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Advanced Traffic Control System on Urban Road Network by Neural Network Models

基于神经网络模型的城市路网先进交通控制系统

基本信息

批准号：
04650473
负责人：
KAKU Terutoshi
金额：
$ 1.28万
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：
1992
资助国家：
日本
起止时间：
1992 至 1993
项目状态：
已结题

项目摘要

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.

利用人工智能技术，我们开发了一个逐步的方法来优化信号配时参数，如分裂和偏移，在城市街道。该方法分为两个过程，训练过程和优化过程。在训练过程中，我们使用了两个神经网络模型：多层模型和Kohonen特征映射模型。前者建立了信号配时参数与目标变量之间的输入输出关系。后一种模型提高了计算效率和估计精度。在优化过程中，为了避免陷入局部极小值，我们使用了两种人工智能方法：柯西机和遗传算法。我们调整的定时参数，以尽量减少总的加权和的延迟时间和停止频率。然后，通过一个结合Kohonen特征映射技术的多层神经网络模型描述了密度、交通流率和空间平均速度等交通变量之间的宏观关系。与解析回归方法相比，神经网络方法大大提高了回归系数，并能很好地描述变量间的非线性和不连续性。这种自组织关系有助于精确模拟交通流并有效检测事件。