Development of Dynamic Traffic Simulation System for Tokyo
Metropolitan Expressways and Its Future Prospects
Hiroshi WARITA1), Shuichi KOYAMA1), Yuji TAMURA2), Ryota HORIGUCHI3), Masao KUWAHARA4)1) Metropolitan Expressway Company Limited, 2) Pacific Consultants Company Limited, 3) i-Transport LabCompany Limited, 4) Tohoku University【Keywords】
Mesoscopic model ; Urban Expressway network ; Dynamic traffic simulation ; Traffic conditions prediction ;Traffic incident【Abstract】
Tokyo Metropolitan Expressway (MEX) extends approximately 300 km and forms a large road network thatcarries approximately one million vehicles per day. Although traffic congestion has recently tended to bereduced as a result of the expansion of the road network, it has not completely been solved because vehicles areconcentrated in certain time zones and incidents frequently occur, such as traffic accidents (at least 30incidents per day). Travel time information with the aim of reducing traffic congestion and improving theservices for drivers is provided on MEX. Currently, the instantaneous travel time on MEX calculated on thebasis of real-time data is provided to drivers and its accuracy is relatively high. However, when the traffic
congestion suddenly expands owing to traffic accident, the provided instantaneous travel time may become
greatly different from the experienced travel time of the driver. A solution for this problem is to predict future
traffic conditions. Since 2005, Metropolitan Expressway Company Limited started to develop a dynamic
traffic simulation named Real Time Traffic Information by Dynamic Simulation on Urban Expressway (RISE).
In 2009, the RISE was connected to the Traffic Control System (TCS) of MEX, and since then, it has been
continuously operated on a trial basis. In this paper, we explain the outline of the RISE and its futureprospects.

Figure. Tokyo Metropolitan Expressway Network1

Firstly, the RISE outputs the traffic situation prediction results of two hours ahead at 5 minute intervals andconsists of four modules. The features of the RISE are as follows.
(a) Online data from vehicle detectors densely installed on MEX (approximately 300 m intervals) is imported.
(b)The input data is prepared from a large database, including past data accumulated over a long period (thosefrom vehicle detectors and related to accidents) and electronic toll collection origin-destination (ETC-OD) data(the rate of using ETC on MEX exceeds 90%).
(c)The incident management module is implemented to consider the effect of frequent accidents.
Here, we explain the incident management module in detail.
Traffic Control System

1. BaseSimulation

2. Estimation of
traffic volume onon‐ramps

3. Incident
management
module4.Simulation of Estimation
Traffic Control System

Figure. RISE flow chart
The incident management module consists of four submodules and mainly targets accidents.
The first submodule is used to estimate the period of lane closure. On the basis of accident information
registered in the TCS, the period of lane closure is estimated by statistical analysis. The initial value is set to50 min, which is the median of the period of lane closure among all accident cases, and the period of laneclosure is updated in accordance with the updated accident information, such as (1) the presence of fire engines
and ambulance cars, (2) the presence of emergency service vehicles, and (3) the number of vehicles involved inthe accident.
The second submodule is used to estimate the traffic capacity under lane restriction. For the section withaccident information registered, whether traffic congestion occurs or not is judged. When there is traffic
congestion, the degraded traffic capacity is set as the traffic capacity of the link. When there is no trafficcongestion, the traffic capacity of the link is set in accordance with the number of closed lanes.
The third submodule is used to estimate the variation in the on-ramp traffic volume. Here, a pattern table forthe relationship between the accident point and the rate of variation in the on-ramp traffic volume is used.
When an unusual on-ramp traffic volume is detected for an on-ramp that is considered to be affected by theaccident, the on-ramp traffic volume in the pattern table is modified to a lower or higher value for theestimated period of lane closure.
The fourth submodule is used to estimate the diverted traffic patterns on the off-ramps. Here, a pattern table
for the relationship between the accident point and the range of diverted traffic patterns on the off-ramps isused. When an unusual off-ramp traffic volume is detected for an off-ramp that is considered to be affected by
the accident, the destination in the pattern table is changed into a different one.
Next, the method of analyzing the effect of the incident management module is explained. In the case ofaccidents, the accuracy of the RISE when each submodule is implemented and operated is estimated andcompared with the accuracy when none of the four submodules are activated as the reference. The accuracy
is evaluated using the travel time as the index (whether the estimated travel time is within ±10 min of the true
travel time calculated using the time slice data from vehicle detectors). The results show that the accuracy is
only 17% in the case without the incident management module, whereas it increases to 71% when thesubmodules for estimating the period of lane closure and the traffic capacity are implemented. The accuracy
further increases to 83% when the submodules for estimating the variation in the on-ramp traffic volume and2

the diverted traffic patterns on the off-ramps are implemented. Therefore, it is demonstrated that theincident management module is indispensable for predicting short-term future traffic conditions on MEX inreal time.
Finally, we describe the future prospects of the RISE.
Addition to the accuracy improvement, for maintenance simple, automatic adjustment of the parameters isrequired as TCS, but solutions will not be found in a short period of time. Recently, we have examined a new
method of utilizing the RISE. It is to support the improvement of efficiency in traffic control works, such as
the evaluation of the effect of traffic restrictions arising from road construction, as an in-house utilization.
This offline utilization highlights the advantage of the RISE of providing past data without the step for
reproducing the situation of that time.
In the future, we will further improve the accuracy of the RISE and at the same time examine new methods ofutilizing the system to enable early full-scale operation and utilize the predicted traffic information in astepwise manner.
References［1］Keiko MUNAKATA, Yuji TAMURA, Hiroshi WARITA, Tomoyoshi SHIRAISHI. (2009). A Case Study
about the Traffic Prediction Under Accidents using Dynamic Traffic Simulation on Tokyo Metropolitan
Expressway, The 16th World Congress on Intelligent Transport Systems, Stockholm, Sweden3