AN ENHANCED TRAFFIC SIMULATION SYSTEMFOR INTERACTIVE TRAFFIC ENVIRONMENT
M.Kuwahara*, S.Tanaka*, M.Kano**, M.Furukawa***, K.Honda***, K.Maruoka****, T.Yamamoto*****,
T.Shiraishi*, H.Hanabusa*, N.Webster*
Abstract—This study develops a microscopic traffic simulation
model (KAKUMO) which is built into a mixed reality experiment
system under an interactive traffic environment. We aredeveloping a mixed reality experiment system is underdevelopment to examine several human factors such as driving
behavior and travel choice behavior, which are required to designand evaluate various ITS systems. The system is characterized by
the environment in which a driver, vehicles and infrastructure aredynamically interacting with each other. In this paper, we
introduce the specification and performance of the micro
simulation model which is built into the prototype mixed reality
system.

such as experiment vehicles, traffic detectors, AVIs, VICS andprobe vehicles; and Virtual Experiment Laboratory that
supplies a virtual environment in which a driver, vehicles andinfrastructure are dynamically interacting with each other sothat human factors can be measured in a realistic and fully
controlled virtual environment.

Index Terms—ITS, Traffic Simulator, Driving Simulator ,Image Generator,

I. INTRODUCTION
Further analysis on human factors such as driving behavior
and travel choice behavior is required to design and evaluatevarious ITS systems, equipment, and schemes. For a traffic
information system, for example, it is important to provide
effective and timely information for drivers. Therefore, wehave to analyze driver’s reactions when the system provides
some information such as traffic congestion and trafficaccidents and evaluate an effective way of providing. In respectof the study, we have to analyze driving behaviors (acceleration,deceleration, lane changing for route choice, etc.) by Driving
Simulator (DS). On the other hands, we have to evaluate an
influence to the large area by Traffic Simulator (TS). To satisfy
both demands, it is useful to construct an evaluation andfeedback system in which TS and DS communicate with eachother. For an experimental tool for human factor analysis,
Mixed Reality Experimental Laboratory is under development
in “Sustainable ITS Project” at Center for Collaborative
Research, University of Tokyo, in which work has beenongoing since it was initiated in April 2003 as a
private-public-academia partnership project. As shown inFig.1, the Mixed Reality Experimental Laboratory consists oftwo main components: Real Observation Laboratory that
efficiently collects and processes real data from various sensors*Institute of Industrial Science, University of Tokyo**TOSHIBA SOLUTIONS CORPORATION*** CHODAI CO., LTD.
**** Social System Research Institute Inc.
***** MITSUBISHI HEAVY INDUSTRIES, LTD.
0-7803-8961-1/05/$20.00 ©2005 IEEE.

Fig.1 Mixed Reality Experimental Laboratory

As Fig.1 indicates, the way of connecting TS and DS is a core
process in the Virtual Experiment Laboratory. However, it isdifficult to supply to DS virtual traffic environment with reality
only by TS. The main reason is that there are various
differences in the precision of these models. We need todevelop the micro simulation model(KAKUMO) as a solution
for these problems. KAKUMO proposed here is a part of themixed reality system and it links the network-wide traffic
simulator with the driving simulator so that a vehicle driven inDS can interactively run among vehicles in TS.

II.

TRAFFIC SIMULATOR

In this paper, Traffic Simulator means the model reproducing
traffic condition in a network wide road network like whole onecity, generally known as a dynamic traffic assignment model.
TS is useful to evaluation of policies whose impacts spread to alarge area, for example provision of traffic information or- 739 -traffic demand management like peak-road pricing etc.
AVENUE(1),(2) and SOUND(3) are typical TS models.

setting the coefficients of generalized route cost calculation forevery vehicle.

III.

CONCEPT OF KAKUMO

The micro simulation model, KAKUMO is a part of the Virtual
Experiment Laboratory (VEL) and it links the network-wide
traffic simulator (TS) with the driving simulator (DS) so that avehicle driven in DS can interactively run among vehicles in TS.
Major differences in the specifications between TS and DS areshown in Table 1 and KAKUMO is required to link these twosimulators with quite different specifications.
Table 1. Differences between systems
Traffic Simulator

Fig. 2 Example of Traffic Simulator (SOUND)Area scale

TS consists of Vehicle Flow model and Route Choice model,and traffic condition is reproduced by repetition of these twokinds of model as Fig.3. Vehicle Flow model assigned a given
traffic volume between OD pairs on a road network based on
the selection probability of each route computed by Route
Choice model, and computed travel times of all road links. InRoute Choice model, the selection probability of each route
between OD pairs is revised every specified time interval (forexample, 5 minutes), based on route characteristics, such as
travel time, distance, and number of right or left turns, is sent toVehicle Flow model. Thus, by repetitions of Vehicle Flowmodel and Route Choice model, the dynamic equilibrium stateof traffic flow is approximated.
Vehicle Flow model

Travel time

Route Selection Probability

KAKUMO

Driving Simulator

Wide Area

A part of the TS area

A part of the(Within a radius ofthat covers the DS

KAKUMO area thatseveral hundred metre

area

covers driver’s viewto several km)
Time scanning

1Hz

20Hz

60Hz

Precision of1m

1 cm

1 cm

Movement theory

By flow quantity

By the acceleration

By the scenario (notof vehicles

calculationand speedequipped theory)

Link ID, Lane ID,

Link ID, Lane ID,

Position (X,Y,Z)

Distance from thePosition (L,A,Z)

network data

Vehicle Position

stop line
Route Choice
Behavior

Equipped with a

Guided from TS

Not equippeddynamic route choicemodel

For TS, we employ an existing traffic simulation model,
AVENUE, which reproduces vehicle positions in every 1
second scanning interval incorporating route choice for a fairly
large network. On the other hand, for DS, a much smallerscanning interval of 1/60 second is used, and also the input dataon road geometry is much more detailed than that for TS.
Judging from the above, we think that the roles of KAKUMO
are follows:

Route Choice model(a) KAKUMO connects TS with DS, and compliments
the difference in the precision of these modes.
(b) KAKUMO reproduces the natural driving behavior
and traffic environment in the DS display.
(c) KAKUMO supplies the field of feedback thatsimulation results (TS and DS) are given each other inVEL.

Fig.3 Structure of Traffic Simulator

Vehicle Flow model is a macroscopic model, in which vehicles
are treated as packets or fluid, and vehicles move based on Q-Kfunction which is a relationship of traffic flow rate and trafficdensity. Q-K function of each link is set up based on roadgeometric structure, such as the number of lanes; traffic flowbottlenecks and shockwave phenomena are reproduced. Thus
traffic congestion expressed as physical queue.
Route Choice model determines the selection probability ofeach route from diverge points to destinations, based on thegeneralized cost of each route computed from dynamic or static
route attributes, such as travel time reproduced by Vehicle
Flow model, restrictions of road closure, toll, or number oftimes a right or left turn is required on each route. It is possible
to analyze the differences of drivers’ route choice behavior by

Since KAKUMO transfers vehicle information to DS in every
1/20 second, calculation of all vehicle motions in theKAKUMO area must be completed within the communication
period of 1/20 second. Therefore, it is not advantageous tocover the whole study area by KAKUMO, rather only
movement of vehicles requested by DS that are in the smaller
KAKUMO area shown in Fig.4. These should betime-dependently calculated relative to the position of the DS- 740 -information is transferred from TS to KAKUMO, and TS hands
over the control of the vehicle to KAKUMO, as shown inFig.7.

vehicle.

Vehicle ID,Vehicle Type ID,
Position(Link ID, Lane ID),
Speed,
Next Route(Next 3 Link ID)

TS

KAKUMO

Fig.7 Communication data (entry to the KAKUMO area)

B. PROCESS 2:IN THE KAKUMO AREA
Fig3 KAKUMO Area

This brings us to the process of VEL system. First of all,dynamic traffic condition in a whole study area is simulated by
TS. Then, one vehicle in TS is selected as the vehicle driven by
DS; that is, the DS vehicle is driven by an examinee interactingwith other vehicles in TS. KAKUMO does not deal with theentire study area but considers an area of just a few hundreds
meters around the DS vehicle. As shown in Fig.5, KAKUMO
supplies detailed information of only vehicles to be displayedin the DS screen. After receiving vehicle information (vehicleID’s and positions) from TS in every 1 second, KAKUMO
produces detailed vehicle motions in the area, and transferredthem to DS in every 1/20 second. DS then displays vehicles
within sight distance of the DS vehicle driven by the examinee.
TSKAKUMO

VehicleDynamics
date

brake
accelerator

Updatenoitacinummo
CUpManager of the vehicles
inside the KAKUMO Area

Information of Vehicles thatleft from the KAKUMO Area

・Demand・Judgement・Operatione1Hz1Hz

Driver Modeld atcalculation resultwith KAKUMO

noitacinummoC20HzEventdriven

UpEntry toQuantity
KAKUMO Area
Dencity
Link Travel Time
Route Choise
Each Vehicle's Info.
：Reflection of theDS

KAKUMO

Subject
Vehicle'sInformation

KAKUMO Controller

Information of Vehicles thatentered to the KAKUMO AreanoitacinummoCTS Engine(AVENUE)

A vehicle within the KAKUMO area runs in accordance withseveral models of vehicle motions such as the car-following,lane changing, and overtaking models. KAKUMO uses a LA
coordinate system to describe a vehicle’s position. This
coordinate system has an axis parallel to the road and an axis
perpendicular to the road. L the distance measured fromupstream end of the link and A the perpendicular distancemeasured from the right boundary of the lane. A vehicle
position is determined by each vehicle’s action model. As
shown in Fig.8, TS manages the route choice behavior of allvehicles. When a vehicle passes the end of a link, the sequenceof next 3 links (route) that the vehicle will take is determined byTS. Fig.9 shows the communication data of the vehicle movedby KAKUMO.

Link 6

When a vehicle goes overthe boundary of the link

Link 420Hz

Link 2Other Vehicle'sInfomation

Link 0KAKUMO Data

Link 8Link 1

Link 7

・Road Network・Vehicles etc...

Link 9Link 3

Fig.5 Data flow in VEL

1: Link 2
2: Link 3
3: Link 7

KAKUMO requests the nextroute of the vehicle to TS

IV. VEHICLE MOTION IN VEL

TS returns the next route (3 links)of the vehicle to KAKUMO

In this chapter, we explain the vehicle motion in VEL(transferring control of vehicle, sharing vehicle information
between TS and KAKUMO). The procedure of vehicle motioncalculation in KAKUMO is divided into three processes as
explained below (seeFig.6).

TS forecasts the next route of the vehicle

Traffic Simulator
Fig.8 Mechanism of the route choice

TSVehicle Information(Vehicle ID,Position(Link ID, Lane ID),
Distance from the stop line
Vehicle ID,
Next Route(3 Link ID)

KAKUMO

Communications protocol : TCP/IP

Fig.9 Communication data (in the KAKUMO area)

C. PROCESS 3: LEAVING THE KAKUMO AREA
When a vehicle leaves the KAKUMO area, the vehicle
information is transferred from KAKUMO to TS andKAKUMO handles the control of the vehicle to TS (seeFig.10).

Fig.6 Three processes of Vehicle Motion

A. PROCESS 1:ENTRY TO THE KAKUMO AREA
When a vehicle enters into the KAKUMO area, the vehicle- 741 -TSVehicle ID,Vehicle Type ID,Position(Link ID, Lane ID)

KAKUMO

Communications protocol : TCP/IP

Fig.10 Communication data (leaving the KAKUMO area)

V. FUTURE WORKSFirst, we will verify the relationship between TS and KAKUMO to ensure thestability (in terms of traffic phenomena) of vehicles handled in the transitionarea around KAKUMO boundary.
Then we will consider the Human Factors in the current vehicle motion models
such as acceleration, response time and driving experience. We will also
develop a vehicle motion model structure that can incorporate data collectedfrom the examinee who is driving the DS.

REFERENCES(1) R. Horiguchi, M. Katakura, H. Akahane and M. Kuwahara, “ADevelopment of a Traffic Simulator for Urban Road Networks: AVENUE”,
Proceedings of Vehicle Navigation & Information Systems, Yokohama,pp.245-250, 1994
(2) R. Horiguchi, M. Kuwahara, M. Katakura, H. Akahane and H. Ozaki, “ANetwork Simulation Model for Impact Studies of Traffic Management'AVENUE Ver. 2”, Proceedings of the 3rd Annual World Congress onIntelligent Transport Systems, Orlando, 1996
(3) T. Yoshii and M. Kuwahara, “SOUND: A Traffic Simulation Model for
Oversaturated Traffic Flow on Urban Expressways”, Preprint at 7th World- 742 -