VERIFICATION PROCESS AND ITS APPLICATION TO
NETWORK TRAFFIC SIMULATION MODELS
Ryota Horiguchi
Dr. Eng., President, i-Transport Lab. Co., Ltd.
2-12-404 Agebacho Shinjuku 162-0824 Tokyo Japan
Phone: +81-3-52613077, Fax: +81-3-52613077, E-mail: horiguchi@i-transportlab.jp
Masao Kuwahara
Professor, Institute of Industrial Science University of Tokyo
IIS-Building-C-Cw504 4-6-1 Komaba Meguro 153-8505 Tokyo Japan
Phone: +81-3-54526419, Fax: +81-3-54526420, E-mail: kuwahara@nishi.iis.u-tokyo.ac.jp

ABSTRACTThis paper summarizes a standardized verification process for network traffic simulation
models. After the general introduction of philosophy of verification, we explain detailed
processes of the verification and its application to several well-known simulation models.
“Verification” here means several examination tests of simulation models using virtual dataon a simple network so as to confirm their fundamental functions. In the course of model
development, the developers have to examine whether the model performance is consistent
with the specifications that they intend and also with the well-authorized traffic engineering
theory. Because of several constraints in putting the model specifications into the computer
programming such as discretizing of time and space and simplifying vehicle behaviors tosome degree, the intended model specifications may not be fully achieved in a computer.
Therefore, we strongly recommend the verification before applying the models to a real
network.

INTRODUCTION
Necessity of Standardized Verification
Nowadays, quite a number of network traffic simulation models are available in all over theworld. Sometimes, however, we face difficulties in understanding model performances.
For instance, a maximum discharge flow rate from a bottleneck link is not consistent with the
input link capacity, vehicles cannot be generated as the input OD data, merging and diverging
ratio cannot be achieved as we want, and so on. A part of the reasons would come fromincapability of users who do not know what parameters should be adjusted, but the causes
would also be found in modeling itself. In the course of model development, a model has tobe examined whether the performance is consistent with the specifications developers intend
and also with the well authorized traffic engineering theory. Because of several constraints
in putting the model specifications into the computer programming such as discretizing oftime and space and simplifying vehicle behaviors to some degree, the intended model
specifications may not be fully achieved in a computer.
Thus, we recommend the verification of models, where “Verification” means several
1

examination tests of simulation models so as to confirm their fundamental functions such as1) vehicle generation, 2) bottleneck capacity at simple road sections, 3) capacity of
merging/diverging areas, 4) traffic jam growing/shrinking with propagation of shock waves,5) capacity of left/right turn at an intersection, 6) drivers’ route choice behavior, and so on.
This verification is useful for model developers to confirm the model functions. For
systematic as well as overall tests, a standardized verification process is needed. Same time,the verification would be also helpful for model users to receive results of the verification to
get familiar with model characteristics as well as model parameters to be adjusted, since users
may frequently have difficulty in fully understanding the contents of the model from only theliterature and the manual. Many of users in fact have felt the hearts of models are “blackbox”.
One way to verify a model is to apply simulation models to real traffic data and to examine
their reproducibility. Although the examination using real field data is important, complex
traffic conditions in the real world would mostly get bogged down individual model functions.
Furthermore, the model performance would be different by ways of the application such asparameter adjustment and input data quality. Thus, we in principle suggest verifying themodels using virtual data on a simplest network so that we could examine each of thefunctions separately.

Stages of Model Development
In order to clarify the standpoint of verification, let us start with stages of the model
development. We consider the following five stages:
Framework
According to purposes of model applications, we have to first construct the model framework;that is, what the model input and output are, what kind of traffic phenomena should be
described, and so on. Then, we have to conceptually decide vehicle motions as well as
travelers’ behavior to be incorporated so as to reproduce the traffic phenomena concerned.
Specification
The model framework defined above should be specified in more detailed way. For instance,
we should decide how long the scanning interval is, and what kind of car-following models aswell as route choice models should be employed, etc.
Implementation
This process consists of programming to run the model contrived in the previous stage on acomputer and debugging to check if the computer operates according to the algorithm.
Debugging is just the fixing coding errors and it must be distinguished from verification
described in the next.
Verification
Verification is a series of simple tests to confirm that fundamental model functions areproperly programmed as in the specification. As mentioned earlier, the simulated result iscompared with what the result should be obtained from the well authorized theory. In order
to individually examine each of functions and also to get the theoretical solutions to becompared, we should use virtual data on a simplest network.

2

Validation“Validation” seems quite similar to “Verification” in general. However, in this paper,“Validation” must be clearly distinguished such that it is the evaluation of the model
specification using real field data. Even if the model is verified as in the specification, themodel specification (or the model framework) itself may not be adequate to describe real
traffic phenomena. The model cannot be practically applicable, if actual traffic situations are
not sufficiently reproduced due to the incomplete model specification. Furthermore, themodel performance as a system should also be confirmed, such as whether the execution of
the model can be finished within a practical computing time.

Japanese Activity for Standardization of Traffic Simulation
Although this paper mainly focuses on verification, we have been working for standardizing
several stages mentioned above. Our first outcome is the verification manual, which
describes the standard verification procedure so as to be applied to various different types ofmodels. The second output is the benchmark datasets, which are real field data relatively
well observed and processed. The benchmark datasets have been gathered so thatdevelopers can utilize them to validate their models, since in general acquisition of real fielddata is substantially expensive and time consuming.
These our activities can be found in “Clearing-House” at an Internet website:
http://www.jste.or.jp/sim/ (in Japanese).
The developers and users of simulation models can also publish their experiences onverification and validation through the clearing-house. Followings are brief introduction ofmaterials available on the clearing-house at present.
Draft Version of the Manual of Standard Verification Process for Traffic Simulation Models
This manual describes minimal verification items mainly for so-called network simulation
models [JSTE, 2001]. The subsequent chapter of this paper explains the main purport of theverification manual and an outline of the substantive verification steps.
Standard Benchmark Data Set for Validation of Traffic Simulation Models
Validation requires data on simulation inputs including traffic demand and operation as wellas highly reliable data representing the traffic situation to be compared with the simulation
result. Such data collection has been a substantial burden for model developers. With thisbackground, we have proposed the desirable validation process using common benchmark
data sets, which is collected from real world with highly reliable preciseness [Horiguchi,
1998]. Currently, five data sets for different categories of road networks like urban streets,
inner-city highways, etc. are available.

MANUAL OF STANDARD VERIFICATION PROCESS
Fundamental Concept
Verification is a sort of virtual test using ideal network and demand configurations to qualify
basic phenomena on road traffic. Generally, we do not expect a simulation model toreproduce real traffic situation perfectly, since some simplification in vehicle motions and3user behaviors have to be made and also discretization of time and space is required to somedegree. Accordingly, in the verification, the simulation result is not expected to exactly
agree with the theoretical results. When the simulated result is far different from the theory,
we should revise the programming. However, in most of the cases, the simulation would
perform slightly different way. In these situations, we should utilize the verification tounderstand the model characteristics. Establishing this linkage will provide us very helpful
information to understand the model characteristics. At the same time, it is considered
important to define the relationship between certain model parameters and model behavior.
We may now find two categories of network simulation models. One is that maintain traffic
flows with vehicle lists or fluid approximation according to the macroscopic flow
characteristics, like flow-density or density-speed relationship, given to each link or section.
Another type of simulation models is employing vehicle driving behaviors including carfollowing and lane changing. Here, let us say former as the Q-K type and latter as the C-F(car following) type.
The biggest difference of these two types is that the Q-K type explicitly, or implicitly, gives
the capacity to a link or a section, while the C-F type basically does not have a parameter
directly implies the capacity of link. As the concept of verification is to compare thesimulation model behavior with the established theory that is normally on macroscopic view,the detailed description of verification step would be separately defined for each category, i.e.:a) For the simulation models of the Q-K type, the verification implies that the simulation
result really agrees to the given flow characteristics. In this sense, the verification
process of this type is to be said as a kind of self-consistency check.
b) For the simulation models of the C-F type, the model parameters are concerning toindividual driver’s characteristic. Therefore, the verification process of this typeshould make clear the relationship between those model parameters and the flow
characteristic reproduced by the simulation. In this sense, we may say the verification
is a sort of sensitivity analysis. Although it is not possible and not efficient to examine
flow characteristics under all combinations of several model parameters, we should
understand the influence of major parameters on resulted flow conditions.

Features to be considered through Simulation
We have included six basic features in the verification manual, which should be at minimum
considered by the network simulation models.
Generation of Vehicles and Flow Conservation
For implementation of simulation, it is necessary to generate the traffic at the entry end
according to the arrival distribution of vehicles from outside the study area. Most of thesimulation models seem to assume random arrival at a network boundary section, but there
might be some other arrival patterns to be adopted by considering the objective of thesimulation study. For example, the uniform arrival may be assumed in some cases of theanalysis for over-saturated traffic conditions, in order to avoid the undesirable tendency ofpseudo-random series. The verification process here requires whether the generation patternassumed in the model really achieved. It should be also checked whether the number ofvehicles generated in a certain time period is equal to or different from the given volume.
Once a vehicle generated, it must not disappear until it reaches its destination.

4

Even in thecase that vehicle queue spills out of study area, newly generated vehicles are added to the endof the point queue outside the network and will flow into the network after sufficient time
period. Simulation models, so that, must keep this flow conservation law not only at every
links but also outside of network.
Bottleneck Capacity / Saturation Flow Rate at Link’s Downstream End
As the discharging flow rate from a bottleneck section like sags or tunnels contributes to thereproduction accuracy of the delay caused by the congestion at the bottleneck, it is essentialthat the capacity of the bottleneck should be reproduced in a stable manner during thesimulation.
Even on surface streets in under-saturated conditions, a vehicle may have the delay caused at
signalized intersections. The outflow from an intersection continues at the saturation flowrate till a vehicle queue developed during the red vanishes. It is important to clarify how thesaturation flow rate is reproduced in the simulation model as for the bottleneck capacity.
Growing and Shrinking Traffic Jam Consistent with Shock Wave Theory
When traffic jam beginning at a bottleneck grows to the upstream link, even traffic that does
not need to pass through the bottleneck may be also affected. As a difference in the jam’s
growing/shrinking speed results in difference in the degree of influence on the total delay
upon whole network, it is important to reproduce this phenomenon by using physical-queue toreasonably maintain the traffic density of congestion. The verification of these phenomena
is made by comparing the shock wave speed simulated with the one based on the shock wave
kinematics, as shown in Figure 1.
For surface streets, on the other hand, even if a signalized intersection is under-saturated, thevehicle queue grows and shrinks in every cycle. The tail of the queue moves with some timelag at the begging of the green phase due to drivers’ response delay at departure. Because ofthis time lag, when two signalized intersections are close and the queue heading to the one
intersection spreads beyond another, there may be the case that the vehicles in the tail of thequeue cannot pass through the near intersection depending on the signal offset. Therefore,
the simulation model that is considering signal control effect must reasonably reproduce thisphenomenon including shock wave propagation.
Capacity of Merging and Diverging Section
Not only sags or tunnels but also merging and diverging sections can be the most remarkablebottleneck of highways. At a congested merging section, the travel time on eachapproaching branch may vary with the merging ratio even if the capacity of the merging
section stays constant. Contrary, the capacity of the diverging section is constrained by thecapacities of downstream links and may change depending on the proportion of the demand toeach branch. The verification step includes these merging and diverging configurations.
Gap Acceptance of Right (Left)-Turn at a Signalized Intersection
In ordinary streets, it is daily observed that vehicles waiting for right (left)-turn in thesignalized intersection sometimes obstruct travel of followers and cause congestion. Suchvehicles are waiting to find an acceptable gap in the opposing straight-through traffic in thegreen phase, and consequently the right (left)-turn capacity declines according to the opposing
traffic volume. A simulation model that treats a signalized intersection would be required todescribe such relationship between the turning capacity and the opposing traffic volume bysome set of gap acceptance parameters.

5

Drivers’ Route Choice Behavior
Modeling for drivers’ route choice behavior considered in simulation will be classified asfollows:a) No route choice,b) Dynamic User Optimal (DUO) principle,c) Dynamic User Equilibrium (DUE) assignment,d) Probabilistic route choice.
Of these models, the one using a) above is considered applicable to evaluation of theshort-term traffic management that need not consider routes of drivers, or to a network
without any alternative routes. Verification of these models is not necessary because it isequivalent to the verification at a merging/diverging section.
On the other hand, the simulation model using principles of b), c) or d) let a driver select anappropriate route according to the presented information for routes.
This type of models isfrequently used to evaluate the operational policy such as dispersing the traffic spatially bymeans of informative service or road construction. Models with route choice can be verified
using a simplified network, e.g. with two routes for one O-D pair, to avoid the difficulty tofigure out the theoretical flow pattern to be compared with the simulation result. It is alsointeresting to examine results by changing settings of simulation model such as an update
interval of route costs and locations where the drivers can receive the travel cost information.

Difference in the Verification Procedure by the Types of the Flow Model
As mentioned before, the detailed verification procedure is separately described for Q-K type
and C-F type with different fashion. In order to show how the verification of these two types
is different, the verification of bottleneck capacity is illustrated as an example here.
Verification of Bottleneck Capacity for "Q-K type" Simulation Models
Given the sufficiently large demand to the bottleneck, according to the procedure described
below, verification is made whether the flow ratio on the downstream side is stable at thegiven bottleneck capacity. We may read the meaning of “self-consistency check” from thelast step iv).
i) A corridor network to be used consists of links as shown in Figure 2(a). The mostdownstream link will be the bottleneck. Set the model parameter so that thebottleneck capacity becomes 800, 1000, and 1200 [pcu/hr]. The capacity is set to be2000 [pcu/hr] for other sections.
ii) The traffic demand of 1500 [pcu/hr] is provided so that congestion occurs always in thebottleneck.
iii) Simulation is made for one hour using respective model parameters and the throughput
volume on the downstream of the bottleneck is recorded.
iv) As shown in Figure 2(b), the cumulative curve of throughput from the bottleneck isplotted to see if the given bottleneck capacity is really achieved.
Verification of Bottleneck Capacity for "C-F type" Simulation Models
The purposes of this verification step are:6

i)

To identify the "average" flow characteristics under the popular parameter settings forthe model.
ii) To get general understanding how the changes on each model parameters will affectthe "average" flow characteristic.
Before starting verification, the major model parameters should be listed up with their default
values and plausible setting ranges. They are roughly classified into those related to thedriving behavior and those associated with a location or a road section. Here, the C-F typemodel which has the major parameters shown in Table 1 is assumed as an example.
The verification is made for a simple road network as shown in Figure 3. The average Q-K
curve is derived according to the subsequent steps by changing the parameter settings for "allcombination" of their default, minimum and maximum values.
i)ii)

Set the traffic demand at some level and start simulation.
Wait till the link becomes a “steady state”, and then observe the cumulative throughput
volume at upstream and downstream ends of the link during some time period.
iii) Take the average number of vehicles on the link from these two cumulative volume
curves, and calculate the average traffic density on the link. Then, plot the result on the
Q-K plane.
iv) By superimposing all plots on the same Q-K plane, we may find a family of plots ofwhich the parameters on driving behaviors are the same.
v) Draw a curve along the plots. Each family of plots has a different parameters set onlink performance. The curve, therefore, represents the average Q-K curve for theparameter setting on driving behavior.

From this procedure, we may find a nuance of "sensitivity analysis".
Other steps in the verification of C-F type use these "average" Q-K curves identified here.
This verification step, therefore, must be made at first.

MODEL COMPREHENSION THROUGH THE
VERIFICATION RESULTS
Several simulation models that are practically used in Japan have been evaluated based upon
the proposed verification process. We have verified seven pilot models, such as AVENUE[Horiguchi, 1996], SOUND [Yoshii, 1995], tiss-NET [Sakamoto, 1998], Paramics [Paramincs,
on WWW], NETSIM [NETSIM, on WWW], REST [Yoshida, 1999], and SIPA [Yokochi,
1999] along the verification manual and validated with benchmark data set. Figure 4
illustrates the rough model classification according to their spatial resolution in vehicle
motions and the size of the network for their usual application studies.
In this chapter, let us introduce several verification results to understand how the verification
process put into practice. We also state some implications from the result in order to show
how the verification helps to comprehend the model characteristic.

Generation of Vehicles

7

For the verification of vehicle generation, the headways of arriving vehicles at a network
entry point are accumulated for certain travel demand. Figure 5 and Figure 6 illustrate the
distributions of the headways of AVENUE and SIPA under the demand = 1000 [veh./hr]. As
both models assume random arrival in the vehicle generation, the headways must obey the
exponential distribution. However, the result of AVENUE estranges from the theoretical
distribution, while the result of SIPA seems to fit. In the case when the result of simulationshows the difference from the theory, the tester of the verification required to explain why
such result could arise.
In this case, both AVENUE and SIPA are implemented as time-periodic scanning models.
They calculate the number of generated vehicles within each scanning interval according toPoisson arrival. Therefore, the headway between the vehicles generated at the samescanning interval is considered as 0 second.
While the time is continuum in the theory, the theoretical headway will be rounded up or
rounded off the fraction with the scanning interval. As shown in Figure 7, the theoretical
headway of 1.5 second may be rounded in the simulation as 1 second for some time or as 2
second for the rest, so that the headway appearance in the simulation may be distorted moreor less by the effect of the discrete time interval. As the unit range of the headway in Figure
6 is 1 second and the unit time interval of AVENUE is also 1 second, the headway of whichinterval is [0,1) will be counted up to 0 or 1 second, and the appearance shape estranges fromthe theoretical distribution. On the contrary, as the unit time interval of SIPA is 0.2 second,
only headway of which interval is (0.8,1.0] will be counted up to 0 or 1.
Adding to the above, the total number of generated vehicles is to be verified. Figure 8 and
Figure 9 indicate the results with different random seeds for AVENUE and tiss-NET, both ofwhich assume random arrival in vehicle generation. AVENUE always generates the same
number of vehicles as the given demand level, on the other hand tiss-NET varies its resultswith each random seed.
The results coming from the difference in the attitude of their "specification" stages can be
known only through the qualify tests in verification, and can give the meaningful implications
that the literatures would not tell us. For this case, a user of the simulation model that has
the same nature as tiss-NET in vehicle generation should realize that he or she has to repeat
the simulation for the same network and demand configuration with different random seeds.
The user also has to be careful in choosing the set of random seeds not to be biased in thenumber of generated vehicles against to the given demand setting. Subsequently, the user
must evaluate the variation of the number of generated vehicles for each calculation.

Traffic Flow Characteristics and Bottleneck Capacity of C-F type Models
According to the procedure described in the previous chapter, the traffic flow characteristics
of each C-F type simulation model must be identified in its verification process. Here, let us
introduce the verification of Paramics and SIPA as examples, both of which have a dozen ofmodel parameters concerning the driving behavior and the link performance.
The major parameters of each model are listed in Table 2. The meanings of some are clear,e.g. maximum acceleration or limit speed, but not all. For instance, how is the "minimum
headway" of Paramics difference from the "target headway" of SIPA, what is the "headway
coefficient" of Paramics, or is the inverse of "allowable minimum headway" of SIPA equal to8the link capacity? Even if their software manuals or technical papers state the meanings ofthe parameters, they are mostly conceptual explanations. It is still mysterious how each of
the parameters effects on the bottleneck capacity.
Our interest here is to understand the quantitative relationships between the model parameters
and the bottleneck capacity reproduced in the simulation. Furthermore, we would like tofind the most sensitive parameters through the verification process, because it must be themost efficient strategy to fit the simulation result to an actual traffic condition by changing themost sensitive parameters.
Figure 10 shows a portion of the results of Paramics. The dots in the figure indicate thevolume-density plots observed with varying major parameters. The shape of the dots is
associated with the sort of varied parameters. The remarkable point is that the decline of theflow rate is found only when the "headway coefficient" of the bottleneck link is 1.5 (dotssurrounded by the circle) and otherwise there are no effects. This implies that only thechanges on the "headway coefficient" of the bottleneck link does affect to the bottleneck
capacity while others have less influence.
Figure 11 shows the result of SIPA in the case that the "minimum headway" of the bottleneck
link changes from 2.0 seconds to 3.0 seconds. Theoretically, the minimum headway must be
equal to the inverse of the capacity, so that the bottleneck capacity must be 1200 pcu per hour
if the minimum headway is 3.0 seconds. However, the bottleneck capacity reproduced in theverification is slightly grater than the theoretical value. We may realize that the "minimum
headway" of SIPA is similar but different parameter from the link capacity.
There are common findings through the verification of bottleneck capacity for the C-F typemodels:i) Most of them have the parameters that affect to the minimum headway of each link.
ii) Such parameters have strong influence on the bottleneck capacity but others have lessinfluence.
iii) Such parameters are not exactly equivalent to the inverse of the bottleneck capacity.
There are some implications obtained from i) and ii). Even if we use so-called micro-scopic
simulation models, we have to be rather careful in calibrating the link parameters related toheadways than those to driving behaviors. In this sense, such micro-scopic simulation
models are essentially equivalent to the macro-scopic simulation models that require thecapacities of links.
For the reason to iii), the tester of SIPA reports the study on the effects of the length ofbottleneck sections. Figure 12 illustrates the reproduced bottleneck capacities that increaseas the length of the bottleneck link gets shorter. This phenomenon may be considered asfollows; when a vehicle with the minimum headway allowed in the upstream section enters
the bottleneck link, the headway achieved by the vehicle does not immediately shift to thenew minimum headway. If the length of a bottleneck link is so short that a vehicle passes
through the link before its headway will shift to the new headway, the capacity of thebottleneck link will not decline as expected. The users of C-F type models should realize
this phenomenon because it is found among most of the models, not only in SIPA.

9

Saturation Flow Rate at an Signalized Intersection
For the verification of the saturation flow rate at a signalized intersection, the tester isrequired to show the profile of discharging traffic within a signal cycle. Let us introduce theresult of SOUND, which has the combined flow model: car following for expressways(SOUND/express) and queuing vehicle lists for arterial roads (SOUND/A-21). The former
calculates each vehicle speed according to the spacing-velocity (S-V) function given to eachlink. The S-V functions can be identified through macro-scopic surveys of traffic flows. On
the other hand, the latter assumes the point-queue at the downstream end of each link. The
point-queue of each link accepts vehicles up to the jam density and discharges them at thesaturation flow rate of the link within the green signal.
Figure 13 illustrates the profile of vehicle discharging for SONUD/A-21. As SOUND/A-21
is a sort of "Q-K type" models, the discharging flow rate at saturation is expected to strictly
agree with the given saturation flow rate. Now we may confirm from the figure that thesimulation result attains the given saturation flow rate (1600 pcu/G1hr) in average.
There is another point to be discussed in Figure 13. The discharging flow rate of
SOUND/A-21 immediately goes up to the saturation flow rate when the signal turns to green.
In the actual situation, it takes some time to discharge the flow at the saturation flow rate
because of the response delay of drivers. The tester of SOUND gives the reason to this point
as follows:-

Instead of neglecting the starting delay, a vehicle cannot flow out during yellow
signals in order to adjust the effective green time.
At normal intersections, the duration of green signal is nearly equal to the effective
green time so as to take the yellow interval as much as the starting delay.

CONCLUSION AND FUTURE TOPICS
In this paper, the concept and the outline of the manual of standard verification processcurrently prepared in Japan is introduced. The verification manual is distributed through the“Clearing House” of simulation models on the Internet <http://www.jste.or.jp/sim/>.
Several simulation models that are practically used in Japan have been evaluated based upon
the proposed verification process. We have verified seven pilot models, such as AVENUE,
SOUND, tiss-NET, Paramics, NETSIM, REST, and SIPA along the verification manual andvalidated with benchmark data set. However, only some results are available on the Internet
at present [AVENUE, on WWW] [SOUND, on WWW]. We are now discussing how weencourage model developers to open their verification results to the public. Basically, we
expect the verification process to be “de-fact standard” by educating the necessity of theverification to practitioners and also to people in public sectors who order consulting jobs
using simulation models.
The further discussion in our activity is expected that for to comprehend the results of theverification studies, and to estimate the characteristics of each model. Also, we will afford
the movement of this standard certification process for other simulation models worldwide.

10

ACKNOWLEDGEMENT
We would like to state special thanks to the members of the simulation research group under
the sponsorship of Japan Society of Traffic Engineers, who contributed to meaningful
discussions on the verification manual.

REFERENCEAVENUE. Verification of AVENUE. http://www.i-transportlab.jp/products/avenue/verification (inJapanese).
Horiguchi, Ryota. et. al. 1998. A Benchmark Data Set for Validity Evaluation of Road Network Simulation
Models. Proceedings of 5th World Congress on Intelligent Transport Systems. Seoul.
Horiguchi, Ryota. et.al. 1996. A Network Simulation Model for Impact Studies of Traffic Management'AVENUE Ver. 2'. Proceedings of the Third Annual World Congress on Intelligent Transport Systems.
Orlando.
JSTE (Japan Society of Traffic Engineers). 2001. A Manual of Standard Verification Process for Traffic
Flow Simulation Model (Draft in English). http://www.jste.or.jp/sim/verification/manual/VfyManE.html.
NETSIM (CORSIM): http://www.fhwa-tsis.com/.
Paramics: http://www.paramics.com/.
Sakamoto, Kunihiro. et.al, 1998. Traffic Assignment Method Considering Car-by-car Behavior for Traffic
Impact Studies -Development of the tiss-NET System-. Proceedings of 8th World Conference on Transport
Research. Antwerpen.
SOUND. Verification of SOUND. http://www.i-transportlab.jp/products/sound/verification (in Japanese).
Yokochi, K. et.al. 1999. Development of Microscopic Traffic Simulator for AHS Evaluation. Proceedingsof 6th World Congress on Intelligent Transport Systems. Toronto.
Yoshida, T. et.al. 1999. A Basic Study for the Planning of ETC-dedicated Highway Interchange using a
Traffic Simulator REST (in Japanese). Proceedings of Infrastructure Planning, No.22 (2). pp.231-234.
Yoshii, Toshio. et.al. 1995. An Evaluation of Effects of Dynamic Route Guidance on an Urban Expressway
Network. Proceedings of the 2nd World Congress on Intelligent Transport Systems. Yokohama.

11

Arrival: A

Bottleneck capacity: C1

Q
Free flow

A

Congestion
Shock wave speed: v1

Arrival: Av2

Bottleneck capacity: C2 > C1

C2

Flow

v1

C1
K

Congestion

DensityFree flow
Shock wave speed: v2

Figure 1: The growing speed of jam is determined by the arrival demand and the bottleneck capacity.

Cum.#Veh.
Demand = 1500 [veh./hr]Bottleneck link capacity= 800, 1000, 1200 [veh./hr]

1500

Case with Bottleneck
Capacity of 1000 [veh./hr]

1000
DemandLink throughput
Link capacity = 2200 [veh./hr]0(a) Network and demand configuration

1(b) Expected cumulative curve of traffic volume

Figure 2: Verification for the bottleneck capacity for Q-K type simulation models

Parametersa) For driving behavior of vehicles
a1) Response delaya2) Desired headwaya3) Max accelerationa4) Desired speedb) For traffic demandb1) Composition ratio of drivers groups ofwhich the driving behaviors are differentc) For link performancec1) Limit speedc2) Gradientc3) Lane widthc4) Curvature radius

Default

Minimum

Maximum

1.0 sec
2.0 sec
2.0 m/s2
60 km/hr

0.7 sec
1.7 sec
1.7 m/s2
40 km/hr

1.5 sec
3.0 sec
3.0 m/s2
100 km/hr

Set freely as required
60 km/hr
0%
3.0 m
Inf.

40 km/hr-6 %2.75 m
100 m

100 km/hr
6%
3.5 m
-

Table 1: An example of the model parameter set of C-F type simulation model

12

Hours

Upstream end

Downstream end
Varying the parameters forlink performance to form abottleneck here.

L
Cumulative
volumeQ

Area of this region: S

Plot each sampling statusvalues onto Q-K plane
Flow

dQ

Average density of this section:
K = S /LdT
Flow rate of this period:
Q = dQ /dT

K

Time

dT

Density

Figure 3: Verification of bottleneck capacity for C-F type simulation models# of intersection

Application Network Size

1000

SOUND/express
SOUND/A-21
C-F type

100

10

SIPA
NETSIM
Paramics
REST
tiss-NET
1

AVENUE
Q-K type

10

100

[m]

Flow Model Resolution

Figure 4: Classification of seven pilot models (block -- Japanese model, Italic -- foreign model)

13

Appearance of Headway at Q=1000 [veh./hr]

30%

Theoretical distribution
25%

20%
Trial-1
Trial-2
Trial-3
Trial-4
Trial-5
Random Arrival (1000 veh/hr)

15%

10%

5%

0%

0~1

5~6

10~11

15~16

20~21

25~26

30~31

35~36

Headway Range [sec.]

Figure 5: Headway appearance of each trial with 1000[veh./hr] demand -- AVENUE

Appearance of Headway at Q=1000 [veh./hr]
30%

Theoretical distribution

25%

Trial-1
Trial-2
Trial-3
Trial-4
Trial-5
Random Arrvial (1000 veh./hr)

20%

15%

10%

5%

0%

1

6

11

16

21

26

31

36

41

Headway Range [sec.]

Figure 6: Headway appearance of each trial with 1000[veh./hr] demand -- SIPA

14

Theoretical arrival = Continuum time
A vehicle arrival

1.5

1.5
Time [sec.]

2

1

Simulated arrival = Discrete time

Figure 7: Effects of time discretion on headway distribution

Generated number of vehicles with different random seed

Generated # of veh.

2500
2000
Trial-1
Trial-2
Trial-3
Trial-4
Trial-5

1500
1000
500
0

Q=500 [veh./hr]

Q=1000 [veh./hr]

Q=2000 [veh./hr]

Given demand level

Figure 8: Total number of generated vehicles -- AVENUE

Generated# of veh.
発生台数(台)Demand setting = 1000 [veh./hr]
発生台数（交通需要：1000台/時）
1100
1000
900
800
700
600
500
400
300
200
100
0

1060

977

952

1

2

3

985

1007

1008

1017

976

4

5

6

7

8

1023

1028

9

10

乱数seed値
Randomseeds#

Figure 9: Total number of generated vehicles -- tiss-NET

15

ModelParamics

Driving behavior
Link performanceheadway coefficient,minimum headway,
limit speed,
maximum acceleration,
gradient, etc.
driving aggression, etc.
allowable minimum headway,target headway,
limit speed,target speed,
gradient, etc.
maximum acceleration,
response delay, etc.
Table 2: Major model parameters of Paramics and SIPA

SIPA

Q-K plots varying major parameters
3000
Other plots with changing the parameters
except “headway coefficient”

Flow rate [pcu/hr]

2400
1800

Plots for the “headway coefficient” ofthe bottleneck link is set as 1.5.

1200
600
0

25

50

75

100

125

150

175

Figure 10: Bottleneck capacity and traffic flow characteristics of Paramics

Q-K plots交通流特性（ＱＫ曲線）
varying minimum headwayof bottleneck link

Q 交通量
(flow rate)[pcu/hr]
Ｑ [PCU/時]

3000

自由流
渋滞流（ヘッドウェイ変化）
ジャム密度

2400

Capacity of simple section

1800
1200

The lowest bottleneck capacity

600
0
0

50

100

150

200

[PCU/km]K交通密度(density)Ｋ[pcu/km]

Figure 11: Bottleneck capacity and traffic flow characteristics of SIPA

16

1600

1500
1446

Flow rate [pcu/hr]

通
過
交
通
量

Capacity of bottleneck section

1412

1400

［

Bottleneck section length = 0.5km
Bottleneck section length = 1.7km
Bottleneck section length = 3.0km
Bottleneck section length = 5.0km
Bottleneck section length = 7.0km
Bottleneck section length = 11.7km

1300

P
C
U
/
時

1285
1216

1207

1198

］

1200

1100

-4

-3

-2

1000
-1

0

1

2

3

4

5

6

7

8

9

10

11

12

ボトルネックからの距離 [km]Lengthof the bottleneck link [km]Figure 12: The capacity varying with the length of bottleneck section (SIPA)

PacketSize=1, PCU=1.0, ScanItvl=1.0
Demand=800[pcu/hr], SFR=1600[pcu/G1hr]
30
Red

Green

Red

Cum.Discharge[pcu]25
20
Yellow

15
10
51600 pcu/hr

0

0
-15

0

15

30
45
Lapse time in Green [sec.]

60

75

Figure 13: Link discharging profile at a signalized intersection -- SOUND/A-21

17