Research on a Combined Neural Networks Prediction Model for Urban Traffic Volume

Zheng Zhou, Kun Huang
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Abstract

Urban traffic and transportation problems have become the main problem in the way of urban development. In order to resolve prediction problem of traffic volume, firstly, time series of traffic volume are reconstructed in the phase space in this paper, and correlative information in the traffic volume are extracted richly, then two-stage prediction system for traffic volume is applied: the first stage contains two parallel improved Elman neural networks, which are trained by standard back propagation algorithm, the second stage mixes prediction results of the first stage, which is trained by Karmarkarpsilas linear programming. Real example shows that predicted result of this method is famous, and this method has biggish applied potentials in the region of traffic control.
城市交通量组合神经网络预测模型研究
城市交通运输问题已成为影响城市发展的主要问题。为了解决交通量的预测问题,本文首先在相空间中重构交通量时间序列,丰富提取交通量中的相关信息,然后应用两阶段交通量预测系统:第一阶段包含两个并行的改进Elman神经网络,使用标准的反向传播算法进行训练;第二阶段混合第一阶段的预测结果,使用Karmarkarpsilas线性规划进行训练。实例表明,该方法的预测结果是显著的,在交通控制领域具有较大的应用潜力。
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