VNIS'94 Proceedings

A DEVELOPMENT OF A TRAFFIC SIMULATORFOR URBAN ROAD NETWORKS: AVENUE
Ryota Horiguchi*, Masahiko Katakura**,Hirokazu Akahane*** and Masao Kuwahara***** Kumagai Gumi Co., Ltd., 2-1 Tsukudo-cho, Shinjuku-ku, Tokyo 162, Japan** Tokyo Metropolitan University, Tokyo*** Chiba Institute of Technology, Chiba**** Institute of Industrial Science, University of Tokyo, Tokyo
Abstract: This study develops AVENUE (an Advanced & Visual Evaluator for road Networks in
Urban arEas). AVENUE incorporates a driver'sroute choice model and a traffic flow model thatcan reproduce over-saturated network conditions
as well as under-saturated ones. The developmentof AVENUE is based on the object-oriented programming which affords flexible modifications tousers' requirements and graphical environment
for easy operations. This model was validated
through an application to the Kinshicho area inTokyo.

In this paper, we will describe the development ofAVENUE, which has the following distinctive features for its applications to urban traffic:1) Hybrid Block Density Method -- In thismethod, a link is divided into several blocks.
Density of every block is revised based on theflow-conservation law and the Flow-Density relationship, and discrete vehicle images are
moved according to the calculated flow between
the two neighboring blocks. Thus AVENUE
reasonably reproduces queue blocking backs
and conflicts of vehicles at intersections.
2) Driver's Route Choice Module -- To responddynamic traffic situations on a road network,
AVENUE incorporates driver's route choice
module.
3) Object-Oriented Traffic & Network Model -As the development of AVENUE is based on
the Object-Oriented Programming, the system
has a high flexibility to modify or extend its
functions.
4) Graphical User Interfaces -- For easy operations and visual presentations, AVENUE employs graphical user interfaces.

I. INTRODUCTION
As traffic congestion has caused serious socialand economic problems in urban areas, there is agreat demand for traffic simulation that can evaluate
impacts of improvements on intersection geometry,traffic regulations, signal control, etc. Such a simulation system can be also an efficient tool for traffic assessment at planning stages of large events or urban
developments.
So far, several traffic simulators for network assignment, such as CONTRAM[1] and SATURN[2],have been developed, but some problems are still remained in most of them[3][4]. The most important
problem is that they can not sufficiently deal withover-saturated traffic conditions.

II. HYBRID BLOCK DENSITY METHODAVENUE employs the hybrid representation oftraffic flow, which treats traffic as the continuum
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dL ,Aouti = min Kci, Ki(t)dtFlowQc

K ji - Ki(t) dL ,dt0Kc(1)Ki(t) £ Kci ,Aini (t) =Kj Density

Kci K ji - Ki(t) dL, Kci < Ki(t) £ K ji ,dt
K ji - KciFigure 1. The Q-K curve for the Hybrid Block Density
Method(2)

flow using the Block Density Method[5][6], but at thesame time it moves discrete vehicle images for theconvenience of the route choice calculation and forhandling conflicts of vehicles at intersections andlane changings. We, therefore, call the method as theHybrid Block Density Method. In the traffic flowmodel, each link is divided into several blocks, theflows and the densities of which are revised backward from the downstream end of the link at every
scanning interval based on the flow-conservation law
and the Q-K relationships.
In this method, the length of each block, dL, isequal to the distance that a vehicle runs at the freeflow speed, Vf during the scanning interval, dt, andfor every block i, the Q-K relationship must be specified in advance, which is assumed to be a triangle
shape as shown in Figure 1 so as to be specified bythree parameters of jam density Kji, critical density

As Figure 2 illustrates, the traffic flow Qi,i+1(t) between the neighboring downstream block i and upstream block i+1 is obtained byout
Q i,i+1(t) = min Ain
i (t), Ai+1(t) .

(3)

At the next scanning time t+1, the traffic density
Ki(t+1) is revised as

Ki(t+1) dL = Ki(t) dL - Qi-1,i(t) dt + Qi,i+1(t) dt .
(4)
These calculations can be applicable not only tounder-saturated flow but also to over-saturated flow.
However, since Qi,i+1(t) is still continuum, there
are additional procedures required to move discrete
vehicle images for the implementation of the Hybrid
Block Density Method. Firstly, Qi,i+1(t) is converted
an integer number Mi,i+1(t), vehicles of which are
moved from block i+1 to i. The difference between
Mi,i+1(t) and Qi,i+1(t) will be moved to precede following vehicles at the next scanning time.

Kci, and maximum flow Qci. Then, at every scan,
according to the Q-K curve, the allowable outflow
from block i, Aouti(t), and the allowable inflow toblock i, Aini(t), are calculated from the current traffic
density Ki(t) in block i as follows:Density

Kj

Bi+1

BiIII. OBJECT-ORIENTED TRAFFIC &NETWORK MODEL

jam

Ain
Kc
Aout0Upstream

Qi,i+1

freeThe development of AVENUE is based on theObject-Oriented Programming which affords a highflexibility to describe the traffic model as well as tomodify it[7]. Figure 3 shows the identified classes andDownstream

Figure 2. The calculation of the flow and densities
between two blocks

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their hierarchy and relationships for the Object-Oriented traffic model. Several basic classes that are themajor actors in the various scenes on urban roads are
mentioned below.
1) Base-Node and Base-Link: Objects of theseclasses form a network connected to each other.
The class Base-Node has its subclasses ODNode and Intersection-Node. An OD-Node corresponds to an end point where traffic flows intoand out of a road network. The class Base-Link
has attributes to its length, number of lanes, capacity, route guidance, etc. and relationships tothe class Base-Block. The route guidance attribute contains the costs of the routes for everydestinations. The route searching module updates the contents of the attribute in every specified time interval.
2) Base-Vehicle: Objects of this class have attributes to their origins and destinations, and further more, they have the method for the routechoice. When a Base-Vehicle enters a BaseLink, it decides the next Base-Link to run into
according to its destination and the route guidance attributes of the Base-Link. In the current
version of AVENUE, a Base-Vehicle always
chooses the minimum-time path.

upstream-ofBase-Link
output-to

made-of3) Base-Block: Objects of this class relate to their
up-stream and down-stream Base-Blocks andhave the prototype method for the Hybrid
Block Density Method. The prototype method
is overridden by the specific one at the objectsof the subclass derived from Base-Block suchas Signal-Blocks whose flow calculation iscontrolled by the related Signal-Lights or LaneBlocks which move Base-Vehicles only which
satisfies the traffic regulations of the lanes.

IV. SYSTEM OUTLINEAVENUE is implemented on G2[8] that is an expert system upon UNIX workstations. G2 is originally developed for process plant control in realtime, but it has sophisticated platform for the ObjectOriented Programming and construction of graphical user interfaces.
The operations of AVENUE are interactive
through menu or dialog boxes with a mouse evenduring the simulation. As objects are displayed asicons, it is possible to visually recognize the detailsof traffic simulated as shown in Figure 4.
The input to AVENUE is road network data, sigPassenger-Car

downstream-ofBus

Base-Vehicle

Base-Block

Truck

input-toFluid-Block

Base-NodeOD-NodeRough-Block

Intersection-NodeBase-Lane

Hybrid-Block

Signal-Controller

containing

Isolated-Controller

Selection-Block

Offset-RootController
Lane-Block

Link-Entry-Block

Buffer-Block

Signal-Blockregulated-by

Offset-FollowerController
controlled-by

regulated-by

Signal-Light

Figure 3. The object diagram for the traffic & network model of AVENUE accroding to OMT method
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Block

V. APPLICATION
LaneLinkFigure 5 shows the Kinshicho area in Tokyo
where AVENUE was applied for the system validation. The section that is 600 meters in length has fourintersections. The traffic data was collected during
8:00-10:00 and 12:00-14:00 on a weekday. Particularly, license plate numbers were recorded at boundaries of the study area to obtain the travel times on thelinks and the throughputs at intersections.
Firstly, the data collected during 12:00-14:00
when the traffic volume was relatively small wasanalyzed. Figure 6a compares the observed count ofvehicles discharged from Ryogoku-3-C with thesimulated one. The data is aggregated in every signal
cycle of 120 seconds. In spite of the large variation in
the data, the first half of the simulation values reasonably correspond to the observed ones. However, theSignal-LightIntersection
VehicleFigure 4. The display image of AVENUE

nal control parameters and the OD traffic demand.
The results of the simulation are simultaneously displayed and other observed values listed below are recorded.
• travel times on links and between O-D pairs,• queue lengths on links,• throughputs of vehicles at intersections, etc.

Downstream
Upstream
Ryogoku-1

Ryogoku-2

Ryogoku-3

Midoricho-1

Traffic Detectors
BBBAAAACC

BCC

D

D130m

250m
70m

D

270m

210m

Figure 5. The road network used for the system validation
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VNIS'94 Proceedings(sec.)
90(veh.)
7060
50
4030
20
100

simulation
observation

807060simulation
observation
1020304050504060 300(cycles)

102030405060(cycles)b) Travel times from Ryogoku-3 toRyogoku-1a) Throughputs at Ryogoku-3-C

Figure 6. The comparison of the result of the simulation with the observed data from 12:00 to 14:00(veh.)
1201008060402000(sec.)
200simulation
180
observation
160
140
120
100
80
simulation
60
observation
40
201020304050
60010203040
5060(cycles)(cycles)b) Travel times from Ryogoku-3 toa) Throughputs at Ryogoku-2-C
Ryogoku-1

Figure 6. The comparison of the result of the simulation with the observed data from 8:00 to 10:00

second half of the simulation values are lower thanthe observed ones, which may be caused by the factthat the number of generated vehicles at the up-stream
end of the network was smaller than the observed
ones due to inadequate random number series.
Figure 6b shows the both travel times from
Ryogoku-3 to Ryogoku-1. Taking averages, it was
found that the simulation values are slightly smaller
than the observed ones. This difference may comefrom the fact that the attribute of the free speed given
to the link is greater than the actual speed of the freeflow traffic and that there were several irregular dataof portage cars whose travel times were more than
250 seconds among the observed data.
Secondly, the same comparison with the data collected between 8:00-10:00 when the upstream traffic

had larger volume and revealed congested state wasconducted. Figure 7a shows the comparison of thecount of vehicles discharged from Ryogoku-2-C.
The simulated values are generally smaller than theobserved values. This difference came from thecomplex lane usage that though the lanes at
Ryogoku-2-C consists of 2 straight lanes and 1 rightturn lane in the simulation model, the actual traffic
flowing straight also runs over the right-turn lane.
Figure 7b shows both the travel times fromRyogoku-3 to Ryogoku-1. The simulated values
well follow the observed values which are increasing
as the queue blocking back until 40th cycle, butthereafter, the simulated values become small. Thisis due to the fact that the number of the vehicles generated at the upstream-end of Ryogoku-3-B and D249

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VNIS'94 Proceedingsdecreases, so that the congested state along thestretch is dissolved earlier than the actual traffic.
In this application, the simple road network
which has no alternative path for every O-D pair isemployed to eliminate the influences of the route
choice module. Though there are several points tobe improved in the programming, the results showsthat AVENUE could reproduce traffic flow on bothunder-saturated and over-saturated networks if theparameters which control the traffic flow are carefully adjusted.
As a next step, the validation including the influences of the route choice is being examined throughthe application to a more complicated network of aprovincial city.

[2] M.D. Hall, Vliet D. Van. and L.G. Willumsen,
A Simulation Assignment Model for the Evaluation of Traffic Management Schemes, Traffic
Engineering & Control, Vol.21, No.4, pp.168176, 1980
[3] H. Oneyama, T. Okada, M. Kuwahara and T.
Yoshii, A Study on the Vehicle Motion Logic ofCONTRAM, Seisan-Kenkyu Vol.46, No.3,
pp.54-57, Institute of Industrial Science, University of Tokyo, 1994, (In Japanese)[4] H. Inouye, A Dynamical Simulation Method ofTraffic Flow on A Road Network, Journal of Infrastructure Planning and Management, No.470/ IV-20, pp.87-95, Japan Society of Civil Engineering, 1993, (In Japanese)[5] H. Ozaki, A Dynamic Traffic Simulator toEvaluate Signal Control (DESC), Traffic Engineering, Vol.24, No.6, pp.31-37, Japan Societyof Traffic Engineers, 1989, (In Japanese)[6] I. Ueda, S. Tsubono, M. Kuwahara, H. Akahaneand H. Ozaki, Development of a Traffic Simulation Model Incorporating Route Choice, Proceedings of Infrastructure Planning, No.14 (1),pp.279-286, 1991 (In Japanese)[7] J. Rumbaugh, et al., Object-Oriented Modelingand Design, Prentice Hall, Inc., 1991
[8] G2 Reference Manual Version 3.0, GensymCorporation, 1992

VI. CONCLUSION
This paper introduces the Hybrid Block Density
Method and the concept of the traffic model used inAVENUE and reports its application to a simple real
network. Although, through the validation, several
points to be improved are found, AVENUE seems tobe capable for various purposes such as• optimization of signal parameters,• evaluation of the effects of bus lanes, HOVlanes, or reversible lanes,• planning for route guidance at emergency orlarge events,• traffic assessment of large scale developments,• evaluation of vehicle information and controlsystems, etc.

REFERENCES[1] D.R. Leonard, J.B. Tough and P.C. Baguley,
CONTRAM: A Traffic Assignment Model for
Predicting Flows and Queues during Peak Periods, TRRL Report LR 841, 1978
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