| Project/Area Number |
04650473
|
|---|
| 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 | Hokkaido University |
|---|
Principal Investigator |
KAKU Terutoshi Faculty of Engineering Hokkaido University Professor, 工学部, 教授 (40001135)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
FUJIWARA Takashi Faculty of Engineering Hokkaido University Research Assistant, 工学部, 助手 (50109493)
HAGIWARA Toru Faculty of Engineering Hokkaido University Assistant Professor, 工学部, 助教授 (60172839)
NAKATSUJI Takashi Faculty of Engineering Hokkaido University Assistant Professor, 工学部, 助教授 (60123949)
|
|---|
| Project Period (FY) |
1992 – 1993
|
| Project Status |
Completed (Fiscal Year 1993)
|
| Budget Amount *help |
¥2,000,000 (Direct Cost: ¥2,000,000)
Fiscal Year 1993: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 1992: ¥1,500,000 (Direct Cost: ¥1,500,000)
|
|---|
| Keywords | Traffic Control System / Urban Network / Neural Network Model / Genetic Algorithm / Kohonen Feature Map / Artificial Intelligence / コホーネンフィーチュアマップ / 交通信号制御 / 交通流 / 最適化 |
|---|
| Research Abstract |
Using artificial intelligence techniques, we developed a stepwise method to optimize signal timing parameters, such as splits and offsets, on an urban street. The method is separated into two processes, a training process and an optimization process. In the training process, we used two neural network models ; a multilayr model and Kohonen Feature Map model. The former model builds an input-output relationship between the signal timing parameters and the objective variable. The latter model improves the computational efficiency and the estimation precision. In the optimization process, to avoid the entrapment into a local minimum, we used two artificial intelligence methods ; the Cauchy machine and a genetic algorithm. We adjusted the timing parameters so as to minimize the total weighted sum of delay time and stop frequencies. We compared the solutions by both artificial intelligence methods with those by a conventional method and confirmed that they were useful for establishing advanced traffic control systems in the future.
Next we described the macroscopic relationships among the traffic variables such as density, traffic flow rate, and space mean speed by a multilayr neural network model which was combined by the kohonen Feature Map technique. Comparison with analytical regression method proved that the neural network approach improves the regression coefficient a great deal and describe well the non-linear and discontinuous behavior among those variables. Such self-organizing relationships serve to simulate the traffic flow precisely and to detect incidents efficiently.
|
