高速公路匝道计量控制中的人工神经网络模型分析

Chien-Hung Wei
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引用次数: 33

摘要

高速公路上的交通不仅随时间而且随空间而变化。因此,对高速公路上的动态交通模式进行建模是必要的,以便得出适当的计量控制策略。现有的方法不能有效地完成这一任务。由于具有学习能力,人工神经网络模型可以模拟典型的时间序列交通数据,然后扩展到捕获固有的时空相互关系。根据高速公路的交通特点,提出了由多个基本模块智能连接组成的增强型网络。神经网络模型的输入是高速公路路段每个时间段的交通状态,而输出对应于每个入口匝道的期望计量率。仿真结果表明,将所提出的神经网络模型应用于高速公路交通运行管理取得了令人鼓舞的效果。并讨论了进一步改进的可行方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of artificial neural network models for freeway ramp metering control

Traffic along a freeway varies not only with time but also with space. It is thus essential to model dynamic traffic patterns on the freeway in order to derive appropriate metering control strategies. Existing methods cannot fulfill this task effectively. Due to the learning capability, artificial neural network models are developed to simulate typical time series traffic data and then expanded to capture the inherent time–space interrelations. The augmented-type network is proposed that includes several basic modules intelligently affiliated according to traffic characteristics on the freeway. Inputs to neural network models are traffic states in each time period on the freeway segments while outputs correspond to the desired metering rate at each entrance ramp. The simulation outcomes indicate very encouraging achievements when the proposed neural network model is employed to govern the freeway traffic operations. Also discussed are feasible directions for further improvements.

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