summery by Interstate

Interstate Direction No of Reliable Mainline detectors/No of detectors available

EB 100/190I 66

WB 108/198

NB 72/139

SB 72/133I 95

Rhov 54/75

NB 4/59I 395

SB 4/57

As mentioned earlier, some of the ramp volume counts were collected from the

field through video images transmitted by the CCTV cameras administered and

controlled by the Northern Region Operations. These efforts are explained in the sections

that follow.

Once the location and quality of the detectors were known, the average values of

the six weekday volumes were retrieved from the database. The six weekdays used were

Tue Thurs over two weeks (i.e., May 8-10, 2007 and May 15-17, 2007). By identifying

the detectors available on these freeways, specifying the corresponding detector IDs and

time periods, simple queries in Oracle-SQL Plus were used to retrieve the required

detector information and traffic volume counts.

A sample query that was used to retrieve data from the STL database is as follows


20/92

13

spool C:\vidya\May\Stn1\496.txt

select t.time_string ||' '|| min(detectorid)||' '|| sum(volume) ||' '||

round(avg(volume),2) ||' '|| count (volume)

from nova.detector_flow df, timex t, calendar c

where

t.time_key between 500 and 2000

and df.detectorid = 496

and df.time_key = t.time_key

and df.calendar_key = c.calendar_key

and c.datex in ('8-May-2007','9-May-2007','10-May-2007','15-May-2007','16-

May-2007','17-May-2007')

and screening_tests not like '%0%'

and screening_tests not like '%9%'

group by t.time_string

/

spool close;

A list of the detectors with their IDs and corresponding locations that have been used for

the OD Estimation task of this project has been provided in Table 6, Table 8, Table 10,


21/92

14

Table 12,

Table 14 and

Table 15.


22/92

15

2.1.2 Data from Video Images

Volume data for the on/off ramps at locations characterized by low qualify

detector data was collected by accessing video images at http://vds.trafficland.com. These

videos were recorded and saved using Camtasia software. In order to take advantage of

the CCTV Cameras, time was first spent to identify the camera locations, camera IDs,

and to list the ones that would be helpful in the data collection. For this purpose two visits

were made to the Northern Region Operations TMC in Arlington to obtain detailed

information about the camera locations and the ramps that can be used in the data

collection.

The process of getting the CCTV cameras fixed at the desired locations and

capturing the images for the required time periods was a challenging task as it demanded

uninterrupted video capture and good image quality. Data collection was not possible on

several days in the months of Feb and March 2007 due to very poor video transmission

from most of the cameras. This issue was resolved by using the upgraded version of the

trafficland website (accessed at http://vds.trafficland.com) which was available from

the second week of April 2007. The upgraded system made streamlined videos available

for the cameras located in NOVA region. The volume counts from the video images were

reduced manually which was a time consuming task. Table 7, Table 9,

Table 11 and Table 13 list the locations and the time periods for which the

volume counts have been reduced from the CCTV cameras.


23/92

16

Table 6. I-66 EB Volume Data from Detectors

LocationLinkID

Type ofLink

StationNo Detector ID

Rte 29 - U 25 ML Stn 1 21-27; 37-43; 53-59; 69 -75; 101-107

Rte 29 - A 27 ML Stn 2 117-123

Rte 29 - D 29 ML Stn 3 126 -132

Rte 28 - A 31 ML Stn 4 144 -150

Rte 28 - D 33 ML Stn 5 155 -163; 173 179; 189 - 195

Rte 7100 - A 37 ML Stn 6 205-211; 715-721

Rte 7100 - D 45 ML Stn 7 235-241

Rte 50 - A 47 ML Stn 8 263-271

Rte 50 - D 51 ML Stn 10 302-308

Rte 243 - U 60 ML Stn 11 338-344

Rte 243 - A 61 ML Stn 12 617-623

Rte 243 - D 73 ML Stn 14 413-419; 421-427

Rte 7 - A 83 ML Stn 19 451-453

Sycamore - A 91 ML Stn22 455-457

G mason - A 95 ML Stn 23 466-468

Table 7. I-66 EB Volume Data from Videos

Location LinkID Type ofLink RampNo Time Period of data from videos

Rte 29 - D 28 On R 2 7:00 - 18:00

Rte 28 - U 30 Off R 3 9:00 - 15:00

Rte 28 - D 32 On R 4 9:00 - 15:00

Rte 50 - D 50 On R 11 6:00 - 8:00; 10:00 - 13:00; 14:00 - 15:00

Rte 123 - U 52 Off R 12 6:00 - 15:00; 16:00 - 18:00

Beltway - U 74 Off R 22 9:00 - 17:00

Beltway - A 76 Off R 23 9:00 - 13:00

Beltway - A 78 Off R 24 11:00 - 13:00

Rte 7 - D 84 On R 27 6:00 - 14:00; 15:00 - 18:00

Rte 110 - A 106 On R 37 6:00 - 18:00


24/92

17

Table 8. I-66 WB Volume Data from Detectors

LocationLinkID

Type ofLink

StationNo Detector ID

Before Rte 7 163 ML Stn 2 509-510

Rte 7 - U 174 On Det 1 538

Rte 7 - D 176 Off Det 2 537

Rte 243 - U 187 ML Stn 8 570-576

Rte 243 - D 199 ML Stn 12 625-631

Rte 123 - U 203 ML Stn 13 346-352

Rte 50 - D 212 Off Det 3 687

Rte 50 - D 213 ML Stn 18 679-681

Rte 50 - D 214 On Det 4 688

Rte 50 - D 215 ML Stn 19 243-251

Rte 7100 - D 221 Off Det 5 710

Rte 7100 - D 222 Aux Det 6 711

Rte 28 - U 225 ML Stn 23 702-708; 725-731

Rte 28 - U 226 ML Stn 24 197-203

Rte 28 - D 229 Off Det 741

Rte 28 - D 230 ML Stn 26 733-739

Rte 29 - U 232 ML Stn 27 134-142

Rte 29 - U 233 Off Det 7 751

Rte 29 - U 234 ML Stn 28 743-749

Rte 29 - U 235 On Det 8 762

Rte 29 - A 236 ML Stn 29 754-760

Rte 29 - D 237 On Det 9 752

Rte 29 - D 242 ML Stn 32 109-115

Table 9. I-66 WB Volume Data from Videos

Location Link IDType of

LinkRamp

No Time Period of data from videos

TR Bridge Entry 151 ML M 1 5:00 - 20:00TR Bridge - A 152 Off R 2 5:00 - 20:00

TR Bridge - D 153 ML M 3 5:00 - 20:00

Spout run - D 160 On R 4 9:00-12:00; 14:00 - 18:00

N Sycamore - U 165 ML M 5 10:00 - 18:00

N Sycamore - D 166 Off R 6 10:00 - 18:00

Rte 7100 - U 217 Off R 7 6:00 - 10:00; 12:00 - 18:00

Rte 234 - U 243 Off R 8 6:00 - 13:00

Rte 234 - D 247 On R 9 9:00 - 13:00


25/92

18

Table 10. I-95 NB Volume Data from Detectors


Link Station No Detector IDBeginning of

RHOV 1005Rhov ML

Stn 1 828

Rte 234 - U 859 ML Stn 2 813-815

Rte 234 - A 860 ML Stn 3 816-818

Rte 234 - A 861 Off Stn 4 1489

Rte 234 - D 862 ML Stn 5 1483-1485

Rte 234 - D 864 ML Stn 6 831

Rte 234 - D 865 On Stn 7 837

Rte 784 - U 867 ML Stn 8 840-841

Rte 3000 - U 887 ML Stn 9 893-895Rte 3000 - U 888 Off Stn 10 905

Rte 3000 - D 896 On Stn 11 914

Rte 123 - U 897 ML Stn 12 927-931

Rte 123 - A 899 ML Stn 13 943-944

Rte 123 - A 900 On Stn 14 946

Rte 611 - D 904 ML Stn 15 962-964

Rte 7100 - U 911 Off Stn 16 1032

Rte 7100 - A 912 ML Stn 17 1028-1030

Rte 7100 - A 913 On Stn 18 1033

Rte 7100 - D 916 ML Stn 19 1034-1036

Rte 7100 - D 917 On Stn 20 1038

Rte 644 - U 1019 Rhov Off Stn 21 1057Rte 644 - U 1020 ML Stn 22 1329-1335

Table 11. I-95 NB Volume Data from Videos


LinkRamp


Rte 642 - D 909 On R 26 7:00 - 8:00

Rte 644 - U 919,920 Off R 31 6:00 - 7:00


26/92

19

Table 12. I-395 NB Volume Data from Detectors


Link Station No Detector ID

Rte 648 - A 940 ML Stn 1 1147-1149

Rte 648 - D 941 On Stn 2 1182

Rte 236 - D 946 ML Stn 3 1161-1165

Rte 236 - D 947 On Stn 4 1816

Rte 420 - U 949 Off Stn 5 1586

Rte 420 - A 950 ML Stn 6 1181-1183

Rte 420 - D 951 On Stn 7 1820

Rte 7 - A 954 ML Stn 8 1184-1185

Rte 7 - D 957 On Stn 9 1829

Rte 402 - A 960 ML Stn 10 1200-1203

Rte 402 - D 965 On Stn 11 1833

Rte 120 - A 968 ML Stn 12 1204-1207

Rte 110 - U 1038 Rhov ML Stn 13 1617-1618

Rte 110 - U 1037 Rhov On Stn 14 1619

Table 13. I-395 NB Volume Data from Videos


LinkRamp


From I-495 934,935 On R 0 5:00 - 20:00

Rte 648 - A 939 Off R 2 5:00 - 7:00; 8:00 - 18:00Rte 120 - U 967 Off R 15 5:00 - 20:00

Rte 27 - U 978 Off R 19 5:00 - 10:00

Rte 27 - A 976 ML M 19-20 5:00 - 10:00

Rte 27 - A 1034 Rhov ML R 102 5:00 - 10:00

Rte 27 - D 972 Off R 20 7:00 - 13:00


27/92

20

Table 14. I-95 SB Volume Data from Detectors



Rte 644 - D 1416 Rhov Stn 1 1328

To Rte 617 1204 ML Stn 2 1063-1066

Rte 7100 - U 1205 ML Stn 3 1047-1050

Rte 7100 - A 1208 On Stn 4 1355

Rte 611 - U 1218 ML Stn 5 1405-1407

Rte 611 - U 1219 Off Stn 6 968

Rte 611 - A 1220 ML Stn 7 965-967

Rte 611 - D 1221 On Stn 8 1422

Rte 123 - U 1223 Off Stn 9 952

Rte 123 - U 1224 ML Stn 10 953-957

Rte 123 - A 1225 On Stn 11 1423Rte 123 - D 1226 ML Stn 12 1424-1426

Rte 123 - D 1227 On Stn 13 1427

Rte 123 - D 1228 ML Stn 14 933-937

Rte 3000 - U 1229 Off Stn 15 915

Rte 3000 - U 1230 ML Stn 16 910-912

Rte 3000 - D 1233 On Stn 17 1430

Rte 3000 - D 1234 ML Stn 18 896-899

Opitz Blvd - U 1237 Off Stn 19 889-890

Opitz Blvd - A 1245 ML Stn 20 883-885

Rte 784 - D 1246 ML Stn 21 865-866, 1570

Rte 234 - U 1440 Rhov On Stn 22 870

Rte 234 - U 1247 ML Stn 23 843-845Rte 234 - U 1249 Off Stn 24 836

Rte 234 - U 1250 ML Stn 25 833-835

Rte 234 - D 1252 ML Stn 26 1486-1488

Rte 234 - D 1253 On Stn 27 1490

End of Rhov 1443 Rhov Stn 28 829

Table 15. I-395 SB Volume Data from Detectors



Rte 7 - A 1159 ML Stn 1 1260-1263

Rte 420 - D 1164 On Stn 2 1882

Rte 236 - D 1172 On Stn 3 1889

Rte 236 - D 1416 Rhov Stn 4 1153

Rte 648 - A 1179 ML Stn 5 1283-1287

Rte 648 - D 1183 ML Stn 6 1293-1295- A: At the Interchange

- U: Upstream of the Interchange

- D: Downstream of the Interchange

Link ID: Used in the QueensOD estimation input to assign links

Station No and Ramp No: Used for denoting a location

Detector ID: IDs as per the STL database


28/92

21

2.1.3 Extrapolation of Missing Data

The data could be retrieved from the database to cover the time period 5 AM to 8

PM. The data collected from the detectors and video images in several cases did not

cover the entire time period. For these cases, volume counts were extrapolated from short

term counts to the entire time period by using the flow profiles of mainline, on- and off-

ramp volumes of those that covered the entire period. Figure 2, Figure 3 and Figure 4

show the flow profiles for the mainline, on-ramp and off-ramp of I-66 EB respectively.

0

1000

2000

3000

4000

5000

6000

7000

8000

0 2 4 6 8 10 12 14 16

Hour of the Day (5:00 AM - 8:00 PM)

Vehiclesp

erHour

Stn 1

Stn 3

Stn 6

Stn 7

Stn 5

Figure 2. I-66 EB Mainline Flow Profiles


29/92

22

0

500

1000

1500

2000

2500

3000

0 2 4 6 8 10 12 14 16

Hour of the day (5:00 AM - 8:00 PM)

Vehiclesperhour

On 4

On 2

On 7

On 8

On 11

On 13

Figure 3. I-66 EB On Ramp Flow Profiles

0

200

400

600

800

1000

1200

1400

1600

1800

0 2 4 6 8 10 12 14 16

Hour of the Day (5:00 AM to 8:00 PM)

VehiclesperHour

Off 1

Off 3

Off 9

Off 10

Off 14

Figure 4. I-66 EB Off Ramp Flow Profiles


30/92

23

Since the sections were fairly long, the variation in the flow profiles could be

attributed to the locations and the respective nature of demands. The obtained profiles

were categorized into sections. Thus, the freeway was divided into sections and the flow

profiles corresponding to a particular section of the freeway were used for the

extrapolation.

For example, the video counts for On-ramp 2 did not cover the time period 5:00

AM 7:00 AM (Figure 4). The flow profile of the On-ramp 4 (closest to On-ramp 2) was

used to extrapolate the missing data for the time period 5:00 AM 7:00 AM.

Table 16. Extrapolation of Missing Data

On-Ramp 2 (initial) missing missing 872On-Ramp 4 1483 1716 1605

Flow Profile of On 4 (based on 3rd time period) 0.923988 1.069159 1

On-Ramp 2 (Extrapolated) 806 932 872

The extrapolated values were further adjusted to balance the flows based on the

available mainline counts upstream and downstream of the corresponding ramps. Missing

data and extrapolated data for this particular case is shown in Table 16. Similar

extrapolation was done to obtain missing counts on the other sections.

The data collected through the detectors and videos did not cover all the links of

the NOVA network. To overcome these, AADT data published for year 2006 (accessible

at http://www.virginiadot.org/info/ct-TrafficCounts-2006.asp ) were used to supplement

and give an estimation of the volumes of other links. Once an estimate of the total

volume say x for a particular link was obtained for the 15 hour period from the AADT


31/92

24

data, the flow profiles were used to distribute this x for each hour within the 15 hour

time period.

2.2 O-D Estimation Using QUEENSOD

Van Aerde et al. [3] proposed the QUEENSOD model for generating dynamic

synthetic O-D matrices and also tested it on a 35 km section of Highway 401 in Toronto

and the Santa Monica Smart Corridor in Los Angeles. Many of the techniques developed

for estimating O-D matrices from link flow counts are based on very similar but slightly

different mathematical/statistical properties of the final matrix obtained. In many

practical applications, it is unclear as to how significant these mathematical/theoretical

intricacies are in view of the amount and quality of data that must be used as inputs.

The QUEENSOD model aims at finding an O-D matrix that minimizes the

discrepancies between estimated and observed link flows. For static O-Ds this approach

may be mathematically expressed as

( )aav

vvFa

,min (2)

subject to

10, =a

ij

ij

a

ijija ppTv

where

( )aa vvF , = function of the general error measurement between av and av ,

av = observed link flow in linka

av = estimated link flow in linka

ijT = estimated trips leaving zone i to zonej


32/92

25

a

ijp = proportion of trips from zone i to zonej traveling through linka,

i = origin,

j = destination, and

a = link identifier

The QUEENSOD model starts with a seed matrix (uniform or historic matrix) and

uses this seed matrix to estimate the link flows. Based on quantitative comparisons

between observed and estimated link flows, adjustments are made on the seed O-D

matrix. This involves adjusting the seed O-D matrix by calculating error correction

factors between the actual link volumes and estimated link volumes. The adjusted seed

O-D matrix is updated in the subsequent iterations to obtain new error correction factors

until the error between the actual link volumes and estimated link volumes are

minimized. A more detailed description of this model can be found in [4]. Hourly OD

matrices for each vehicle type (GP, HOV, HGV) were generated. Table 17 summarizes

some details on the OD Estimation for each freeway.

Table 17. Details of OD Matrices for each Freeway

Interstate Direction Vehicle Types included in OD matrix

I 66 EB GP, HOV 2+, HVs

I 66 WB GP, HOV 2+, HVsI 95 and I - 395 NB GP, HOV 3+, HVs

I 95 and I - 395 SB GP, HOV 3+, HVs

I 495 EB/WB GP, HVs

SH 267 EB/WB Cash (GP), E-Z Pass (HOV)GP: General Purpose

HOV: High Occupancy Vehicle

HVs: Heavy Vehicles


33/92

26

Estimation using QUEENSOD required preparing several input files which are

explained as follows.

Feasible OD Pairs This input file consists of the feasible OD pairs for a particular

corridor and each of the pair was assigned a unique number to be referenced by the

QUEENSOD during the estimation process

Minimum Path Trees This input file is related to the sequence of links that make up the

path for a particular OD pair. For example, if the path tree for a particular OD pair is as

follows.

6601-6606: 25 26 27 28

This denotes a path for the OD pair 6601 6606 and vehicles will take the path

consisting of the link sequence 25, 26, 27 and 28. Since the NOVA network did not have

multiple paths, it was fairly easy to come up with the path trees and these were done

manually.

Link Volume This input file consists of volume data for each link that will be used in

the OD estimation process by identifying the possible OD pair and the link sequence or

path tree for the same.

Once the required input files for the corridor was prepared, QUEENSOD

algorithm coded in MATLAB was used to come up with the corresponding OD tables.

QUEENSOD is an iterative method that tries to balance the link volumes across the paths

for the various OD pairs. The number of iterations used during the OD estimation process

was 100.


34/92

27

2.3 Measures of Effectiveness Data Collection

Field travel time data collected in January 9 through 11, 2007 and May 25

through 30, 2007 were prepared for sections on I-66, I-95 and I-395 of the NOVA

network and used for the calibration purpose. Detector counts and speeds for the

corresponding days in January and May 2007 were retrieved from the STL database and

used in an effort to look at multiple MOEs during the calibration process.


35/92

28

Chapter 3: Simulation Model Calibration

3.1 IntroductionThis chapter explains the application and results of the experimental design

approach using Latin Hypercube Sampling (LHS) method for calibration of a portion of

I-66 Eastbound of the NOVA network modeled in VISSIM. Figure 5 depicts the steps

that were performed in an effort to obtain most suitable combination of driving behavior

parameters for the developed model using experimental design procedure.

Figure 5. Flow Chart for Calibration Using Latin Hypercube Experimental Design

Simulation Model Setup

Initial Evaluation

Experimental Design(LHD)

Adjust Key ParameterRanges (such as SDRF)

Evaluation of the CalibratedParameter Set

(Carry out morereplications with selected

parameter set)

End

Unsatisfied

Satisfied

Yes

No

Satisfied

Parameter SelectionBased on Multiple MOEs(TT, Counts and Speeds)

SatisfactoryMOEs?

Unsatisfied


36/92

29

3.2 Latin Hypercube Sampling

Latin Hypercube Sampling (LHS) [5] is a statistical method that is used to

generate a distribution of plausible collections of parameter values from a

multidimensional distribution. A simple method to generate n combination sets of k

variables is to use random sampling. An alternate method to generate them would be to

use stratified sampling, which can characterize the population of each variable equally

well as random sampling with a smaller sample size.

In stratified sampling of a single variable X1, the distribution of X1 is divided into

m segments. The distribution of n samples over these segments is proportional to the

probabilities of X1 falling in the segments. Each sample is drawn from its segment by

simple random sampling and maximum stratification takes place when the number of

segments m equals the number of samples n required.

For a multi variable case (generating n combination sets of k variables X1,

X2Xk), an efficient sample is the one that is maximally stratified for all the variables

simultaneously. That is, the range of each variable is divided into n non-overlapping

intervals on the basis of equal probability, and one value from each interval is randomly

selected. The n values obtained for X1 are randomly paired with n values of X2. These n

pairs are then combined in a random manner with n values of X3 to form n triplets and so

on. Thus, nk-tuplets can be formed in this fashion (Latin Hypercube Samples). These

samples can be thought of as forming an input matrix of order (n x k) where the

combination set in the ith

row containing values for k input variables can be used as input

of ith run of a computer model.


37/92

30

A Two-Dimensional representation of One Possible Latin Hypercube Sample of

Size 5 for X1 and X2 is shown in Figure 6.

Figure 6.Two-dimensional representation of a LHS of size 5 for X1 and X2

3.3 Experimental Design Results

As a part of the project, the site and time period chosen for the initial calibration

efforts for the NOVA network is I-66 Eastbound from Rte 15, Gainesville to I-495

Beltway (just after Rte 243) from 5:00 AM to 8:00 AM. This is a 23.5 mile section of the

freeway that is heavily congested in the morning. During the remainder of this chapter,

this portion of I-66 Eastbound has been referred to as I-66 EB network. The network

alignment for I-66EB network is shown in Figure 7.


38/92

31

Figure 7. I-66EB Network

3.3.1 Simulation Model Setup

The 23.5 mile section of I-66 EB freeway was coded in VISSIM (a part of the

NOVA network coded in VISSIM), and the traffic data for the time period 5:00 AM

8:00 AM (O-D estimates using QUEENSOD method) were input into the model.

3.3.2 Initial Evaluation

In order to test if the default values for the driver behavior parameters in VISSIM

were sufficient to generate field conditions, 25 replications were conducted for the

network. The travel time simulation outputs based on 5 replications have been shown in

Figure 8, Figure 9 and Figure 10. Since the probe vehicles that were used to collect the

travel time from the field traveled on General Purpose (GP) lanes of the I-66 EB network,

the simulated travel times corresponding to GP vehicle type were used in the analysis.


39/92

32

The default parameters did not represent the field conditions, especially with the

3rd time period TT. Moreover, volume counts were low through out the simulation period.

Comparison of volume counts and speeds from simulations and detectors have been

presented in Figure 11, Figure 12, Figure 13 and Figure 14 for two stations namely

station 2 and station 10. Thus it was necessary to conduct the other steps proposed in the

calibration procedure. It must be noted that the first 15 minutes of the simulation was

considered as warm up time and MOEs were collected from 5:15 AM onwards, for every

15 minutes.

0

100

200

300

400

500

600

700

800

550 600 650 700 750 800 850 900

Travel Time (seconds)

Frequenc

y

Figure 8. Simulated TT Vs Observed TT (6:00-6:30 AM)-Default Parameters


40/92

33

0

50

100

150

200

250

300

550 600 650 700 750 800 850 900 950 1000 1050 1100 1150 1200 1250


Frequency


0

50

100

150

200

250

550

600

650

700

750

800

850

900

950

1000

1050

1100

1150

1200

1250

1300

1350

1400

1450

1500

1550

1600

1650


Frequency



41/92

34

0

200

400

600

800

1000

1200

1400

1600

1800

2000

2700 3600 4500 5400 6300 7200 8100 9000 9900 10800

Simulation time in seconds (5:30-8:00 AM)

Numberofvehicles Default_1

Default_2

Default_3

Default_4

Default_5

May 25th Det data

Jan 10th Det Data

Figure 11. Simulated Counts Vs Detector Counts for Station 2-Default Parameters

0

10

20

30

40

50

60

70

80

900 1800 2700 3600 4500 5400 6300 7200 8100 9000 9900 10800


Speedinmph

Default_1

Default_2

Default_3Default_4

Default_5

May 25th Det speed

Jan 10th Det Data

Figure 12. Simulated Speed Vs Detector Speed for Station 2-Default Parameters


42/92

35

0

200

400

600

800

1000

1200

1400

1600

1800

2700 3600 4500 5400 6300 7200 8100 9000 9900 10800


Numberofvehicles Default_1

Default_2

Default_3

Default_4

Default_5

May 25thDet data

Jan 10th Det Data

Figure 13. Simulated Counts Vs Detector Counts for Station 10-Default Parameters

0

10

20

30

40

50

60

70

900 1800 2700 3600 4500 5400 6300 7200 8100 9000 9900 10800


Speedinmph

Default_1

Default_2

Default_3

Default_4Default_5

May 25th det speed

Jan 10th Det Data

Figure 14. Simulated Speed Vs Detector Speed for Station 10-Default Parameters


43/92

36

Relative errors (based on 25 replications from simulation for default parameters) for

travel time speeds and counts have been summarized in Table 18.

Table 18. Relative Error for Simulations using Default Parameters

Relative Error Default

TT Error (3rd Time period) 1.35

Counts Error (Stn 2) 0.205

Speed Error (Stn 2) 0.36

Counts Error (Stn10) 0.156

Speed Error (Stn 10) 0.188

3.3.3 Initial Calibration Latin Hypercube Design (LHD) for Driving Behavior

The initial set of parameters that were identified as relevant to the performance of

the simulation model with their acceptable ranges are as follows:

1) Waiting time before diffusion (seconds): 30-90

2) Min. Headway (front/rear, meters): 0.1-0.9

3) Max. Deceleration Own vehicle Freeway link (m/s2): -5.00 ~ -1.00

4) Reduction Rate (meters per 1m/s2) Own vehicle Freeway link: 20-80

5) Accepted Deceleration (m/s2) Own vehicle Freeway link: -3.0 ~ -0.2

6) Max. Deceleration Trailing vehicle Freeway link (m/s2): -5.00 ~ -1.00

7) Reduction Rate (meters per 1m/s2) Trailing vehicle Freeway link: 20-80

8) Accepted Deceleration (m/s2) Trailing vehicle Freeway link: -3.0 ~ -0.2

9) Number of observed preceding vehicles: 1 5

10) Maximum look ahead distance (meters): 200 300

11) CC0: average standstill distance (meters): 1.0 2.0


44/92

37

12) CC1: headway at a certain speed (seconds): 0.5 3.0

13) CC2: longitudinal oscillation (meters): 0 ~ 15.0

14) CC3: start of deceleration process (seconds): -30.0 0

15) CC4: minimal closing v (m/s): -1.0 ~ 0

16) CC5: minimal opening v (m/s): 0.0 ~ 1.0

17) CC6: dv/dx (10-4

rad/s): 0.0 ~ 20.0

18) CC7: car following activities b (m/s2): 0.0 ~ 1.0

19) CC8: acceleration behavior when starting (m/s2): 1.0 ~ 8.0

20) CC9: acceleration behavior at v ~ 80 km/h (m/s2): 0.5 ~ 3.0

21) Speed Index 1 (mph): 1 ~ 3 (55 ~ 65, 65 ~ 72.5, 65 ~ 75)

22) Speed Index 2 (mph): 1 ~ 3 (60 ~ 70, 65 ~ 72.5, 65 ~ 75)

23) Speed Index 1 (mph): 1 ~ 2 (65 ~ 75, 65 ~ 72.5)

24) Max. Deceleration Own vehicle Ramp Merge Link (m/s2): -5.00 ~ -1.00

25) Reduction Rate (meters per 1m/s2) Own vehicle Ramp Merge Link: 20-80

26) Accepted Deceleration (m/s2) Own vehicle Ramp Merge Link: -3.0 ~ -0.2

27) Max. Deceleration Trailing vehicle (m/s2) Ramp Merge Link: -5.00 ~ -1.00

28) Reduction Rate (meters per 1m/s2) Trailing vehicle Ramp Merge Link: 20-80

29) Accepted Deceleration (m/s2) Trailing vehicle Ramp Merge Link: -3.0 ~ -0.2

Parameters 21, 22 and 23 set desired speed distributions along the I-66 EB

network based on their locations where three different posted speed limits are present.

These were indexed for the convenience of experimental design. Thus, speed index 1 and

2 have 3 options to define the desired speed distribution on the freeway where the posted


45/92

38

speed limits are 55 mph and 60 mph respectively. Speed index 3 has two options to

define the desired speed distribution where the posted speed limit is 65 mph.

The initial ranges for the lane change and car following parameters were obtained

from [6]. Definitions of these parameters and their functioning in VISSIM can be

obtained from [7]. As mentioned earlier, the Safety Distance Reduction Factor (SDRF)

defined for the links plays an important role in altering the aggressiveness of the drivers

especially near the ramp merge areas. Based on the initial test runs for the I-66 EB

network, the SDRF values for the freeway link and ramp merge link were fixed to 0.6 and

0.2 respectively during the experimental design.

3.3.3.1 Experimental Design Latin Hypercube Sampling

300 combination sets/samples of the 29 parameters were generated using Latin

Hypercube Sampling coded in MATLAB.

3.3.3.2 Multiple Runs

Five replications for each of the 300 cases were conducted in VISSIM, for a total

of 1,500 runs. For these runs, the aggregated travel times for specific sections were

collected. From the five replications for each case, the weighted average travel time

(based on the number of vehicles) for each case was calculated for three different time

periods: 6:00 AM 6:30 AM, 6:30 AM 7:00 AM, 7:30 AM 8:00 AM. Simulated

counts and speeds for station 2 and station 10 were compared with those from the

detector.

3.3.3.3 Parameter Set Selection

The weighted average travel time for GP vehicles (based on number of vehicles

passing the section) over the 5 replications for each parameter set was calculated. The


46/92

39

absolute error of this weighted average against the field travel time for each of the 3 time

periods was calculated. Similarly, for volume counts and speeds the average of absolute

error (between 6:00-8:00) over the 5 replications was calculated. Normalization for the

three measures was done by taking logarithm of these absolute errors. Thus, sum of the

logarithms of the error in travel time for each time period; logarithm of the average

absolute error in counts and for each station was determined for comparison.

Based on looking at the errors for all the MOEs together, the sample case C98

was selected from the LHD samples. An X-Y plot of the weighted average travel time of

GP vehicles against the Sample case is shown in Figure 15.

0

500

1000

1500

2000

2500

0 50 100 150 200 250 300

Sample Case

SimulatedTT(WeightedAveragefor5:00-8:00AM)

Figure 15.Simulated TT Vs Sample Case

C98


47/92

40

Figure 16 shows the variation of the simulated TT with the key parameter CC1

which has a significant impact on the capacity and hence the performance of a network.

0

0.5

1

1.5

2

2.5

3

3.5

0 500 1000 1500 2000 2500

Simulated TT in seconds (Weighted Average for 3 hour time period)

CC1

(seconds)

Figure 16. Simulated TT Vs CC1

25 replications for sample case C98 were carried out and the relative errors

between the simulated and field measures have been summarized in Table 19.

Table 19.Relative Error for Simulations using C98 and Default Parameters

Relative Error C98 Default t-test (p value)

TT Error (3rd timeperiod)

0.247 1.35 7.4 * 10 -27

Counts Error (Stn 2 ) 0.119 0.205 1.4 * 10 -20

Speed Error (Stn 2) 0.15 0.36 2 * 10 -15

Counts Error (Stn 10) 0.09 0.156 8 * 10 -25

Speed Error (Stn 10) 0.09 0.188 5 * 10 -7

C98


48/92

41

It must be noted that the t-test (p value) only show that the distributions of the

measures obtained for C98 and default parameters are significantly different. However,

the t-test results do not prove that C98 is a better parameter set for the model. By

comparing the distributions of the measures obtained from simulation (using sample case

C98) with those obtained from the field, it was found that the sample case C98 was far

better than the default parameters. Based on the 5 replications for sample case C98,

Figure 17, Figure 18 and Figure 19 depict the travel time distribution from simulations

and field observations. Figure 20, Figure 21, Figure 22 and Figure 23 depict the count

and speed data from simulations and field.

0

100

200

300

400

500

600

700

800

550 600 650 700 750 800 850 900


Frequency

Figure 17. Simulated TT Vs Observed TT (6:00-6:30 AM)-C98 (LHD_1)


49/92

42

0

50

100

150

200

250

300

350

400

550 600 650 700 750 800 850 900 950 1000 1050 1100 1150 1200 1250


Frequency


0

50

100

150

200

250

300

350

400

550 600 650 700 750 800 850 900 950 1000 1050 1100 1150 1200 1250


Frequency



50/92

43

0

200

400

600

800

1000

1200

1400

1600

1800

2000

2700 3600 4500 5400 6300 7200 8100 9000 9900 10800


Numberofvehicles C98_1

C98_2

C98_3

C98_4

C98_5

May 25th Det data

Jan 10th Det Data

Figure 20. Simulated Counts Vs Detector Counts for Station 2 C98 (LHD_1)

0

10

20

30

40

50

60

70

80

900 1800 2700 3600 4500 5400 6300 7200 8100 9000 9900 10800


Speedinmph

C98_1

C98_2

C98_3C98_4

C98_5

May 25th Det speed

Jan 10th Det Data

Figure 21. Simulated Speed Vs Detector Speed for Station 2 C98 (LHD_1)


51/92

44

0

200

400

600

800

1000

1200

1400

1600

1800

2700 3600 4500 5400 6300 7200 8100 9000 9900 10800


Numberofvehicles C98_1

C98_2

C98_3

C98_4

C98_5

May 25thDet data

Jan 10th Det Data

Figure 22. Simulated Counts Vs Detector Counts for Station 10 C98 (LHD_1)

0

10

20

30

40

50

60

70

900 1800 2700 3600 4500 5400 6300 7200 8100 9000 9900 10800


Speedinmph

C98_1

C98_2

C98_3

C98_4C98_5

May 25th det speed

Jan 10th Det Data

Figure 23. Simulated Speed Vs Detector Speed for Station 10 C98 (LHD_1)


52/92

45

Table 20 draws a comparison of the parameter set selected in the LHD with those

of the default values.

Table 20. C98 and Default Parameters

LHS C98 DefaultDiffusion 56.9 60

Min Headway 0.355 0.5

Max Decel -Own (F) -2.38 -4

Reduction Rate-Own(F) 46.7 200

Accepted Decel-Own

(F) -0.275 -1

Max Decel-T (F) -4.62 -3

Reduction Rate-T (F) 74.5 200

Accepted Decel-T (F) -1.194 -0.5

#obs veh 4 2

Max Lookahead 253.5 250

CC0 1.768 1.5

CC1 0.729 0.9

CC2 5.225 4CC3 -16.25 -8

CC4 -0.01167 -0.35

CC5 0.01167 0.35

CC6 4.9 11.44

CC7 0.948 0.25

CC8 3.718 3.5

CC9 2.3375 1.5DS1 3 -

DS2 2 -

DS3 1 -

SDRF (F) 0.6 0.6

Max Decel-Own (M) -1.5 -4

Reduction Rate-Own

(M) 30.5 200

Accepted Decel-Own

(M) -0.7579 -1

Max Decel-T (M) -2.2733 -3

Reduction Rate-T (M) 56.3 200

Accepted Decel-T (M) -0.6433 -0.5

SDRF (M) 0.2 0.6

F Freeway Section, M Ramp Merge Section


53/92

46

It was found that the driving behavior parameters from sample C98 generated

field conditions with respect to travel times for I-66WB, I-95 and I-395 as well. Since no

field measures were available for I-495 and SH267, animations of the simulations using

C98 as driving behavior parameter set were watched. Thus, this particular combination

set was chosen as calibrated set for the NOVA network.


54/92

47

Chapter 5: Measures of Effectiveness from the Calibrated Model

In order to obtain base measures of effectiveness for each corridor of the NOVA

network, 25 replications with the calibrated driving behavior parameter set were made.

The following lists the measures that were obtained from the simulation runs.

5.1 Counts and Speeds

For each corridor, simulated counts and speeds were obtained for every 15

minutes and for certain stations that were defined in the VISSIM station. These stations

have been listed in Table 1. The speed values obtained from the simulations are measured

in mph.


55/92

48

Table 21. Stations defined for Simulated Counts and Speeds

Station Number Route/Direction Location

1 I-66 EB Rte 234

2 I-66 EB Rte 50

3 I-66 EB Beltway (I-495)

4 I-66 EB TR Bridge

5 I-66 WB TR Bridge

6 I-66 WB Beltway (I-495)

7 I-66 WB Rte 50

8 I-66 WB Rte 234

9 I-95/I-395 NB Rte 619/Triangle

10 I-95/I-395 NB Dale City

11 I-95/I-395 NB I-495 Interchange12 I-95/I-395 NB Rte 27

13 I-95/I-395 SB Rte 27

14 I-95/I-395 SB I-495 Interchange

15 I-95/I-395 SB Dale City

16 I-95/I-395 SB Rte 619/Triangle

17 I-495 EB/SB Rte 191

18 I-495 EB/SB I-66 (Beltway)

19 I-495 EB/SB I-95/I-395

20 I-495 EB/SB Rte 1

21 I-495 WB/NB Rte 1

22 I-495 WB/NB I-95/I-39523 I-495 WB/NB I-66 (Beltway)

24 I-495 WB/NB Rte 191

25 SH 267 EB Rte 7

26 SH 267 EB Dulles Airport Access Rd (DAAR)

27 SH 267 EB I-66

28 SH 267 WB I-66

29 SH 267 WB Dulles Airport Access Rd (DAAR)

30 SH 267 WB Rte 7

5.2 Travel Time and Delays

Based on the travel time sections defined, VISSIM calculates the time taken (in

seconds) by vehicles to cross the particular section for the time interval specified. The

travel time sections defined for each corridor are listed in Table 22. Based on the travel


56/92

49

time sections defined in VISSIM, the delays (in seconds) for every chosen time interval is

calculated.

Table 22. Travel Time Sections Defined in the Network

Travel Time Section Number Route/Direction Location

1 I-66 EB Rte 234 to Rte 50

2 I-66 EB Rte 50 to Beltway

3 I-66 EB Beltway to TR Bridge

4 I-66 WB TR Bridge to Beltway

5 I-66 WB Beltway to Rte 50

6 I-66 WB Rte 50 to Rte 234

7 I-95/I-395 NB Rte 619 to Dale City

8 I-95/I-395 NB Dale City to I-495

9 I-95/I-395 NB I-495 to Rte 27

10 I-95/I-395 SB Rte 27 to I-495

11 I-95/I-395 SB I-495 to Dale City

12 I-95/I-395 SB Dale City to Rte 619

13 I-495 EB/SB Rte 191 to I-66

14 I-495 EB/SB I-66 to I-95/I-395

15 I-495 EB/SB I-95/I-395 to Rte 1

16 I-495 WB/NB Rte 1 to I-95/I-395

17 I-495 WB/NB I-95/I-395 to I-6618 I-495 WB/NB I-66 to Rte 191

19 SH 267 EB Rte 7 to DAAR

20 SH 267 EB DAAR to I-66

21 SH 267 WB I-66 to DAAR

22 SH 267 WB DAAR to Rte 7

5.3 Density

Density for certain links were obtained on each corridor. The location and

corresponding link IDs for which densities were collected have been shown in

Table 23.


57/92

50

Table 23. Link IDs/Locations for Density

Link ID Route/Direction Location

2005 I-66 EB Rte 234

1601 I-66 EB Rte 50

1517 I-66 EB Beltway (I-495)

1326 I-66 EB TR Bridge

1325 I-66 WB TR Bridge

1513 I-66 WB Beltway (I-495)

1605 I-66 WB Rte 50

1687 I-66 WB Rte 234

1504 I-95/I-395 NB Rte 619/Triangle1383 I-95/I-395 NB Dale City

944 I-95/I-395 NB I-495 Interchange

1948 I-95/I-395 NB Rte 27

1959 I-95/I-395 SB Rte 27

945 I-95/I-395 SB I-495 Interchange

1390 I-95/I-395 SB Dale City

1501 I-95/I-395 SB Rte 619/Triangle

732 I-495 EB/SB Rte 191

84 I-495 EB/SB I-66 (Beltway)

900 I-495 EB/SB I-95/I-395

1871 I-495 EB/SB Rte 11851 I-495 WB/NB Rte 1

905 I-495 WB/NB I-95/I-395

61 I-495 WB/NB I-66 (Beltway)

742 I-495 WB/NB Rte 191

714 SH 267 EB Rte 7

477 SH 267 EB Dulles Airport Access Rd (DAAR)

133 SH 267 EB I-66

134 SH 267 WB I-66

446 SH 267 WB Dulles Airport Access Rd (DAAR)

713 SH 267 WB Rte 7

25 replications for each corridor were made with the calibrated parameter set for

the 15 hour time period (5AM to 8PM) and the measures (counts, speeds, travel times,

delays and densities) were obtained for every 15 minutes. The average over these

replications for each corridor was calculated and has been presented as base measures of

effectiveness in Appendix A.


58/92

51

Chapter 6: Conclusions and Recommendations

The development of microscopic based traffic simulation model for the NOVA

Freeway system using VISSIM involved three main tasks: network coding, data

collection and O-D estimation and calibration. During the development of the model a lot

of time was spent on data collection and calibration efforts. Based on project study, the

following conclusions were made

(1) Default parameters in VISSIM did not simulate realistic behavior for the network

considered.

(2) Use of travel time alone as a measure for evaluating parameter sets from the

experimental design led to the selection of model parameters that did not generate

realistic outputs for other measures such as counts and speeds.

(3) The Safety distance reduction Factor (SDRF) that influenced the aggressiveness of the

drivers on the acceleration links near the on-ramps played a crucial role in generating

realistic ramp merge behavior. Since the network considered in this study consisted of

several on-ramps with high volumes, this parameter became crucial for the networks

performance.

Based on this study, the application of experimental design approach using Latin

Hypercube Sampling is recommended for the calibration of microscopic simulation

models. Use of multiple measures for evaluating the parameter sets from the

experimental design is also recommended.


59/92

52

References

[1] Real-time screening tests recommended for the NOVA detector data, STL DatabaseDocuments

[2] Turner, S., Margiotta, R., and Lomax, T.Monitoring Urban Freeways in 2003:

Current Conditions and Trends from Archived Operations Data. December 2004.

[3] Van Aerde, M., B. Hellinga and G. MacKinnon. QUEENSOD: A Method for

Estimating Time Varying Origin-Destination Demands For Freeway

Corridores/Networks. Presented at Annual TRB Meeting, Washington D.C., January

1993.

[4] Park, B. B. and I,Yun. Development of ITS Evaluation Test-Bed Using Microscopic

Simulation City of Hampton Case Study. Research Report No. UVACTS-15-0-45,

2003.

[5] Wyss, D. G. and K. H. Jorgensen.,A Users Guide to LHS: Sandias Latin hypercubeSampling Software. Sandia National Laboratories, Albuquerque, 1998.

[6] VISSIM User Manualversion 4.30. PTV Planung Transport Verkehr AG,

Karlsruhe, Germany, 2007.

[7] Park, B. and J. Won,Microscopic Simulation Model Calibration and Validation

Handbook, Virginia Transportation Research Council, June 2006.


60/92

53

Appendix A

MOEs for I-66 EB Table 24.Simulated Counts for I-66 EB

1 2 3 4Time Period/

Station Number Mean StDev Mean StDev Mean StDev Mean StDev

5:15 1205.14 9.5 1119.86 11.21 962 18.33 311.79 5.43

5:30 1326.86 70.57 1237.43 44.55 1057.07 49.75 578.36 10.55

5:45 1196.36 93.38 1059.79 38.34 1014.21 50.77 592.07 12.82

6:00 1177.21 91.11 1087.36 79.32 1028.86 43.75 584.64 13.9

6:15 1277.93 79.77 1199 71.34 1220.5 63.08 674.79 26.79

6:30 1237.21 88.18 1409.29 59.9 1426.36 69.03 649.29 31.76

6:45 1217.57 88.46 1460.79 96.38 1336.79 90.69 648.57 30.28

7:00 1202.71 69.93 1532.5 73.89 1346.86 94.64 633 26.69

7:15 1279.29 77.73 1422.79 80.91 1416.29 97.17 635 19.28

7:30 1256.36 99.62 1381.71 59.09 1359.79 90.79 589.07 20.687:45 1230 91.52 1331.57 49.21 1363.43 95.15 551.14 20.69

8:00 1263.93 84.92 1314.57 94.48 1335.43 90.87 538.57 26.4

8:15 1123.21 87.74 1372.36 79.91 1328.71 77.16 561.71 21.59

8:30 1214.21 124.67 1329.21 74.42 1220.07 63.75 684.79 33.02

8:45 1260.57 86.25 1338.07 29.02 1300.29 72.09 718.71 16.87

9:00 1269.5 79.33 1302.86 32.07 1251.93 51.34 724.57 20.71

9:15 1149.21 63.22 1331.57 29.58 1250.79 45.31 723.29 37.97

9:30 1182.43 82.35 1308.64 48.17 1259.36 45.06 706.64 26.52

9:45 1218.64 99.04 1292.86 58.53 1200.71 39.11 703.71 35.1

10:00 1261.29 94.02 1152.29 40.78 968.43 47.95 734.71 26.21

10:15 999.2 9.5 1073.4 9.5 1211.12 18.33 414.84 5.43

10:30 1028.3 70.57 1168.4 70.57 1427.43 49.75 689.94 10.55

10:45 1069.2 93.38 1184.65 93.38 869.46 50.77 682.63 12.82

11:00 963.4 91.11 1074.33 91.11 846.88 43.75 709.8 13.9

11:15 1037.5 79.77 1179.5 79.77 969.98 63.08 647.4 26.79

11:30 925.6 88.18 1225.65 88.18 717.54 69.03 677.43 31.76

11:45 915.4 88.46 1092.6 88.46 869.98 90.69 703.2 30.28

12:00 886.6 69.93 1160.3 69.93 966.5 94.64 674.25 26.69

12:15 920.33 77.73 1100.6 77.73 727.43 97.17 692.74 19.28

12:30 872.6 99.62 1067.2 99.62 970.65 90.79 643.93 20.68

12:45 876.6 91.52 1003.1 91.52 821.54 95.15 695.94 20.69

13:00 804.7 84.92 1093.2 84.92 808.21 90.87 605.62 26.4

13:15 871.7 87.74 1067.5 87.74 1053.76 77.16 659.54 21.59

13:30 806.4 124.67 1039.6 124.67 1276.53 63.75 619.8 33.02

13:45 830.7 86.25 1041.1 86.25 1177.02 72.09 600.5 16.8714:00 899.6 79.33 1057.65 79.33 895.81 51.34 602.4 20.71

14:15 848.3 63.22 1076.7 63.22 1097.3 45.31 657.2 37.97

14:30 825.5 82.35 1079.9 82.35 1117.6 45.06 665.8 26.52

14:45 956.3 99.04 1066.4 99.04 1267.5 39.11 579.23 35.1

15:00 863.6 94.02 1157.6 94.02 1320.9 47.95 609.76 26.21

15:15 912.2 9.5 1106.22 9.5 1255.3 18.33 626.45 5.43

15:30 881.6 70.57 1039.67 70.57 1241.2 49.75 593.92 10.55

15:45 799.2 93.38 1048.6 93.38 1335.4 50.77 596.64 12.82

16:00 902.24 91.11 1052.21 91.11 1211.33 43.75 639.56 13.9

16:15 859.5 79.77 1078.65 79.77 1299.44 63.08 644.76 26.79

16:30 892.6 88.18 1096.32 88.18 1387.32 69.03 698.93 31.76

16:45 865.6 88.46 1116.6 88.46 1435.71 90.69 587.53 30.28


61/92

54

17:00 865.2 69.93 1042.3 69.93 1283.51 94.64 597.92

17:15 914.5 88.46 1107.6 70.57 1281.42 90.69 617.64 9.5

17:30 870.5 69.93 1062.77 93.38 1450.64 94.64 609.83 70.57

17:45 965.1 77.73 1121.8 91.11 1413.32 97.17 623.84 93.3818:00 837.6 99.62 1071.23 79.77 1277.84 90.79 662.55 91.11

18:15 918.4 91.52 1116.6 88.18 1134.64 95.15 639.74 79.77

18:30 818.6 84.92 1008.7 88.46 1142.84 90.87 629.84 88.18

18:45 792.55 87.74 1011.8 69.93 1218.99 77.16 561.64 88.46

19:00 811.78 124.67 1030.2 77.73 1086.94 63.75 590.34 69.93

19:15 765.65 86.25 908.8 99.62 1214.94 72.09 593.77 77.73

19:30 625.65 79.33 854.9 91.52 1149.64 51.34 609.83 99.62

19:45 664.67 63.22 844.2 84.92 1282.83 45.31 632.53 91.52

20:00 642.76 82.35 843.65 70.57 1304.92 45.06 588.66 84.92

Table 25.Simulated Speeds for I-66 EB

1 2 3 4Time Period/

Station Number Mean StDev Mean StDev Mean StDev Mean StDev

5:15 65.94 1.08 62.47 1.65 57.99 1.09 47.30 0.06

5:30 60.94 2.81 43.98 6.28 56.79 1.45 46.16 1.03

5:45 62.38 3.22 14.18 1.01 57.56 1.35 46.56 1.33

6:00 62.11 3.85 13.99 1.36 57.29 1.48 46.24 2.59

6:15 60.53 3.81 15.65 1.26 44.01 1.41 46.66 1.18

6:30 61.44 3.22 19.44 1.36 36.84 3.45 45.51 3.03

6:45 60.76 3.56 21.69 1.94 29.19 9.82 45.89 2.44

7:00 60.96 3.04 26.53 4.31 25.46 9.13 46.11 1.51

7:15 57.26 8.71 32.22 10.74 24.69 7.14 45.09 2.84

7:30 58.62 9.81 38.66 6.34 21.86 6.87 23.81 23.827:45 60.54 3.49 36.63 8.63 19.56 4.29 46.41 2.12

8:00 59.34 3.01 26.31 8.62 18.47 2.09 46.65 1.35

8:15 62.36 3.52 24.1 5.05 17.53 1.54 45.80 2.74

8:30 62.29 2.34 35.32 8.25 15.88 1.01 44.97 2.21

8:45 61.24 2.48 47.66 6.87 18.07 2.02 42.94 3.20

9:00 61.13 2.6 45.39 5.54 16.72 1.23 42.26 2.80

9:15 62.78 1.25 41.17 7.56 16.59 1.22 43.23 2.11

9:30 63.16 1.87 41.85 8.6 16.86 1.19 41.24 3.37

9:45 62.56 1.87 42.36 10.58 15.59 1.08 41.14 3.73

10:00 62.93 1.69 30.49 15.81 12.05 0.89 42.98 3.04

10:15 68.1 1.48 63.1 1.36 45.6 4.29 47.2 2.44

10:30 67 1.41 37.1 1.26 37.5 2.09 46.9 1.51

10:45 67.2 3.45 17.3 1.36 10.2 1.54 47.1 2.8411:00 68 9.82 16.4 1.94 10.9 1.01 47 23.82

11:15 67.1 9.13 18.8 4.31 11.3 2.02 47 2.12

11:30 67.6 7.14 18.5 10.74 8.4 1.23 47 1.35

11:45 67.8 6.87 43.7 6.34 10.8 1.22 46.9 2.74

12:00 67.9 4.29 61 8.63 12.4 1.19 47.1 2.21

12:15 67.5 2.09 59.5 8.62 8.9 1.08 47.1 3.20

12:30 68.1 1.54 64.6 5.05 11.6 0.89 47 2.80

12:45 68 1.01 64.9 8.25 9.9 4.29 47 2.11

13:00 68.3 2.02 61.1 6.87 10.8 2.09 47.1 3.37

13:15 68.2 1.23 60.5 5.54 13.5 1.54 47.1 3.73

13:30 67.9 1.22 65.3 7.56 17.2 1.01 47 3.04


62/92

55

13:45 67.4 1.19 63.8 8.6 14.4 2.02 47 2.44

14:00 67.6 1.08 64.4 4.29 10.5 4.29 47.1 1.08

14:15 67.5 0.89 60.4 2.09 13.1 2.09 47 2.81

14:30 67.7 1.48 65.2 1.54 13.8 1.54 47.1 3.2214:45 67.9 1.41 65 1.01 17.9 1.01 47.2 3.85

15:00 68.3 3.45 46.6 2.02 18.5 2.02 47.1 3.81

15:15 67.8 9.82 62.2 1.23 17.6 1.23 47.1 3.22

15:30 68.2 9.13 65 1.22 16.9 1.22 47.1 3.56

15:45 68.3 7.14 63.6 1.19 17.7 1.19 46.9 3.04

16:00 67.9 6.87 65.8 1.08 16 1.08 46.9 8.71

16:15 68.1 4.29 65.2 0.89 18.5 0.89 46.9 9.81

16:30 68 2.09 64 4.29 18.2 4.29 47.1 3.49

16:45 68 4.29 63.6 2.09 19.7 2.09 47 3.01

17:00 68.3 2.09 61.6 3.22 17 1.54 47.2 3.52

17:15 68.1 1.54 65 3.8