On-line Multi-step Prediction of Short Term Traffic Flow Based on GRU Neural Network
On-line Multi-step Prediction of Short Term Traffic Flow Based on GRU Neural Network
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DOI：
10.1145/3144789.3144804
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发表时间：
2017-07
期刊：
影响因子：
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通讯作者：
J. Guo;Zijun Wang;Huawei Chen
中科院分区：
文献类型：
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作者：
J. Guo;Zijun Wang;Huawei Chen
加强道路交通流量监测和预测，可以缓解道路交通拥堵，促进道路交通安全规划。提前多步预测交通流量的能力尤为重要。道路交通流监测数据具有不确定性和非线性的特点。而用现有的方法进行多步预测，误差会非常大。本文基于这些特征，提出了GRU神经网络和自相关分析进行多步预测。我们使该模型以实测实时数据为输入，动态更新网络，即在线预测，从而有效地、持续地工作。通过理论推导和仿真分析，表明所提出的GRU预测模型的预测精度得到了提高。该模型可作为多步交通预测的有效方法。
Strengthened road traffic flow monitoring and forecasting can ease road traffic congestion and facilitate road traffic safety planning. Multi-step ahead of the ability to predict the traffic flow is particularly important. The monitoring data of road traffic flow is characterized by uncertainty and non-linearity. And using the existing methods to carry out multi-step prediction error will be very large. In this paper, based on these feature, we propose GRU neural network and autocorrelation analysis for multi-step prediction. We make this model dynamically update the network with the input of the measured real-time data, namely on-line prediction, to work effectively and constantly. Through the theoretical derivation and simulation analysis, it is shown that the prediction accuracy of the proposed GRU prediction model is improved. The model can be used as an effective method for multi-step traffic prediction.