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Dynamic Traffic Congestion Simulation and Dissipation Control Based on Traffic Flow Theory Model and Neural Network Data Calibration Algorithm

机译：基于交通流理论模型和神经网络数据标定算法的动态交通拥堵仿真与耗散控制

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摘要

Traffic congestion is a common problem in many countries, especially in big cities. At present, China’s urban road traffic accidents occur frequently, the occurrence frequency is high, the accident causes traffic congestion, and accidents cause traffic congestion and vice versa. The occurrence of traffic accidents usually leads to the reduction of road traffic capacity and the formation of traffic bottlenecks, causing the traffic congestion. In this paper, the formation and propagation of traffic congestion are simulated by using the improved medium traffic model, and the control strategy of congestion dissipation is studied. From the point of view of quantitative traffic congestion, the paper provides the fact that the simulation platform of urban traffic integration is constructed, and a feasible data analysis, learning, and parameter calibration method based on RBF neural network is proposed, which is used to determine the corresponding decision support system. The simulation results prove that the control strategy proposed in this paper is effective and feasible. According to the temporal and spatial evolution of the paper, we can see that the network has been improved on the whole.
机译：在许多国家，交通拥堵是一个普遍的问题，特别是在大城市。当前，我国城市道路交通事故多发，发生频率高，事故引起交通拥堵，事故引起交通拥堵，反之亦然。交通事故的发生通常导致道路通行能力的下降和交通瓶颈的形成，引起交通拥堵。本文利用改进的中型交通模型对交通拥堵的形成和传播进行了仿真，研究了交通拥堵的控制策略。从定量交通拥堵的角度出发，提供了构建城市交通一体化仿真平台的事实，并提出了一种基于RBF神经网络的可行的数据分析，学习和参数标定方法，确定相应的决策支持系统。仿真结果证明了本文提出的控制策略是有效可行的。根据本文的时空演变，我们可以看到网络总体上得到了改善。

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