| Project/Area Number |
06650585
|
|---|
| Research Category |
Grant-in-Aid for General Scientific Research (C)
|
| Allocation Type | Single-year Grants |
|---|
| Research Field |
交通工学・国土計画
|
|---|
| Research Institution | SAITAMA University |
|---|
Principal Investigator |
KUBOTA Hisashi Saitama Univ., Dept.of Civil and Environmental Eng., Assoc.Professor, 工学部, 助教授 (80205145)
|
|---|
| Project Period (FY) |
1994 – 1995
|
| Project Status |
Completed (Fiscal Year 1995)
|
| Budget Amount *help |
¥2,300,000 (Direct Cost: ¥2,300,000)
Fiscal Year 1995: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 1994: ¥1,800,000 (Direct Cost: ¥1,800,000)
|
|---|
| Keywords | tiss-NET WIN / Traffic Impact / Route choice / Traffic simulation / Traffic assignment model |
|---|
| Research Abstract |
The aim of this study is to develop an innovative simulation system named "tiss-NET WIN (traffic impact study system for road NETwork for WINdows)", which combines both traffic assignment and car-by-car traffic simulation. This system would be quite useful for traffic impact study.
In this study, source program of tiss-NET WIN was originally developed on Windows. The outline of tiss-NET is as follows :
-Streets are divided into 5-meter-length "compartments" to express car by car movement in the street.
-Each vehicle proceeds from origin to the destination through minimum path that is calculated using actual record of trip time considering any kind of micro behavior including turning delay in intersections and influence of on-street parking.
-Result of the simulation is expressed by animation as well as by statistics.
Another focus of this study is to analyze the mechanism of route finding and/or route choice of car drivers. Investigators conducted some experiments of car driving by controlling the quality and quantity of information of drivers, in which subjects were asked to drive to the goal by himself.
The ultimate goal of this study is to develop the revised tiss-NET WIN which can consider the each driver's experience and characteristics. In this study, investigators developed 'the intersection discriminate model' as the first step to the goal.
|
