Comparative study on simulation performances of CORSIM and VISSIM for urban street network Daniel(Jian) Sun a,⇑ , Lihui Zhang b , Fangxi Chen c a State Key Laboratory of Ocean Engineering, School of Naval Architecture Ocean and Civil Engineering, Shanghai Jiao Tong University, No. 800 Dongchuan Road, Shanghai 200240, China b School of Transportation and Logistics, Dalian University of Technology, No. 2 Linggong Road, Ganjinzi District, Dalian 116024, China c Education Development and Community Office, Shanghai Jiao Tong University, No. 800 Dongchuan Road, Min-Hang District, Shanghai 200240, China article info Article history: Received 2 January 2013 Received in revised form 16 May 2013 Accepted 17 May 2013 Available online 15 June 2013 Keywords: Micro-simulation model Urban transportation network Comparative study Signal intersections Sensitivity analysis abstract With the progress of simulation technologies, many transportation simulation packages were developed. However, little information is available to the users in applying these models to the most appropriate situations, or even seldom with the simulation accuracy of the individual model. This study conducts a comparative analysis of two popular simu- lation models (VISSIM and CORSIM), based on their simulation performances on an urban transportation network. Road network and field traffic data from North Bund, Hongkou District, Shanghai, China were used as the simulation background and input. Sensitivity analysis was carried out to compare the performance of both models based on four key indices, namely software usability, average control delay, average queuing length, and cross-sectional traffic volume. Advantages of each simulator were identified based on com- parison analyses of simulations with different levels of congestion and intersection geospa- tial scales. The main performance difference was found lying in the default parameter configuration within the models, including driver behavior settings, traffic environment settings, and vehicle types, etc. Consequently, it was recommended that analysts should choose their appropriate tools based on intersection type and level of saturation within the simulation case. Ó 2013 Elsevier B.V. All rights reserved. 1. Introduction As an important branch and application of traffic engineering studies, traffic simulation has become one major tool in testing the operational performance of transportation facilities and evaluating planning strategies [1,2]. Micro-simulation packages model individual driver for their behavior and decision making procedure, so as to approximate road traffic con- ditions. Consequently, they are more applicable on small urban area or special transportation facility studies [3,4]. The tech- nology has wide applications to provide new solutions to urban transportation problems, because of its extraordinary advantages in cost effective, safety, reproducibility, and easiness of use. In general, microscopic simulators can be divided into commercial products and the ones for laboratory research [5]. Of those for commercial purposes or similar, CORSIM, VISSIM, AIMSUN, and PARAMICS are the most important ones [2,3,5,6]. Particularly, the first two are more widely used in China, comparing to the latter ones. However, with the increasing diversity of the micro-simulation packages and their per- formance discrepancy on driver/vehicle behavior modeling, traffic analysts have rather difficulties in choosing an appropri- ate simulation platform. This study conducts a technical comparative analysis of CORSIM and VISSIM. The comparison is 1569-190X/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.simpat.2013.05.007 ⇑ Corresponding author. Tel.: +86 13918769316; fax: +86 21 34206674. E-mail address: jiansun@ufl.edu (Daniel(Jian) Sun). Simulation Modelling Practice and Theory 37 (2013) 18–29 Contents lists available at SciVerse ScienceDirect Simulation Modelling Practice and Theory journal homepage: www.elsevier.com/locate/simpat
Transcript
Simulation Modelling Practice and Theory 37 (2013) 18–29
Contents lists available at SciVerse ScienceDirect
Daniel(Jian) Sun a,⇑, Lihui Zhang b, Fangxi Chen c
a State Key Laboratory of Ocean Engineering, School of Naval Architecture Ocean and Civil Engineering, Shanghai Jiao Tong University, No. 800 DongchuanRoad, Shanghai 200240, Chinab School of Transportation and Logistics, Dalian University of Technology, No. 2 Linggong Road, Ganjinzi District, Dalian 116024, Chinac Education Development and Community Office, Shanghai Jiao Tong University, No. 800 Dongchuan Road, Min-Hang District, Shanghai 200240, China
a r t i c l e i n f o
Article history:Received 2 January 2013Received in revised form 16 May 2013Accepted 17 May 2013Available online 15 June 2013
With the progress of simulation technologies, many transportation simulation packageswere developed. However, little information is available to the users in applying thesemodels to the most appropriate situations, or even seldom with the simulation accuracyof the individual model. This study conducts a comparative analysis of two popular simu-lation models (VISSIM and CORSIM), based on their simulation performances on an urbantransportation network. Road network and field traffic data from North Bund, HongkouDistrict, Shanghai, China were used as the simulation background and input. Sensitivityanalysis was carried out to compare the performance of both models based on four keyindices, namely software usability, average control delay, average queuing length, andcross-sectional traffic volume. Advantages of each simulator were identified based on com-parison analyses of simulations with different levels of congestion and intersection geospa-tial scales. The main performance difference was found lying in the default parameterconfiguration within the models, including driver behavior settings, traffic environmentsettings, and vehicle types, etc. Consequently, it was recommended that analysts shouldchoose their appropriate tools based on intersection type and level of saturation withinthe simulation case.
� 2013 Elsevier B.V. All rights reserved.
1. Introduction
As an important branch and application of traffic engineering studies, traffic simulation has become one major tool intesting the operational performance of transportation facilities and evaluating planning strategies [1,2]. Micro-simulationpackages model individual driver for their behavior and decision making procedure, so as to approximate road traffic con-ditions. Consequently, they are more applicable on small urban area or special transportation facility studies [3,4]. The tech-nology has wide applications to provide new solutions to urban transportation problems, because of its extraordinaryadvantages in cost effective, safety, reproducibility, and easiness of use. In general, microscopic simulators can be dividedinto commercial products and the ones for laboratory research [5]. Of those for commercial purposes or similar, CORSIM,VISSIM, AIMSUN, and PARAMICS are the most important ones [2,3,5,6]. Particularly, the first two are more widely used inChina, comparing to the latter ones. However, with the increasing diversity of the micro-simulation packages and their per-formance discrepancy on driver/vehicle behavior modeling, traffic analysts have rather difficulties in choosing an appropri-ate simulation platform. This study conducts a technical comparative analysis of CORSIM and VISSIM. The comparison is
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mainly focused on simulation performance, in terms of four selected key measuring indices (i.e. software usability, averagecontrol delay, average queuing length, and cross-sectional traffic volume), thus to provide guidelines in selecting micro-sim-ulation models for urban arterials traffic operational analyses.
The remainder of the paper is structured as follows: Section 2 summarizes literatures related to the overall performanceand vehicular behavior within the two micro-simulation models (CORSIM and VISSIM). Next, an experimental case studywas carefully designed in Section 3, and various simulations were conducted with the field data collected from North Bund,Hongkou District, Shanghai, China. Section 4 presents the comparative analyses and findings based on the results from multi-ple runs of simulation in CORSIM and VISSIM, in terms of four selected measures of effectiveness (MOEs). Finally, conclusionsand recommendations for future work are provided in Section 5.
2. Literature review on traffic simulation models
As two widely used micro-simulation packages, CORSIM and VISSIM are perhaps more similar than they are different.Bloomberg and Dale [7] compared the two models on specific measures, such as throughput and intersection level of service(LOS), and found the predictions from the both models were similar, while both are different from the HCM predictions.Especially, for congested intersections with complex geometrics, the micro-simulation models are more appropriate thanthe HCM methodology. However, only general qualitative comparison was documented in the study, with no detailed quan-titative impacts provided.
The main differences found between CORSIM and VISSIM are in vehicular and driver behavior, primarily in the car-fol-lowing and gap acceptance logic [7,8]. As it is widely studied, vehicular movements in the simulation models were decidedby car following model in longitudinal and lane-changing model transversely [9–11]. Car-following models deal with thetime and space relationships of two consecutive vehicles in the same lane, and control the motion (e.g. time headway) ofthe lag car [12–14]. In CORSIM, each simulated driver type has an ideal safety distance, and the distances between vehiclesare updated for every simulation time step. In general, aggressive drivers would accept a much smaller gap than their defen-sive counterparts. On the contrary, VISSIM implemented Widemann psycho-physical car following model (1974 and 2000).Whenever the lag driver notices the gap is smaller than the psycho (safety) distance, he/she would brake and decelerate. Thespeed of the lag vehicle reduces until it becomes lower than that of the lead vehicle. However, due to uncertainty involved indeciding the exact speed of the lead vehicle, the procedure would last until the gap between the two vehicles attains anotherpsycho (safety) distance, and then the lag driver starts to accelerate again [15,16].
Lane changing affects the distribution of vehicles across lanes [17–19]. Compared to car-following models, in which thebehavior of the lead vehicle is relatively unaffected by the lag one, the lane changing process depends on many scenarios andparameters (such as merge, weaving), and hence it is more complex [7]. In CORSIM, the maneuvers are divided into manda-tory lane changing, discretionary lane changing, and random lane changing [20]. The maneuver-related parameters includemaximum deceleration rate, average lane changing duration, minimal acceptable gap, etc. On the contrary, VISSIM dividedlane changes into necessary lane changing (to reach the next connecting road of a scheduled route) and free lane changing(because of more room/higher speed). The behavior parameters include the maximum acceptable deceleration for the sub-ject vehicle and the lag vehicle on the target lane, desired safety distance of the lag vehicle on the target lane (depending onthe speeds of both lag vehicle and subject vehicle).
Other recent comparative studies on microscopic traffic simulation models were mainly focused on the calibration and val-idation efforts [21,22]. For example, Abbas et al. [21] compared car-following models, including Gazis-Herman-Rothery (GHR),Gipps, Wiedemann, Intelligent Driver and velocity difference, by using the calibration effort to individual drivers with the nat-uralistic data, and found Wiedemann model and the velocity difference model showed consistency across a variety of drivers.
Park and Schneeberger [23], Park et al. [24] proposed an experimental design and genetic algorithm (GA) based parametercalibration procedure for simulation model calibration and validation. By using a coordinated actuated signalized network,the performance was evaluated by comparing the distribution of simulation outputs with an average value of field data. Thetravel time was used for calibration, while the maximum queue length measure was used for validation. The results indi-cated that the default parameters of both VISSIM and CORSIM simulation models were not able to replicate field travel times.Only by one (for CORSIM) or two (for VISSIM) trials of calibration, the simulation outputs of the calibrated parameters wereable to replicate field measured validation condition.
Yang et al. [22] calibrated parameters for four car-following models: Newell’s model, Pipes’ model, optimal velocity mod-el, and the linear GM model with vehicle trajectories provided from NGSIM. However, the emphasis is to demonstrate thatthe proposed calibration methods yield consistent results across different car-following models, without touching the appli-cation of these models in transportation operation and management.
In summary, with respect to microscopic simulation models on urban streets, the effectiveness of operation performancehas not been studied in much detail. Existing research focuses more on calibration effort of theoretical driver behavior mod-els, without guidelines for real world practical users. This paper focuses more on statistically rigorous comparisons of thetwo models, applied to the same network with field data to test roadways and intersections of different functional classifi-cations. Furthermore, a complete sensitivity analysis of performance measures (e.g., control delay, average queue length,etc.) was carried out based on varying demand volume levels, thus to provide recommendations in assisting analysts tochoose their appropriate simulation tools.
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3. Simulation experimental design and implementation
The simulation background of this study was selected as the North Bund Area, Hongkou District, Shanghai, China. As itsunique history and one of the key areas around Huangpu River, the area is known as the unique shipping service clusteringarea, with typical urban transportation characteristics. The daily traffic operation and management are especially complicatebecause of the location of seaport for international trade.
The study area is bounded by Haining Road and Zhoujiazui Road from north, East Daming Road from south, DalianRoad from east, and Wusong Road from west, with the entire area as an irregular rectangle. A sketch of the site is shownin Fig. 1. Field surveys were conducted to collect simulation input data on April 14, 2011, and May 10, 2011, respec-tively. Providing the complexity of data requirement, the first survey (on April 14) focused on static transportation infor-mation, including channelization of the intersections, road segment, bus station and schedules, and the average traveltime between selected OD for calibration purpose. The second survey was mainly focused on real time traffic and signalinformation, such as signal timings, intersection throughput, etc. Additionally, at intersection level, the entry link baseddelays, maximum and average queue length, and cross-sectional volume for entry links were also collected from the twostudying subjects – Xinjian Road & Tangshan Road Intersection (X & T Intersection), and Wusong Road & Haining RoadIntersection (W & H Intersection).
3.1. Design of simulation experiment
The network was coded both in CORSIM and VISSIM, according to the layout shown in Fig. 2. Simulation runs were per-formed with the same field survey data, such as road network, signal timings, and demand volumes. By input field surveydata, such as the road network, signal timings, demand volumes, simulations were run with the same input values. The se-lected key measures of performance were produced and compared, along with their field collected counterparts, so that thecapabilities of two simulators under various situations can be attained.
During the experimental design, in order to reflect the performance of both simulators in a more detailed way, we in-tended to use four different levels of input volumes. That is, based on the field collected traffic volume from the 13 exitsand entries, sensitivity analyses were conducted for four levels of input volumes, denoted as low volume, off peak volume,peak volume, and over-saturated volume. Then, different output for the key simulation indices were generated and analyzed.During the implementation, the field collected off-peak and peak traffic were used, while traffic volumes for the other twolevels were manipulated based on the survey data volumes, by subtracting 300, adding 300 (unit: pcuvph), respectively, asfollows:
� Situation 1: field traffic off-peak volume � 300, with a minimum value of zero;� Situation 2: field traffic off-peak volume;� Situation 3: field traffic peak volume;� Situation 4: field traffic peak volume + 300.
Fig. 1. Layout of the study area network in North Bund, Hongkou District, Shanghai, China.
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(b)Fig. 2. Control delays in (a) X & T Intersection, and (b) W & H Intersection.
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By randomly setting different simulation seeds, multiple runs of simulation were conducted. The minimal number runs ofsimulation was determined as eight, by setting level of confidence as 95%, and confidence interval as 3 times of the samplingstandard deviation. Consequently, ten runs of simulation were carried out for each situation, with the simulation period as3600 s, and interval as 200 s. For each situation, the average values for the selected key indices were obtained for furthercomparative studies.
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3.2. Selection of key simulation indices
Considering the objective of the study, along with the availability of various MOEs from each simulator, four indices, forthe comparative analysis, were chosen as software usability plus other three intersection level measures, namely control de-lay, average queue length, and cross-sectional volume.
3.2.1. Software usabilityFor the two simulation application software, the content of usability includes network edibility, model calibration effort
and simulation output measurements. Both for beginners and professional simulation engineers, this index has great signif-icance in saving the practice time acquired for proficiency, improving efficiency, and expediting commercial promotionprogress.
3.2.2. Average control delayControl delay is an important MOE for efficiency and level of service at urban intersections, which not only reflects the
effectiveness of signal control and traffic designs, but also reports travelers’ perceived impedance and quality of service, en-ergy consumption and environment protection. Consequently, accurate replicate of control delay is an important task of ur-ban intersection simulation.
3.2.3. Average queuing lengthQueuing at intersections means the decrease of average travel speed, lower transportation network efficiency and level of
service, which indirectly infers the reduction of vehicle travel efficiency and even the loss of economics. Studies on queuingcharacteristics, mainly analyzing the queuing vehicle number or length and delay time, can assist to design queuing facility,evaluate intersection level of service and signal timings, etc. The output of simulation on queuing length includes averagequeuing length, maximal queuing length, and queuing vehicle stop times. This study selected the average queuing lengthas the key index, as it’s more meaningful comparing to others.
3.2.4. Cross-sectional traffic volumeVolume is one of the fundamental parameters in describing the characteristics of the traffic. Long time continuous obser-
vations on cross-sectional volume can help to understand traffic distribution on time, spatial variation, thus to provide nec-essary information for transportation planning, and traffic management and control. In addition, volume is a direct index toevaluate the production of transportation facilities, and the observed volume is the empirical result of demand, capacity andtraffic flow.
3.3. Network simulation and calibration
By completing road network construction, route choice strategies selection, signal phasing and timing configuration, andbus station and route setting, the background network was built within the two software, respectively. The network wasfirstly simulated using peak hour traffic (Situation 3) for model parameter calibration. The purpose is to tune up each effec-tive parameter based on the field traffic situations, so that the simulation model can replicate the real life, thus to minimizethe difference between simulation output and field measured values [23,24].
The main calibration variables in VISSIM include emergency stop distance, lane change distance, minimal space headway,average stop distance, and waiting lane changing duration, etc. The minimal space headway was selected as the calibrationparameter in this study, which represents the minimal distance to the lead vehicle in target lane during a lane-changingmaneuver. The default value in VISSIM is 0.5 m, while the field observation found a much larger value. The main model cal-ibration variables in CORSIM include lane change time, acceptable gap, acceleration/deceleration, vehicle normal decelera-tion, minimal time headway, free flow speed etc. The minimal time headway was selected for this study, as the default valueof minimal time headway is 1 s. in CORSIM, while the field observation found a much larger one. Calibration scheme for theselected parameters in both simulators are listed as follows:
� VISSIM Scenario 1: minimal space headway = 0.5 m;� VISSIM Scenario 2: minimal space headway = 1 m;� VISSIM Scenario 3: minimal space headway = 2 m;� CORSIM Scenario 1: minimal time headway = 1 s;� CORSIM Scenario 2: minimal time headway = 1.5 s;� CORSIM Scenario 3: minimal time headway = 2 s.
During the calibration, this study used the route average travel time as the control parameter. The field value was col-lected from the preselected route on May 10, 2011, peak hours (6 pm), starting from Dalian Road & Zhoujiazui Road, to Xinj-ian Road, East Daming Road, ending at East Daming Road & Wusong Road. The route (about 3.2 km) was intentionallyselected to include major arterials, such as Zhoujiazui Road, Xinjian Road, and East Daming Road etc., to pass typical inter-sections (total number = 4). Nine runs were driven, with the travel time collected as 1000100, 905000, 902400, 905600, 901500, 1000400,
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1002600, 904500 and 900400, respectively and then the average travel time was calculated as 904500 (585 s). The number of runs (9)was validated to satisfy the minimal requirement N P 1:962S2
e2 (N is the minimal num of runs, S2 is the variance calculated from9 runs, e is the error as the 5% of the average travel time, and the confidence level is set as 95%, corresponding to the criticalvalue of 1.96).
The initial network was simulated within each software package by using the average travel time as the calibration index.The objective is to adjust the default value of the selected calibration parameter(s), namely the minimal space headway in
Table 1Simulation Travel Time (T.T.) from the two simulators.
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VISSIM, and the minimal time headway in CORSIM, so that the overall performance of simulation replicates the real situation(i.e. within ±10% of the field measured values).
The simulated travel time for calibration purpose in both simulators were presented in Tables 1 and 2, respectively. InCORSIM, the travel time can only be obtained by link, and consequently, travel time for each link was obtained and accumu-lated along the route. For the default setting (minimal time headway = 1 s), the average route travel time is 477.48 s, with arelative error of 18.38% compared to field travel time (585 s). By increasing the value to 1.5 s, the overall route travel timeincreased to 569.76 s, with the relative error reduced to 2.61%. However, when the value increased to 2 s, the overall routetravel time becomes 665.37 s, with the relative error increased again (13.47%). Consequently, the correct minimal time head-way was deemed as 1.5 s.
The average route travel time in VISSIM was comparably easy to obtain. The travel times for the road segment included inZhoujiazui Road, Xinjian Road and East Daming Road were configured and generated directly after simulation. When usingthe default minimal space headway (0.5 m), the average travel time is 499.4 s, with a relative error of 14.64%. By increasingthe value to 1 m, the output total travel time becomes 579.9 s, which is very close to the field value, with a relative error ofonly 0.87%. However, when the minimal space headway was set to 2 m, the output total travel time comes to 686.8 s, with arelative error around 17.4%. Consequently, the minimal space headway was chosen as 1 m.
Occasionally, multiple parameters may have to be calibrated simultaneously. To this end, a regressive-orthogonal designmethod is generally adopted for large scale experiments [23]. Traffic flow, capacity, travel time, delay and queue length weregenerally included as dependant variables, while the calibration parameters were selected as independence variables, so thatlinear regression can be used to estimate the coefficients of the solution. As the calibration criteria (within ±10% of the fieldmeasured average travel time) in this study based on minimal space headway (for VISSIM) and minimal time headway (forCORSIM) were already met, no further complicated calibration experiment was carried out.
4. Analyses of simulation results
This section analyzes and compares the simulation results from CORSIM and VISSIM. Sensitivity analyses were conductedto study the simulation results with four levels of input traffic flow, as situations defined in Section 3.1. The performance ofsimulation was compared from perspectives of software usability, average control delay, average queuing length, and cross-sectional traffic volume. Studies on software usability compared entire simulation efforts on network edit, model calibration,and simulation output indices, while comparisons of the rest three indices were performed quantitatively based on simula-tion output values.
Considering the last three indices are mainly for urban signal intersections, two typical intersections were chosen fromthe study area: Xinjian Road & Tangshan Road Intersection (X & T Intersection), and Wusong Road & Haining Road Intersec-tion (W & H Intersection). The X & T Intersection lies interior of the study area, with small area and comparative low through-put (and low saturated), while the W & H Intersection is one major exit/entry of the study area. Both Wusong Road andHaining Road are major arterials with high traffic volumes, and the intersection also has several main transportation infra-structures, such as Shanghai Railway Station, and Suzhou River in the vicinity, thus is much larger scaled, with comparablelarger throughput traffic (high saturated as well).
4.1. Comparison analysis of software usability
The comparison of usability is mainly from three aspects, including network construction/edibility, model calibration ef-fort required, and availability of simulation output MOEs.
4.1.1. Network construction/edibility4.1.1.1. Network construction/edibility. In VISSIM, the network construction starts from link building, and then connected toeach other. Consequently, the layout of links should follow strictly on the base map. However network construction in COR-SIM is started from node building, and then connects the nodes with new generated links, which to some extent, reduces thenetwork editing efforts.
4.1.1.2. Phasing setting. The difference of phasing setting is rather large. VISSIM 4.3 sets the signal controller device first, andthen creates phasing and timing within each controller, and finally deploys the signal controller to the selected link. Signalphasing in CORSIM is comparable simpler and more straightforward, and all configuration operations are conducted withinthe intersection node properties, with a clear phasing selection procedure.
4.1.1.3. Bus station and route setting. In VISSIM, the editing of bus station and route only needs to select route start and endpoints, and then add the related route information on the network. However, in CORSIM, the route editing requires to inputall nodes and links passed by. The deployment becomes much more complicated for areas with many public transportationroutes covered.
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4.1.2. Network calibration effortThe calibration efforts of the two simulators were explained in Section 3.3. The basic procedures are similar, except for the
key calibration parameter selection. In fact, the calibration procedures of almost all mainstream micro-simulation packages,including PARAMICS, AIMSUN, etc. do not differ too much.
4.1.3. Measures of simulation outputAs to the output MOEs of simulation results, both VISSIM and CORSIM include almost all indices that micro-simulation
can output. Moreover, VISSIM provides the GUI animation, along with the simulation procedure, and then collects the pre-configured indices at the end of simulation. On the contrary, CORSIM generate simulation results first, and then output var-ious MOEs. The animation procedure of CORSIM simulation can be generated by TRAFVU module. Moreover, the overall sim-ulation speed of CORSIM is a little faster than that of VISSIM.
4.2. Comparison analysis of average control delay
Delay is one of the most important indices to measure the effectiveness and LOS of urban signal intersections. Variousdefinitions were provided for the travel time delays of signalized intersections, such as stop delay, control delay etc., inwhich only control delay fully accounts for any slow down caused by intersection signal control. Control delay is the vehicletravel loss time caused by the signal control, calculated as the difference between the intersection pass-by time with a nor-mal speed and real travel pass-by time [25]. It is defined as summation of stop delay and acceleration/deceleration (start uploss time) in HCM 2000 [25], and is a more accurate measurement of vehicular delay caused by intersection control.
In VISSIM, the delay is obtained whenever a vehicle is detected by a travel time detector despite of vehicle types. The con-trol delay in VISSIM is measured from one or multiple travel times, using real travel time to minus ideal travel time (with noother vehicles and signal control), calculated as the average value of all vehicle delays occurred within one or multiple roadsegments or intersections [24].
Starting from Version 5.0, CORSIM reports the control delay (sec/veh) as a new MOE, which is comprised of the followingthree components [20]:
� Stopped delay (Ds): the time lost whereas a vehicle is stopped in the queue waiting for green or waiting for its leader tomove forward;� Delay incurred while a vehicle is decelerating to stop at the stop bar or the end of the queue (Dd); and� Delay incurred while a vehicle is accelerating to gain its full operating speed after the signal indication turns green (Da).
Consequently, control delay in CORSIM is always smaller than the delay given in Webster equation, because the normaloperating speed of the vehicle is smaller than free flow speed especially when traffic volume is heavy [20].
Fig. 2 shows the comparison of average control delays from X & T Intersection and W & H Intersection, as well as the fielddata for validation. It was found that the CORSIM simulation result is closer to the field data in X & T Intersection, which iscomparably lower congested. However, at high congested W & H Intersection (see Fig. 2b), simulation results from VISSIMare more reliable. Especially, in Situation 4, the huge delay of north entry links is believed due to the reason that the link isone of the main entries of the study area. In summary of the different situations, VISSIM tends to enlarge the delay of smallintersections (low congested), while CORSIM generally has larger delays in large and high saturated intersections. Thesecould be related closely to the various vehicle behavior characteristics (such as lane-changing duration, driver types, andacceleration/deceleration), and the definition of driver behavior within the simulators, as different parameter settings affectintersection output delays substantially [26,27].
For each situation, the weighted average delay was calculated across all entry links to generate the delay trends for var-ious levels of traffic volumes, as shown in Fig. 3. It can be figured out that the increasing trend of VISSIM results is closer to
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Situation 2
VISSIM
Field Data
CORSIM
0
10
20
30
40
50
60
70
80
Que
uing
Len
gth
(# o
f V
eh.)
Situation 4
VISSIM
CORSIM
0
5
10
15
20
25
30
East West North South East West North South
East West North South East West North South
East West North South East West North South
East West North SouthEast West North South
Que
uing
Len
gth
(# o
f V
eh.)
Situation 3
VISSIM
Field Data
CORSIM
(a)
(b)Fig. 4. Average queue length in, (a) X & T Intersection, (b) W & H Intersection.
26 Daniel(Jian) Sun et al. / Simulation Modelling Practice and Theory 37 (2013) 18–29
that of the traffic demand than that of CORSIM results. Consequently, it is recommended to use CORSIM in studying delays ofsmall scale intersections, while VISSIM has more advantages on investigating delays occurred in large intersections.
A two-sided T test was performed to investigate whether the delays from two models are statistically different. First, theH0 and H1 hypotheses were provided as assume that for given level of confidence a = 0.10 (at 90% confidence level):
0
20
40
60
80
100
120
140
160
180
200
Tra
ffic
Vol
ume
(vhp
)Situation 1
VISSIM
CORSIM
0
50
100
150
200
250
300
350
400
450
Tra
ffic
Vol
ume
(vph
)
Situation 2
VISSIM
Field Data
CORSIM
0
100
200
300
400
500
600
Tra
ffic
Vol
ume
(vph
)
Situation 4
VISSIM
CORSIM
0
100
200
300
400
500
600
700
800
Tra
ffic
Vol
ume
(vhp
)
Situation 1
VISSIM
CORSIM
0
200
400
600
800
1000
1200
1400
Tra
ffic
Vol
ume
(vph
)
Situation 2
VISSIM
Field Data
CORSIM
0
200
400
600
800
1000
1200
1400
1600
Tra
ffic
Vol
ume
(vph
)
Situation 4
VISSIM
CORSIM
0
100
200
300
400
500
600
Tra
ffic
Vol
ume
(vph
)
Situation 3
VISSIM
Field Data
CORSIM
0
200
400
600
800
1000
1200
1400
1600
East West North South East West North South
East West North South
East West North South East West North South
East West North South
East West North South
East West North South
Tra
ffic
Vol
ume
(vph
)
Situation 3
VISSIM
Field Data
CORSIM
(a)
(b)Fig. 5. Cross-sectional traffic volume for, (a) X & T Intersection, (b) W & H Intersection.
Daniel(Jian) Sun et al. / Simulation Modelling Practice and Theory 37 (2013) 18–29 27
H0. the delays from two models are not statistically different;
H1. the delays from two models are statistically different.The critical T value is calculated l1�a
2¼ l0:95 ¼ 1:645, that is if the T-test value is larger than 1.645, we should reject H0,
and accept the alternative hypothesis that the delays from two software are statistically different. A detailed statistical com-parison was performed as shown in Table 2. The statistical results indicated that in all cases except for particular entry lanes
28 Daniel(Jian) Sun et al. / Simulation Modelling Practice and Theory 37 (2013) 18–29
(e.g. X & T Intersection, Situation 1 Eastbound traffic, Situation 4 Northbound traffic; H & W Intersection, Situation 1 East-bound traffic and Northbound traffic), the null hypothesis H0 was rejected, indicating the delays produced by the two sim-ulation software are statistical different.
4.3. Comparison analysis of average queuing length
This section compares the field measured average queuing length of different situations with the corresponding simula-tion outputs for the target intersections, thus to evaluates the performances of both simulation packages.
In VISSIM, the queue at intersections is defined from the queue counter location on the entry link to the last vehicle inqueuing state. If queue counters were placed in each lane of entry links, then all queuing vehicles can be recorded, so thatthe average queue length can be calculated. In CORSIM, the queue lengths were recorded from the start of simulation, untilthe end of each run. The simulated average queue lengths for different situations, along with the field measured value werecompared in Fig. 4.
It can be easily figured out that simulation results from both VISSIM and CORSIM have large difference from the field val-ues under real traffic demand situation (Situations 2 and 3). The trend of queue length from VISSIM is comparably closer tothe field values. Given the queue length measurement is affected largely by individual intersection properties and instanttraffic flow, rather large fluctuations were found. By studying simulation results from all four situations of traffic volumes,the queue lengths from VISSIM in total are closer to the field measured values. Consequently, the authors recommend usingVISSIM to simulate average queue length.
4.4. Comparison analysis of cross-sectional traffic volume
The field cross-sectional traffic volumes were counted manually during the data survey. In a similar way, VISSIM simu-lation obtained the cross-sectional volume of each entry link by setting data collection points at each corresponding link. InCORSIM, the dispatched vehicle number of each link was recorded, and consequently the cross-sectional traffic volume waseasily obtained by retrieving the vehicle numbers at each entry link. Fig. 5 presents the output cross-sectional traffic volumefor each situation within two intersections.
It was found that under both non-peak and peak traffic conditions (Situations 2 and 3), the output volumes of two sim-ulation software were close to each other. Further investigations on volume of each direction indicate that the outputs ofCORSIM from both intersections are closer to the field measured values. This may be due to the reason that CORSIM treatseach link as a unit in recording global volume, and consequently has more reliable results. However, in VISSIM the measure-ment was carried out through placing traffic counters manually, which may introduces errors because of inappropriate plac-ing location or devoid of traffic counters. Consequently, the authors recommend using CORSIM to obtain cross-sectionaltraffic volume for urban intersections.
In terms of level of congestions, differences between cross-sectional volumes under Situations 3 and 4 are small. This maybe due to the reason that both situations have traffic volume close to saturated condition, and when the traffic demand at-tains intersection capacity, the throughout during unit period does not change much. For low congested Situations 1 and 2,the trends of outputs of CORSIM are closer to the trends of traffic demand variation.
5. Conclusions and recommendations
In this research, we studied the two popular micro-simulation packages, VISSIM and CORSIM, and particularly comparedsimulation performance of the two micro-simulation software (CORSIM and VISSIM) based on the urban network in NorthBund area, Hongkou District, Shanghai, China. Conclusions were drawn in terms of four selected key indices as follows:
1. For the software usability, the network editing and signal configuration in CORSIM are easier. Due to different simulationmechanism, simulation results of CORISM can be output directly and fast, while VISSIM provides friendly configurationinterface with a separated output file for simulation results. For calibration effort, both software provide multiple calibra-tion parameters to enable the simulated network replicating the real situation. In summary, operations in CORSIM arecomparably more convenient, while VISSIM provides versatile indices for direct output and is believed to be more appro-priate for the beginners.
2. For the intersection average control delay, VISSIM is more appropriate for large intersections with high throughput traffic,while CORSIM is good at modeling unsaturated intersections. For example, in W & H Intersection, simulation results ofVISSIM are closer to the field measured values, and also change closely related to the input traffic demands. CORSIM ismore accurate in replicating small scale intersections (intersected by 2 s arterials), which is mainly caused by the prin-ciple of simulation models and default parameter settings.
3. For the average queue length, both software packages do not perform well, which may be caused by the large fluctuationsexisted in the field situation. Comparably, VISSIM has closer simulation results to the real situation. However, it wasbelieved this index can only be used as a reference index in the comparative analysis study.
4. For the cross-sectional traffic volume, both simulation outputs are close to the field data, while CORSIM is slightly better.
Daniel(Jian) Sun et al. / Simulation Modelling Practice and Theory 37 (2013) 18–29 29
Based on the analyses in terms of different key indices, under different levels of congestion, and for different scales ofintersections, it was concluded each simulator has its particular advantage in replicating real traffic. The main reason liesin the embedded simulation models and default parameter configuration, including driver behavior setting, traffic environ-ment setting, and vehicle type, etc. Consequently, researchers should choose their appropriate simulation tools based onintersection type and level of saturation. In particular, the calibration effort should be closely related to the simulation objec-tives, namely the four indices mentioned above. Although the results are promising, additional experiments should be con-ducted to improve model performance.
First, the model calibrations in this study were performed based on urban arterial empirical data using one single param-eter (minimal space headway for CORSIM, and minimal time headway for VISSIM). As a follow-up study, the calibration canbe extended to large scale multiple parameters calibration, in which the orthogonal design is generally adopted. A regressionequation may be constructed using the calibration parameters as independent variables, while various attributes, such asvolume, capacity, travel time, delay or queue length as dependant variables. Second, in addition to the selected key indices(e.g. delay, queue length and cross-sectional volume), other MOEs, such as total stops time, network average speed may beincluded. Finally, the main comparisons in this study were focused on urban intersections. Other facilities, such as unsignal-ized intersections or urban streets may also be investigated, which would form an important extension to the currentresearch.
Acknowledgement
This research was sponsored in part by the National Natural Science Foundation of China (No. 71101109), the ShanghaiPujiang Program (No. 12PJ1404600), and the Youth Fund of State Key Laboratory of Ocean Engineering (No. GKZD010059-29). The authors would like to express their appreciation to Mr. Yizheng Wu from School of Transportation Engineering, Bei-jing Jiaotong University, China, for his valuable effort and assistance in the data collection and analysis during this study.
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