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The Study on Development and Applicability of Knowledge-Based Learning Algorithm for Route Guidance

基于知识学习的路径引导算法开发及适用性研究

基本信息

批准号：
18560519
负责人：
MIYAGI Toshihiko
金额：
$ 2.06万
依托单位：
Tohoku University (2007)Gifu University (2006)
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2006
资助国家：
日本
起止时间：
2006 至 2007
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18560519/
关键词：
Wardrop Equilibrim Hannan Consistency Nash Equilibrium Approchability Theorm Reinforcement Learning Adaptive Learning Repeated Games Markov Decision Process Hannan均衡交通均衡

项目摘要

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.

本研究旨在对学习算法进行基础性研究，以开发分布式导航系统。该研究通过理论分析和数值分析相结合的方法进行，得出了当所有驾驶员都使用该算法时所要实现的交通均衡特性的重要结果。我们方法的基本假设是，每个司机根据自己日常经验获得的出行信息来选择自己的路线。然而，旅行信息，例如从旅行起点到目的地的旅行时间，可能会随机波动，受到其他司机选择的影响。即使在这样的情况下，每个司机如果按照后悔匹配规则选择路线，也可以达到一定的稳定状态。这一结论不同于传统的假设驾驶员的完全信息的沃洛普均衡概念。这一结论表明，精心设计的信息采集系统可以让交通系统保持稳定，让驾驶员了解最佳路线，实现这一点的核心引擎可以称为智能驾驶算法。智能驾驶算法由马尔可夫决策过程和后悔匹配算法相结合组成。数值计算的效率取决于近似动态规划理论和随机逼近算法。我们通过将该算法应用于一个简单的网络来测试该算法，但是，我们假设可能的现实主义是链路成本函数。在所有情况下，我们都获得了成功的结果，但收敛速度非常慢。本研究表明，分布式车辆导航系统的可能性，即单个驾驶员使用GPS自动收集出行信息，并由机器学习自动引导到更好的路线。