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Applicability of Neural Network Models to the Future Traffic Management Systems.

神经网络模型在未来交通管理系统中的适用性。

基本信息

批准号：
02805062
负责人：
NAKATSUJI Takashi
金额：
$ 0.96万
依托单位：
Hokkaido University
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (C)
财政年份：
1990
资助国家：
日本
起止时间：
1990 至 1991
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-02805062/
关键词：
Traffic Control Neural Network Model Traffic Management System 交通マネ-ジメントシステム

项目摘要

This study is mainly concerned with developing of a self-organizing traffic control system using neural network models. To realize a self-organizing traffic management system, neural network models were introduced. First, a multilayered neural network model, consisting of both a training process and an optimization process, was applied to an optimal traffic control problem of a single intersection. It was shown that in the training process a few iterations of the training operation by the backpropagation method was able to build up a steady input-output relationship. In the optimization process a stepwise method that combined the Cauchy method with a feedback method was able to produce a good approximated sequence of control variables. Compared to results by the feedback method alone, this stepwise method was able to avoid entrapment into local minimums and reach a global minimum promptly.Second, to deal with the rapid increase of synaptic weights of neural networks for roads that cons … More ist of several intersections, a multiple split model, in which only neurons that were related to an intersection were connected to each other, was proposed. This multiple split model improve d not only the computation time but also the estimation precision for untrained patterns.Third, to deal with the optimization of offsetsand the variation of traffic situations, a complicated neural network, which has three input sources, one for splits, one for offsets and the other for inflow traffic volumes, was proposed. This model was classified into four types by how the neural networks for those input sources were connected.Finally an interconnected neural network model was applied to a general minimum cost flow problem that was closely associated with a route guidance system in dynamic traffic management systems. To obtain the optimal solution the Hopfield model was used. That is, synaptic weights and input biases of the neural system were formulated based on the assumption that the total traveling time corresponds to the total energy of the neural network system. Considered were constraints on link flow where each link flow does not exceed the link capacity, and where the average traveling time depends on link flow. It was found that by specifying proper weight factors of the objective function, the Hopfield algorithm was able to give approximated solutions that were in good agreement with analytical ones. Less

本研究主要涉及利用神经网络模型开发自组织交通控制系统。为了实现自组织交通管理系统，引入了神经网络模型。首先，一个多层神经网络模型，包括一个训练过程和一个优化过程，应用于一个单一的交叉口的最优交通控制问题。结果表明，在训练过程中，通过反向传播方法的训练操作的几个迭代能够建立一个稳定的输入输出关系。在优化过程中，一个逐步的方法，结合柯西法与反馈方法，能够产生一个很好的近似控制变量序列。与单纯反馈法相比，该方法能够避免陷入局部极小值，并能迅速收敛到全局极小值。其次，针对有条件道路的神经网络突触权值快速增加的问题，提出了一种基于神经网络的神经网络模型。 ...更多信息在多个交叉点的基础上，提出了一种多分裂模型，其中只有与交叉点相关的神经元相互连接。第三，针对交通流状态的变化和偏移量的优化问题，提出了一种复杂的神经网络模型，该网络具有三个输入源，一个是分流量，一个是偏移量，一个是流入交通量。根据输入源的神经网络连接方式将模型分为4类，最后将互连神经网络模型应用于动态交通管理系统中与路径诱导系统密切相关的一般最小费用流问题。为了获得最优解，使用Hopfield模型。也就是说，神经系统的突触权重和输入偏置是基于总行进时间对应于神经网络系统的总能量的假设来公式化的。考虑了路段流量的约束条件，其中每个路段流量不超过路段通行能力，平均旅行时间取决于路段流量。结果发现，通过指定适当的权重因子的目标函数，Hopfield算法能够给出近似的解决方案，是在良好的协议与解析的。少