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

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.
本研究的目的是对分布式导航系统的学习算法进行基础性的研究。通过理论和数值分析进行了研究,并获得了显着的结果,当所有的司机使用该算法的交通平衡的特性来实现。我们的方法的基本假设是,每个单独的司机选择他或她的路线的基础上获得的一个人的日常经验的旅行信息。然而,旅行信息,例如从旅行的起点到目的地的旅行时间,可能随机波动以受到其他驾驶员的选择的影响。即使在这样的情况下,如果每个驾驶员根据遗憾匹配规则选择路线,也可以达到一定的稳定状态。这一结论与传统的假设驾驶员完全信息的Wardrop均衡概念不同。这一结论意味着精心设计的信息采集系统可以使交通系统保持稳定,使驾驶员了解最佳路线,而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。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。我们通过将其应用于一个简单的网络来测试该算法,但是,我们假设链路成本函数可能是现实的。对于所有的情况下,我们都获得了成功的结果,但是,收敛速度非常缓慢。这项研究表明,分布式车辆导航系统的可能性,其中一个单独的司机收集旅行信息,通过使用GPS和机器学习自动引导到一个更好的路线。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Multi-agent learning models for route choices in transportation networks: An integrated approach of regret-based strategy and reinforcement learning
交通网络中路线选择的多智能体学习模型:基于遗憾的策略和强化学习的综合方法
  • DOI:
  • 发表时间:
    2006
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Miyagi;T
  • 通讯作者:
    T
渋滞波及モデルによるオンランプをもつ2車線道路の渋滞シミュレーション
使用拥堵扩散模型对带有入口匝道的双车道道路进行拥堵模拟
Analysis of the effects of acceleration lane length at merging by using micro-simulations
利用微观模拟分析合道时加速车道长度的影响
  • DOI:
  • 发表时间:
    2007
  • 期刊:
  • 影响因子:
    0
  • 作者:
    X. Wang;T. Miyagi;A. Takagi;and J. Ying
  • 通讯作者:
    and J. Ying
A simulation model for traffic behavior at merging sections in highways
高速公路合流路段交通行为仿真模型
  • DOI:
  • 发表时间:
    2007
  • 期刊:
  • 影响因子:
    0
  • 作者:
    X. Wang;T. Miyagi;J. Ying
  • 通讯作者:
    J. Ying
Prediction of traffic flows based on on-line learning
基于在线学习的交通流预测
  • DOI:
  • 发表时间:
    2006
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Miyagi;T.
  • 通讯作者:
    T.
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MIYAGI Toshihiko其他文献

MIYAGI Toshihiko的其他文献

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{{ truncateString('MIYAGI Toshihiko', 18)}}的其他基金

A Study on Dynamic Traffic Assignment Based on An Atomic Model of Route-Choice
基于路由选择原子模型的动态交通分配研究
  • 批准号:
    26420511
  • 财政年份:
    2014
  • 资助金额:
    $ 2.06万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
Theory of Reinforcement Learning and Algorithms of Route Choice in Transportation Networks
交通网络中的强化学习理论与路径选择算法
  • 批准号:
    22360201
  • 财政年份:
    2010
  • 资助金额:
    $ 2.06万
  • 项目类别:
    Grant-in-Aid for Scientific Research (B)
Non-surveying Construction of a 47 Interregional Input-Output Table and Calibration of SCGE Model
47个区域间投入产出表的非调查构建及SCGE模型的校正
  • 批准号:
    15560458
  • 财政年份:
    2003
  • 资助金额:
    $ 2.06万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
Sensitivity Analysis for Multiregional General Equilibrium Models
多区域一般均衡模型的敏感性分析
  • 批准号:
    13650582
  • 财政年份:
    2001
  • 资助金额:
    $ 2.06万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
Integration of Transportation Planning Process Combining with Demand Forecasting Process
交通规划流程与需求预测流程的集成
  • 批准号:
    11650545
  • 财政年份:
    1999
  • 资助金额:
    $ 2.06万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
A STUDY ON APPLIED NETWORK EQUILIBRIUM MODELS
应用网络均衡模型的研究
  • 批准号:
    07650618
  • 财政年份:
    1995
  • 资助金额:
    $ 2.06万
  • 项目类别:
    Grant-in-Aid for Scientific Research (C)
A formulation of spatial price equilibrium model and its computation procedure
空间价格均衡模型的建立及其计算过程
  • 批准号:
    63550387
  • 财政年份:
    1988
  • 资助金额:
    $ 2.06万
  • 项目类别:
    Grant-in-Aid for General Scientific Research (C)
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