IATSS Research Vol. 18 No. 2, pp. 76-79, 1994

NOTES
THE CASE STUDIES OF THE TRAFFIC ASSESSMENT USING ‘AVENUE’Ryota HORIGUCHI Masahiko KATAKURA

Hirokazu AKAHANE Masao KUWAHARA

Ms. Eng.,
Kumagaigumi Co., Ltd.
Tokyo Japan

Dr. Eng., Associate Professor
Chiba Institute of Technology
Chiba, Japan

Dr. Eng., Professor, Faculty of Technology
Tokyo Metropolitan University
Tokyo, Japan

Ph. D., Associate Professor
Institute of Industrial Science,
University of Tokyo, Tokyo, Japan

Keywords: Traffic simulation, Traffic assessment, Route guidance

1. INTRODUCTION OF AVENUE
Recently, traffic congestion has caused serious social problems in urban areas, so that traffic assessment thatevaluates the impacts of large events or urban developments
on urban traffic is strongly required. AVENUE[1] (an Advanced & Visual Evaluator for road Networks in Urban
arEas) was developed to be applied for such assessment,
considering the following distinctive features:
1) Hybrid Block Density Method -- is the traffic flow model
which reasonably reproduces queue blocking backs andconflicts of vehicles at intersections.
2) Driver's Route Choice Model -- is incorporated into thesystem in order to dynamically respond to varying traffic condition.
3) Object-Oriented Traffic Flow & Network Model -- affords a high flexibility for modeling various traffic control systems and regulations.
4) Graphical User Interface -- makes easy operation andimpressive presentations possible.
Figure 1 displays the image of AVENUE, which contains the road network diagram, the detail of the intersection, the signal phase diagram, etc. The global traffic condi-tion is displayed on the road network diagram with thecolored arrows. The detail of the traffic condition aroundthe intersection can be visualized if it is necessary.
Figure 2 illustrates the generic procedure of the traffic assessment using AVENUE. AVENUE requires the input data listed below:
1) road network data including number of lanes, trafficregulations, capacities of links, etc.,
2) signal control parameters and various traffic regulations, and3) varying traffic demands per each origin-destination pair.
As the result of the simulation, AVENUE simultaneously displays the traffic conditions on CRT and records
the following data:1) count of throughputs at each link end per each turning
direction,2) travel times per each link, each path, each origin-destination pair,3) queue length of each link, etc.
In this note, two case studies of the traffic assessment using AVENUE are introduced to show its flexible
applications.

SignalParameters

RoadNetwork

Online / Offline

Manual / Digitizer

AVENUEOD-TrafficDemands
Manual / File

CRT

VisualPresentation

Object-OrientedTraffic-Model

Files

Route-InformationModule

Fig. 1 The displayed image of AVENUE

Travel Times
Queue Length
Throughputsetc.

Fig. 2 The flow of the simulation with AVENUE

IATSS Research Vol. 18 No. 2, pp. 76-79, 1994

2. CASE 1: PLANNING OF UNDERGROUND PARKINGPLACE
The central area of A-City contains several large department stores, so that numbers of cars for shopping concentrate in the daytime of holidays. Figure 3a shows theoriginal road network used in the simulation and the observed traffic volumes at the intersections in a peak periodof a holiday. Particularly, the traffic volume on the roadcrossing from side to side, Route-A, is close to the capacity.
An underground parking place of which capacity is
400 cars is planned in this area. However, this project may
generate the additional traffic and consequently it will havesome impacts to the traffic condition on Route-A. Figure
3b illustrates the modified network and the generated traffic for the project during the peak period.

Because of the advantages of the Object-Oriented
Traffic Model, the function of parking management system
can be easily implemented in AVENUE by adding the 'parking object' that has the performance of the gate, the capacity, the distribution of the exit cars, etc. Figure 4a shows theparking object added to the original network and Figure 4bshows the detail around the parking place.
In this case, we applied AVENUE to evaluate thequeue length in front of the parking entrance and the travel
times along with Route-A. Figure 5 shows the comparisonsof both travel times of the original network and the modified network. The results may conclude that there are no
obvious differences among these travel times and that they
are close to the average travel time of the road traffic census (90.7 seconds).

56
107
781

869

877

111

724

1721
1491
605

55

1758
1741
665

254

1785
102

213

1818

172

48

P
210
(12:00-13:00)
changed toone way0

(12:00-13:00)

100m

0100m200a) The original network and the observed
traffic volumes at the intersectionsb) The future network and the generated traffic
volumes towards the parking place in a peak period

Fig. 3 The road network used in Case1

a: The parking object added to the network

b: The detail of the traffic around the parking place

Fig. 4 The displayed images of Case1

IATSS Research Vol. 18 No. 2, pp. 76-79, 1994

3. CASE 2: PLANNING OF ROUTE GUIDANCE

4. CLOSING REMARKS

Figure 6 illustrates the road network including a shopping center that has the parking place for 1100 cars. Though
the parking capacity is enough even during the peak period,
traffic jam frequently appears around the marked intersection, A-Cross. This is originated from that the shopper's cars
are guided shown in Figure 7a and that these cars consequently concentrate to A-Cross. Figure 7b shows the resultof the simulation for the present state of the area.
Figure 8a illustrates the alternation of the guidance
to disperse the congestion around A-Cross. In the simulation model, only when the contents of the route guidance
objects are modified as shown in Figure 8b, the shopper'scars will follow the direction. As a result of this countermeasure, the queues around A-Cross become shorter thanthe present state. Figure 8c shows the result of this alternation.

AVENUE has just been developed as version 1.0 that
only has basic functions. For this kind of traffic simulator,
there are a lot of demands for the applications such as• optimization of signal control parameters,• evaluation of the effects of bus lanes, HOV lanes, reversible lanes, etc.,• evaluation of the effects of vehicle navigation systems.
Considering these demands, we will develop the next
version based on both the microscopic analyses of thedriver's behaviors and the macroscopic analyses concerning to the route choice behaviors.

150

REFERENCE[1] R. Horiguchi, M. Katakura, H. Akahane and M. Kuwahara: A Development of a Traffic Simulator for Urban Road Networks: AVENUE, VNIS'94
Proceedings , Vehicle Navigation & Information Systems, pp.245-250,
Sep. 1994(sec.)

present state (upstream)
future state (upstream)present state (downstream)
future state (downstream)120

average time (road traffic census)

90

60

30
11:00

11:30

12:00

12:30

13:00

13:30

Fig. 5 The comparisons of the travel times on Route-A

Fig. 6 The displayed image of Case2

14:00

IATSS Research Vol. 18 No. 2, pp. 76-79, 1994

ACB

AC

B

S.C.

S.C.

PP
d1

d1
DE

E

D

F
d2

guide signs
for S.C.

F
d2

guide signs
for S.C.

a) The improved route guidance for the shopping centera) The original route guidance for the shopping centerb) The modification of the route guidance objectb) The simulation image of the present stateFig. 7 The original state of Case2c) The simulation image of the future stateFig. 8 The future state of Case2