Third Annual World Congress on Intelligent Transport Systems, Oct. 1996, Orlando, Florida

A NETWORK SIMULATION MODEL FOR IMPACT STUDIESOF TRAFFIC MANAGEMENT 'AVENUE Ver. 2'
Ryota HORIGUCHI*
Masao KUWAHARA*
Masahiko KATAKURA**
Hirokazu AKAHANE***
Haruo OZAKI*****) Institute of Industrial Science, University of Tokyo
7-22-1 Roppongi, Minato-ku, Tokyo 106, JAPAN
Phone: +81-3-3402-6231 / Fax: +81-3-3401-6286
e-mail: poepoe@nishi.iis.u-tokyo.ac.jp, kuwahara@nishi.iis.u-tokyo.ac.jp**) Department of Civil Engineering, Tokyo Metropolitan University
1-1 Minami-Osawa, Hachioji, Tokyo 19203, JAPAN
Phone: +81-426-77-2781 / Fax: +81-426-77-2772 / e-mail: katakura-masahiko@c.metro-u.ac.jp***) Department of Civil Engineering, Chiba Institute of Technology
2-17-1 Tsudanuma, Narashino, Chiba 275, JAPAN
Phone: +81-474-78-0444 / Fax: +81-474-78-0474 / e-mail: akahane@ce.it-chiba.ac.jp****) Department of Civil Engineering, Toyo University
2100 Kujirai, Kawagoe, Saitama 356, JAPAN
Phone: +81-492-39-1393 / Fax: +81-492-31-4482 / e-mail: ozaki@krc.eng.toyo.ac.jp

ABS TRACTThis paper describes the development of the road network simulation model for impact studies of traffic management,
which we call AVENUE (an Advanced & Visual Evaluator for road Networks in Urban arEas). Since traffic congestion
has caused serious social problems in urban areas, traffic assessment which evaluates the impacts of large events or urban
developments on road traffic is strongly required. So far, we have developed the first version of AVENUE which includes
basic functions to reproduce traffic flow considering drivers' behaviors such as lane choice and route choice, and has beenvalidated its traffic flow model with several real road networks. However, some modifications have been pointed out
through the experiences of the model validations. The newer version is therefore improved to incorporate various route
choice models and to reduce the calculation time. First, this paper describes the concepts of the traffic model used in
AVENUE Ver.2 which is characterized by the Multi-Scan Hybrid Block Density Method and the Multi-Layered Route
Choice Model. The traffic model have been verified with several sample networks in terms of vehicle queue setting back,delays at signalized intersections, the decline in capacity of turning movements, and the route choice behaviors. Then, themodel validation including its route choice model using the real road network which has about 50 intersections isreported. Based on this field application, the problems associated with the simulation of urban traffic as well as the model
validity are disclosed.

IN TRODU CTION
For the recent several years, ITS technologies are gathering great expectations to improve mobility of road traffic andtraffic assessment tools which can evaluate the impacts of any traffic management strategies are strongly required. So far,
we have developed a traffic simulation model for various impact studies, named AVENUE Ver.1, which can reproduce
traffic conditions on urban road networks controlled by signals.
AVENUE is characterized by the following features: 1) employing the Hybrid Block Density Method for its traffic
flow model so as to reproduce over-saturated traffic conditions, 2) incorporating drivers' route choice behaviors to meet the
1 / 8 (in CD-ROM)Third Annual World Congress on Intelligent Transport Systems, Oct. 1996, Orlando, Florida
recent requirements for the evaluation of dynamic route guidances, 3) modeling drivers' lane choice behaviors according totheir turning movements, 4) considering conflicts between vehicles at intersections, lane changing, etc. 5) designed andimplemented in Object Oriented Programming in order to satisfy various users' needs, and 6) adopting Graphical User
Interface and Animation for easy operations and persuasive presentations. Furthermore, the traffic model of AVENUE
Ver.1 was validated throughout the several applications to the real road networks which have no alternative route for eachO-D pair.
However, some modifications were pointed out through the experiences of the model applications. When the road
networks had many alternative routes and contained as much as some dozens of intersections, the route choice strategies
were so simple not enough to well simulate the traffic conditions and the calculation cost were so large that it took
considerable time for the computation. The newer version is therefore improved to incorporate various route choicemodels and to reduce the calculation time.
The following chapters describe, first, the concepts of the traffic model used in AVENUE Ver.2 which is characterized
by the multi-scan hybrid block density method and the multi-layered route choice model. Then, the traffic model havebeen verified with several sample networks in terms of vehicle queue setting back, delays at signalized intersections, thedecline in capacity of turning movement by interfering traffic, and the route choice behaviors. Furthermore, the model
validation including its route choice model using the real road network which has about 50 intersections is reported. Basedon this field application, the problems associated with the simulation of urban traffic as well as the model validity aredisclosed.

TRAFFIC MODEL OF AVEN U E Ver. 2
This chapter describes major modification on the traffic model of AVENUE from the previous version, i.e. the MultiScan Hybrid Block Density Method as its traffic flow model andthe Multi-Layered Route Choice Model.
Flow

Multi-S can Hybrid Block Dens ity Method
AVENUE employs Hybrid Block Density Method (HBDM)
in its traffic flow model[1]. In this method, each link is divided
into several blocks of which length is equal to the distance thata vehicle runs at the free flow speed of the link during ascanning interval. The in/out-flow and the density of each block
are revised at every scanning interval based on the flowconservation law, i.e. the flows between two adjacent blocks arecalculated by a function of the allowable out-flow of the
upstream block and the allowable in-flow of the downstream
block both which are determined by the flow-density
relationships of the link (Fig. 1).
Since the fluid approximation of traffic has some difficulties
in handling individual vehicle information, this flow calculation
was extended so as to move not only the continuum traffic
density but also the discrete vehicle images containing thevarious attributes such as vehicle types, origins anddestinations, route choice criteria and turning movements, etc.
In the previous version, the scanning interval of each blockis uniform and usually equal to 1 second, so that the flow
calculation cost proportionally increases as getting longer the
total length of the links. This may lead to inefficiency inpractical use of the simulation model. It is possible to reducethe calculation cost by adopting longer scanning interval, but

Capacity
Free Flow Speed

DensityJam Density

Critical Density
DensityJam Dens.

Up Block

Down Block

Traffic Vol.

Allowable Inflow

Critical Dens.
Allowable Outflow

Flow

Block Length
Fig1: Block, Flow and Density in HBDM
HBDM with uniform scanning interval
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Multi-Scan HBDM

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8scanning interval (sec.)
4

2

Fig2: block configurations in a link

1 1

Third Annual World Congress on Intelligent Transport Systems, Oct. 1996, Orlando, Florida
there will be inaccuracy at the signalized sections.
The Multi-Scan HBDM can reduce the calculation cost while keeping the accuracy of the calculation. The scanningintervals are set to 1 second at the most downstream blocks in each link, and doubled as going upstream. The length ofeach block is equal to the distance that a vehicle runs at the free flow speed during the scanning interval of the block (Fig.
2). The longest scanning intervals are limited to as much as 2 - 16 seconds by each link depending on the requirements
for the preciseness. For each block, the allowable in/out-flow is evaluated at every its own scanning interval. Then, at thesame time, the in-flow between the block and its upstream block is computed and the densities inside them are also
revised. It is clear that the calculation cost would be drastically reduced in this way, since not only the number of theblocks decreases but also each block is not always evaluated in every 1 second.

Multi-Layered Route Choice ModelIn the previous version, AVENUE only had the simple strategy of the drivers' route choice such that they always
choose the minimum cost paths that were updated every certain minutes. Each vehicle decided its turning movement at
every intersections, based on the minimum path information toward its destination. It is obviously difficult for theprevious model to handle the various drivers layers that have different route choice criteria, although such situation must
be frequently considered in the impact studies. Furthermore, too much simple strategy in a simulation easily leads totraffic conditions very far from real world.
In order to improve these problem, the Ver.2 now can treat multiple user layers on a network. Each of the layers canbe distinguished by vehicle types, travel purposes, and especially route choice strategies. There are two types of the route
choice strategies: stochastic route choices and prefixed route traverses. Drivers in the stochastic route choice layers choose
their paths when they enter the network according to the logit model:

Pij = exp (-qj*cij) / k exp (-qj*ckj)(1)where Pij means the choice probability of the i-th path for the j-th user layer, qj is the logit parameter for the j-th layer,
and cij is the cost of the i-th path for the j-th layer expressed as a function of the distance, the travel time and the numberof turns on the path.
In this model, alternative paths are given a priori for each O-D pair, instead of popular Dial's algorithm because it
sometimes eliminates quite relevant paths due to the definition of "the efficient paths". In the actual implementation, thealternative paths are automatically enumerated as well as system users can add or delete arbitrary paths.

MODEL VERIFICATIONThis chapter describes the verification of the traffic model of AVENUE with several sample networks. The flow
model is examined in terms of vehicle queue setting back, delays at signalized intersections and the decline in capacity ofturning movement by interfering traffic. Another verification refers to the route choice model comparing with thetheoretical user equilibrium state.

Vehicle Queue S etting Back
First, the ability to reproduce vehicle queue setting back is examined using a sample network shown in Fig. 3 (a), of
which demand exceeds the bottleneck capacity at the downstream end of Link2. The links are divided into blocks in twoway, i.e. the fine configuration such as the scanning intervals of the blocks are up to 2 seconds and the roughconfiguration up to 16 seconds.
Fig. 3 (b) illustrates the cumulative flow diagrams at each link end for both configurations. The arrows in the
diagrams figure out the time points when the shockwave reaches each link end at the theoretical speed. The flow rate ateach link end obviously declines to the bottleneck capacity after the shockwave arrives there. Furthermore, there are no3 / 8 (in CD-ROM)Third Annual World Congress on Intelligent Transport Systems, Oct. 1996, Orlando, Florida

Link1 & Link2:
Capacity=1800 [veh./hr]
Jam Density=120 [veh./km]
Free Spd.=36 [km/hr]

DemandLink2 inflow
3'20"

Link1 inflow
Link2 outflow

the time when the shockwave
arrives at the link end

Cum. #veh.
Fine Configuration

Demand1680 [veh./hr]

B ottleneck
1200 [veh./hr]

Link1
1000m

Link2
1000m

400

Rough Configuration

10'00"

10'00"

280 6'40"
200
3'20"
0

(a) A sample network and the parameters

6'40"
3'20"
10'00"

20'00"

10'00"

20'00"(b) The cumulative flow diagram at each link end

Fig. 3: Verification for Vehicle Queue Setting Backdifferences between the two diagrams. Thus, it may be concluded that the Multi-Scan HBDM can properly reproduce
queue setting back including shockwave propagations, even if the links are roughly divided.

Delays at S ignalized Inters ections w ith U nder-S aturated Traffic Conditions
Fig. 4 (a) shows the network used for the delay verifications. The average delay of each vehicle are collected over 10
signal cycles, varying the saturation flow rate at the stop line of the link while keeping the traffic conditions undersaturated. The random arrival is assumed in the simulation. Both the fine and the rough configurations are examined sameas above.
Each dots in the diagrams of Fig. 4 (b) represent the delay per vehicle obtained from the simulation. The upper curvemeans the delay calculated by Webster's formula assuming random arrivals and the lower means the delay of uniform
arrivals. In the fine configuration case, as Webster's formula closes infinity when the saturation degree approaches to 1.0,
it seems reasonable that the dots are distributed between two curves. On the other hand, in the rough configuration case,the dots are mostly distributed beneath the curve of the uniform arrivals. The reason is probably that the random arrival
pattern would be rounded to the uniform pattern at the most upstream block of which scanning interval is 16 seconds, andmoreover the travel time of each vehicle would be fastened at the block.

Capacity=2000 [veh./hr]
Jam Density=120 [veh./km]
Free Spd.=36 [km/hr]
Demand400 [veh./hr]Delay/vehicle [sec.]
160
Fine Configuration
Webster's Formula

Cycle=120 [sec.]
Split=0.458

80

S.F.R. at Stop Line
900 〜2000 [veh./hr]

0(a) A sample network and the parameters

Rough Configuration

Deg. Saturation

Webster's Formula

10

Deg. Saturation

1(b) Delay/vehicle by Webster's Formula and by the simulation

Fig. 4: Verification for Delays at Signalized Intersections with Under-Saturated Traffic Conditions

Decline in Capacity of Turning Movement by Interfering Traffic
Fig. 5 (a) illustrates a network used for the verification of the declines in capacities of turning movements. The
throughput at the intersection is counted over 1 hour, varying the demand of the opposite direction and the green time.
Fig. 5 (b) shows the comparisons of simulation results denoted by the dots with the theoretical right turn capacities
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SR [veh./hr of green]SR [veh./hr of green]
1200
1200
1000
1000
S.F.R=2000 [veh./hr of green]
800
800
Right Turn Capacity
600
600
DemandSR
400
400
Q = 200 - 1000 [veh./hr]
G/C = 90/120
G/C = 80/120200200
Q [veh./hr]
Q [veh./hr]
0
0
200
1000
200
1000
S [veh./hr of green]
S [veh./hr of green]
1200 R
1200 R
Cycle=120 [sec.]
1000
1000
G/C = 70/120
G/C = 60/120
Green=60-90 [sec.]
800
800
Demand600
600
1000 [veh./hr] 400
400
S.F.R=1800 [veh./hr of green]
200
200
0
Q [veh./hr]0
Q [veh./hr]
200
1000
200
1000
(a) A sample network and the parameters(b) The decline of right turn capacity
Fig. 5 : Verification for the Decline in Capacity of Turning Movements

which is derived from the gap acceptance model assuming random arrival of the opposite flow. Though the simulation
values are slightly over above the theoretical values, the model reveals the tendency to decline the turning capacities atintersections as the traffic volume of the opposite direction increases.

Behavior of Route Choice Model
Fig. 6 (a) shows a network used in the verification of the route choice model. There are two routes on the network

1200m
Link1
1200m
Demand2100 [veh./hr]

1200m

Bottleneck
1800 [veh./hr]

Link2
3000m

(a) The sample network and the parameterscum. #veh.

cum. #veh.

q=0.001, Update=1min1000

q=0.01, Update=1min

1000
Link1-in (UE)

Link1-in (UE)800

800
Link1-in (Sim)

Link1-in (Sim)

600

600
DemandDemand400

400
Link2-in (UE)
Link2-in (Sim)

200
0
0:00

Link1-out (UE)
Link2-out (UE)
0:10

0:20

0:30

200

0
0:40
0:50
1:00
0:00
0:10
0:20
(b) The comparisons of the simulation result with UE
Fig. 6 : The verilication of the route choice model

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Link2-in (UE)
Link2-in (Sim)
Link1-out (UE)
Link2-out (UE)
0:30

0:40

0:50

1:00

Third Annual World Congress on Intelligent Transport Systems, Oct. 1996, Orlando, Florida
and the merging point of Link1 and Link2 will
ChokojiMaruyama-Cho
be the bottleneck. Drivers select the routes
Shimo-Ichibawith the logit choice probabilities based on thecurrent travel time when they enter the
Toyota-Cho-Kita
network. The cumulative flows of Link1 andMaeyama-Cho
Link2 are observed over several variations of
the logit parameter and the cost updated
Heiwasho-Maeinterval.
Hoei-Cho
Fig. 6 (b) illustrates the cumulative flowsof Link1 and Link2 in the case of q=0.001 andToyota-Cho-Minami
0.01. The cost update interval is equal to 1
Parking Site
1km
minute for both cases. The cumulative flows
assuming user equilibrium state (UE) are also
Fig. 7 : The applied network in Toyota-City
illustrated for the reference. The simulation
results of both cases oscillate around UE because the route costs when a vehicle enters the network is different from route
cost that driver actually spends. Therefore, in the simulation model, drivers tend to over-react in selecting the shorterroute. The amplitude of the oscillation becomes larger when q is larger.

MODEL VALIDATION WITH REAL ROAD N ETWORKThis chapter describes the model validation of its applicabilities to the real traffic. We have already applied thesimulation model to simple but real networks which has no alternative route for each O-D pair and validated its
applicability comparing the simulation result with the observed data in terms of travel times, queue lengths, etc. [2] As thenext step, the network which has number of alternative routes is used to validate the applicabilities not only of the traffic
flow model but also of the route choice model.

Road N etw ork and Input Data
The network in Toyota City has 84 nodes and 193 links and covers about 3x3 sq. km. (Fig. 7). Inside the network,
there are several factories involving some parking sites of large capacities. During the morning peak period, vehicles
commuting to these factories occupy a half of traffic flowing into the network and traffic jams are frequently observed atbottleneck intersections such as Toyota-Cho-Kita and Toyota-Cho-Minami.
The O-D traffic volume is estimated for every 15 minutes from 6:30 am to 9:30 am, including the morning peak
period, based on traffic counts observed at the major intersections in 1993 and the survey of parking users. Totally about
20,000 vehicles are flowing into the network. The flow-density curves and the saturation flow rates of the links are
estimated from the intersection geometries. The logit parameter for the route choice model is brought from othersimulation study similar to this network.

S imulation Res ults
Fig. 8 illustrates the comparison between the observed and simulated queue lengths at 8:00 am when the traffic
conditions are heavily congested. As the definition of observed queue lengths is not quantitatively clear, it is difficult tocompare them precisely, but the simulation result reveals the actual bottleneck intersections.
Fig. 9 (a) figures out routes that floating cars recorded their travel times. Each floating car ran twice on each route.
Fig. 9 (b) are the comparisons between travel time of the floating car and the several simulation cars which departure ataround the same time of each floating car. In the simulation, too many vehicles selected the routes passing from
Maeyama-Cho to Toyota-Cho-Kita such as Route1, the simulation travel times in the section become larger than theobserved one, since the drivers' route choice behaviors are not sufficiently analyzed. However, the results reveal otheractual bottlenecks such as Hoeicho on Route1 and Toyota-Cho-Minami on Route2.

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1km(a) Observed traffic jams (8:00 am)(b) Traffic jams in the simulation (8:00 am)

Fig. 8 : The comparison in queue length

20 [min]Route1 (around 8:10 am)}

Toyota-Cho-Kita

Simulation

Maeyama-Cho
Hoeicho10

ToyotaCho-Kita

Observed Data

Maeyama-Cho020004000 [m]Route1
15 [min]Route2

Toyota-Cho-Minami

10

Route2 (around 8:00 am)

Toyota-ChoMinami}

Kawai-Cho(a) The routes on which the travel times are observed (above)(b) The travel times on Route1 and Route2 (right)

Observed Data

50Fig. 9 : The comparison in travel times

Simulation

Kawai-Cho

2000

4000 [m]CON CLU S ION SThis paper presents the concept and abilities of the traffic model used in AVENUE. The model verification concludes
that the Multi-scan HBDM can well reproduce vehicle queue setting back, delays at signalized intersections and thedeclines in capacities of turning movements, and that the route choice strategy based on current route costs let drivers
over-react in selecting the shorter route and makes the traffic condition unstable being far from user equilibrium state.
Throughout the experiments of its applications, we find the following future research needs:(1) to analyze route choice behavior in relation to drivers' characteristics,(2) to develop a method which automatically tune various parameters in simulation models,(3) to research a method which precisely estimate O-D traffic demand for applied networks, and(4) to examine observations on traffic conditions which is conscious of impact analysis with a traffic simulation.

ACKN OWLEDGEMEN T
We would like to express our sincere thanks to Ministry of Construction, Toyota City Government, Toyota MotorCo., Ltd., and Toyota Transportation Research Institute who kindly provided the traffic data for the research.
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EN DN OTES
1. R. Horiguchi, M. Katakura, H. Akahane, and M. Kuwahara, "A Development of a Traffic Simulator for Urban Road
Networks: AVENUE", Proceedings of Vehicle Navigation & Information Systems Conference in Yokohama, pp.
245-250, 1994
2. R. Horiguchi, M. Kuwahara, and I. Nishikawa, "The Model Validation of Traffic Simulation System for Urban Road
Networks: AVENUE", Proceedings of the Second World Congress on Intelligent Transport Systems in Yokohama,Vol. IV, 1977-1982, 1995

Fig. 10 : The display image of the simulation

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