| Project/Area Number |
18560519
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
交通工学・国土計画
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| Research Institution | Tohoku University (2007)
Gifu University (2006) |
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Principal Investigator |
MIYAGI Toshihiko Tohoku University, Graduate School of information Sciences, Professor (20092968)
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| Project Period (FY) |
2006 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥3,220,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥420,000)
Fiscal Year 2007: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2006: ¥1,400,000 (Direct Cost: ¥1,400,000)
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| Keywords | Wardrop Equilibrim / Hannan Consistency / Nash Equilibrium / Approchability Theorm / Reinforcement Learning / Adaptive Learning / Repeated Games / Markov Decision Process / Hannan均衡 / 交通均衡 |
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| Research Abstract |
This study aims a fundamental study of the learning algorithm to develop a distributed navigation system. The study was conducted through both theoretical and numerical analysis, and obtained significant results about a characteristic of the traffic equilibrium to be realized when all drivers used this algorithm. The basic assumption of our approach is that each individual driver chooses his or her route based on travel information obtained by one's daily experience. Travel information, far instance a travel time to a destination from an origin of a trip, however, can fluctuate stochastically to be affected by the choice of other drivers. Even with such a situation, each driver can reach a certain stable state if he chooses a route according to the regret matching rule. This conclusion is fir different from the conventional concept of Wardrop equilibrium where drivers' perfect information is assumed. This conclusion implies that a carefully designed information acquisition system allows the transportation system being stable and drivers to acquaint the best route.
The core engine that enables this maybe called the intelligent driving algorithm. The intelligent driving algorithm consists of a combination of Markov decision process and the regret matching algorithm. The efficiency of numerical calculation depends on the theory of approximate dynamic programming and stochastic approximation algorithm. We tested the algorithm by applying it to a simple network, but, we assumed possibly realist is link cost functions. For all cases, we obtained successful results; however, the rate of convergence was very slow.
This study suggests the possibility of the distributed vehicle navigation system, in which an individual driver collects travel information by self with using GPS and is automatically guided by machine learning to a better route.
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