Calibration of VISSIM for Shanghai Expressway Weaving Sections Using Simulated Annealing Algorithm

Jian SUN1, Zhizhou WU2 and Xiaoguang YANG3

1Doctor Candidate, Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University Siping Road 1239, Shanghai, China, 200092.PH (86-21) 6598-2275; FAX (86-21) 6598-1589; email:sunjian@126.com2Doctor, Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University Siping Road 1239, Shanghai, China, 200092. PH (86-21) 6598-4957; FAX (86-21) 6598-1589; email:wzzfwp@126.com3Professor, Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University Siping Road 1239, Shanghai, China, 200092. PH (86-21) 6598-2275; FAX (86-21) 6598-8372; email:yangxg@mail.tongji.edu.cn

Abstract

This paper presents the application of a simulated annealing algorithm as an optimization method for finding a suitable combination of VISSIM parameter values. Four weaving sections are simulated in VISSIM platform using field data coming from Traffic Information Collecting System (TICS) in Shanghai. Numerous simulation tests indicate the main parameters affecting simulation precision are Desired Lane-Change Distance (DLCD), Witting Time Before Diffusion (WTBD), and Wiedemann99 car-following parameters CC0 (the average desired distance between stopped cars), CC1 (the headway time (in s) that a driver wants to keep at a certain speed), CC2 (safety distance a driver allows before he intentionally moves closer to the car in front). The prepositional parameter combination of DLCD, WTBD, CC0, CC1 and CC2 is 300,90,1.5,0.8 and 3.50 for peak time and 200,45, 1.5,1.0 and 5.0 for midday off-peak traffic.

Introduction

Recently, urban expressway has sprung up in many Chinese cities, and the built-expressway proved to be much valid for solving the traffic problems. Weaving section is typical type to carry out merge and diverge in urban expressways;meanwhile it is usually a traffic congested bottleneck and incident spot and is characterized for intense lane-changing and complicated driver behavior. So analysis on weaving sections is helpful to decrease or eliminate the effect of bottleneck and enhance the highway capacity and service level.

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At present, study on expressway weaving sections is mainly based on three methods: regression analysis, empirical model and simulation model (CHEN et al, 2000). As the progress of computer science and technology, there are more and more field simulation model applications in foreign countries. Roger and Sutti (2003) utilized VISSIM and PARAMICS worked on a suburban interchange. Prevedrouros, P.D. and Y.wang (1999) used CORSIM, INTEGRATION and WATSim accomplished a large freeway/arterial network. Alexander Skabardonis (2002) worked on weaving sections in California using simulation model.

Microscopic simulation models contain numerous independent parameters to describe traffic control operation, traffic flow characteristics, and drivers’ behaviors. These models contain default values for each variable, but they also allow users to input a range of values for the parameters. Considering the road status, traffic demand and driver behavior have big differences between china and developed countries and most of the simulation parameters are determined according to the foreign situations and not always suit for our china, we need the simulation model parameters calibrated and validated. But few researches on weaving sections are conducted in china (CHEN et al, 2000). With the recent growth in computational resources and ITS data there is an opportunity for developing an automatic calibration methodology for traffic micro-simulation models that is based on optimization theory. In recent years genetic algorithms (Cheu et al. 1998, Lee,D. etal,. 2001), sequential simplex algorithm (Kim, K, 2003) have been studied by several researches.

In this paper simulated annealing algorithm is proposed as an appropriatetechnique for the calibration of traffic micro-simulation model. It has been widely used for optimizing systems containing factors (Aarts E H L et al, 1987. Kirkpatrick S. et al, 1983). The proposed approach will be illustrated on four typical weaving sections in Shanghai Expressways, China.

Characteristic of Traffic Flow in Weaving Sections

Weaving is defined as the crossing of two or more traffic streams traveling in the same general direction along a significant length of the roadway, without the aid of traffic control devices (Cheu, R.L., et al, 1998). Figure 1 is a typical weaving section in shanghai.

From the Figure 1 we can see the weaving vehicles need to change to the target lane in limited weaving length, it will influence all the vehicles in weaving sections and make the traffic flow unstable, headway increase, speed decrease and capacity deareas is characterized by intense lane-

F

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V1

V2

V3

V4

cline. The operation of expressway weaving changing and complicated driver behaviors.

ig. 1. A typical weaving section in shanghai

Computing in Civil Engineering 2005ivil Engineering (2005)

Because of the complexity of vehicle interactions operational problems may occur at weaving areas even when traffic volumes are less than capacity.

According to the definition in the Highway Capacity Manual 2000, there are three types (A, B or C) weaving in highway. The classification is based on the minimum number of lane changes that must be made by weaving vehicles as they travel through the section. Type A weaving sections require that both directions must make one lane change to successfully complete a weaving maneuver. Most of theweaving sections belong to type A in china urban expressways. Our research objects are four A weaving sections.

Simulation of Weaving Sections

VISSIM Simulation model

The simulation model used in this research was VISSIM, version 3.70. VISSIM is a microscopic, time step, and behavior based simulation model. Essential to the accuracy of a traffic simulation model is the quality of the actual modeling of vehicles or the methodology of moving vehicles through the network. VISSIM uses the psychophysical driver behavior model developed by Wiedemann.

Test Bed

Four major expressway weaving sections named BJWH, HHXJH, MMHS and JSKX were chosen for the application of the model. The BJWH and HHXJHlocated in “N-S” elevated road; the other located in “Yan’an” elevated road, they all belong to the “shen” expressway systems in Shanghai. We consider that these weaving sections can embody the typical traffic flow operating character in China.

Data Collection

Now ITS in shanghai is under construction. Through the ICICS (Inner City Information Collecting Systems) we can get the “Yan’an” elevated road real-time data including volume, speed, vehicle type, occupancy and headway for every lane at 20 seconds interval. We extracted one-week data for this research. Meanwh ile we saved one week peak time video information about “N-S” elevated road, through the TJVICS (TongJi Video Information Collecting Systems) we also can get the needed traffic information such as traffic volume, vehicle type, headway, speed etc, al (Jian SUN, et al, 2004). Table 1 shows the key geometric and traffic data for each testingsite. For the intention of calibration we extracted the speed of every lane. Near the road sideline lane is No 1.

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VISSIM Calibration Parameters

Each test site was coded into the VISSIM model, and then we selected the calibration parameters according to the characteristics of weaving sections traffic flow and practical experiences. The main parameters affecting simulation precisionare Desired Lane-Change Distance (DLCD), Witting Time Before Diffusion (WTBD), and Wiedemann99 car-following parameters CC0 (the average desired distance between stopped cars), CC1 (the headway time (in s) that a driver wants to keep at a certain speed), CC2 (safety distance a driver allows before he intentionally moves closer to the car in front).

Calibration Procedure Using Simulated Annealing (SA)

Simulated annealing algorithm is motivated by an analogy of the optimization problem to the statistical mechanics of annealing of solids. Simulated annealing has been widely used in solving large-scale combinatorial problems. In a 76 arcs traditional transportation network design problem with continuous decision variables, Friesz et al (1990) obtained a satisfactory result by employing a simulated annealing algorithm approach. Peter J. M. Van Laarhoven et al (1992) used simulated annealing

Table 1 Key Parameters o f Simulation Areas

WS N L V1 V2 V3 V4 SL1 SL2 SL3

958 515 401 92 42.4 45.2 45.9

3849 927 1805 164 34.7 34.3 32.8

3710 410 1320 73 38.2 39.8 41.7

1953 867 861 153 34.1 37.0 36.9

MHS 4 2354

2474 991 1106 175 34.1 36.2 32.2

783 582 276 103 39.5 40.1 44.8

2867 1527 790 270 18.9 15.2 14.9

2383 1624 812 287 16.5 15.5 16.7

2020 1319 566 233 31.5 32.9 36.8

J S

KX 3 1216

1653 964 497 170 36.9 37.2 40.6

4458 1360 1900 80 25.6 23.4 24.5BJ

WH6 550

6600 1100 900 100 18.6 15.3 16.9

3199 941 2072 167 35.4 38.6 40.3HH

XJH4 2420

5237 1045 2026 185 13.4 12.1 17.9

WS: Weaving Sites; N: Number of lanes in weaving section; L: length of weaving section (ft); V1: expressway-to-expressway volume (pcu/h); V2: expressway-to-ramp volume (pcu/h); V3: ramp-to-expressway volume (pcu/h); V4: ramp-to-ramp volume (pcu/h); SL1: average speed of lane 1 (mph); SL2: average speed of lane 2 (mph); SL3: average speed of lane 3 (mph). (From outerside to innerside the lane number increase).

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to find the minimum makespan in a job shop-scheduling problem. The algorithm successfully avoids the local minimum, though it is at the cost of large running time.Kang (1994) also used a simulated annealing algorithm in solving one-way street network design problem.

The basic simulated annealing algorithm steps is as follows:

STEP 0 Initialize starti , 0c , 0L : starti is the initial solution state; 0c a control

parameter (also called “temperature”) and 0L denotes maximum number of

transitions allowed under one specific control parameter. Let startii = .

STEP 1: Set L = 0

STEP 2: Create next solution state j from current solution state i .

)(),( ifjf is objective function value for the States j and

i .If]1,0[)

)(

)()(exp(

0

randorcc

ifjf

k

>−, then .1, +=← LandLji

STEP 3: If )( 0LLL k< go to STEP2, else update )( 0cck and )( 0LLk .

STEP 4: If stopping criteria is satisfied, stop; else go to STEP1.The software that performed this calibration may be divided into three

modules, the VISSIM, SA and control modules, respectively, as shown in Fig.2. The VISSIM module performed regular simulation runs based on the input parameters provided by the control module, through the VISSIM data input file. The control module is a program compiled in the Visual Basic language that provided data input and controlled the interaction between the VISSIM and SA modules.

Fig.2 Program Modules for VISSIM Calibration

Results and DiscussionsAll test sites were simulated using default parameters at peak and off-peak

time. The prepositional parameter combination of DLCD, WTBD, CC0, CC1 and

New Parameter

Objective Function Values

New TemperatureVISSIM Output

ControlModule

Simulated annealing(SA) algorithm

Modelue

VISSIMModule

(Simulation Runs)

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CC2 is 300,90,1.5,0.8 and 3.50 for peak time and 200,45, 1.5,1.0 and 5.0 for midday off-peak traffic. Limited to the paper size here we only selected the typical results.

Figure 3 shows the measured and predicted speeds from BJWH using the default parameters at off-peak time. Figure 4 shows the measured MMHS and predicted speeds from using the default parameters at off-peak time.

From the predicted speed inbetween measureerror (RMS) value

Fig.3 Measured vs. Predicted Speeds in Each Weaving Lane at Peak Time (17:00~18:00)—Default Model Parameters in BJWH.

Fig.4 Mea on-ramp L Parameter

Fig.5 MeaTime (17:00~

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sured vs. Predicted Speeds in Each Weaving Lane andane at Off-peak Time (11:00~12:00)—Default Model

figures we can find that there are big difference in measured and each lane. Especially at peak time the absolute average difference

d and simulated speeds is 21.8 percent, with a root mean square of 6.9. Other weaving sites have similar results.

s in MMHS.

sured vs. Predicted Speeds in Each Weaving Lane at Peak 18:00) — Calibrated Model Parameters in JSKX.

Computing in Civil Engineering 2005 Computing in Civil Engineering (2005)

Figure 5 calibrated paramspeeds from MM

From the agreement with tbetween measureerror (RMS) valmeasured and simvalue of 2.48.

Conclusions

VISSIM Shanghai. The aillustrated. Sevesummarized as fo

(1) The mCC0, CC1 and C

(2) The operations on ameasured and pcharacteristics an

(3) In geShanghai are modefault values in

(4) We neresearch we usedspeed in the traffi

Acknowledgmen

Fig.6 Me on-ramp L Parameter

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asured vs. Predicted Speeds in Each Weaving Lane andane at Off-peak Time (11:00~12:00)—Calibrated Model

shows the measured and predicted speeds from JSKX using the eters at off-peak time. Figure 6 shows the measured and predicted HS using the calibrated parameters at off-peak time.figure we can see the VISSIM predicted average speeds are in close he field measurements. In figure 4 the absolute average difference d and simulated speeds is 4.83 percent, with a root mean square ue of 1.72. In figure 5 the absolute average difference between ulated speeds is 1.10 percent, with a root mean square error (RMS)

was calibrated against field data collected through ICICS in pplication of SA as a parameter optimization technique has been ral important findings result from this calibration effort be llows:ain parameters affecting simulation precision are DLCD, WTBD,

C2. calibrated VISSIM model reasonably replicate observed traffic ll four real-word weaving sections. Good agreement between redicted values was obtained for all the combinations of design d traffic demand patterns.neral, the calibrated parameter values indicate that drivers in

re aggressive in lane changing and car following compared with the VISSIM.ed to pay more attention to the study of free-flow speed and in this it as an artificial “speed limit” that adjusts the overall operating c stream. Ulterior researches are going on.

ts

s in MMHS.

Computing in Civil Engineering 2005 Computing in Civil Engineering (2005)

This work was supported by the project of Natural Science Foundation of China (NSFC), Grant No.70122201/G0114.

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