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Traffic Congestion Prediction Based on Long-Short Term Memory Neural Network Models

机译：基于长时记忆神经网络模型的交通拥堵预测

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摘要

Predicting urban network congestion and exploring congestion mechanisms are vital for both transportation researchers and practitioners. The state-of-the-art studies rely on either mathematical equations or simulation techniques to depict the traffic congestion evolution. However, most of the existing studies tend to make simplified assumptions since transportation activities involve complex human factors which are difficult to represent or model accurately using mathematics-driven approaches. In this paper, long-short term memory neural networks (LSTM NN) are employed to interpret traffic congestion in terms of traffic speed. Traffic speed predictions are also made by considering both temporal and spatial correlation information. The proposed approach is tested on different links in one road network in Beijing, China. The results demonstrate the advantage of LSTM NN for analyzing the complex non-linear variations of traffic speeds as well as its promising prediction accuracy.
机译：预测城市网络拥堵并探索拥堵机制对于交通研究人员和从业人员都至关重要。最新的研究依靠数学方程式或仿真技术来描述交通拥堵的演变。但是，由于运输活动涉及复杂的人为因素，使用数学驱动的方法难以准确表示或建模，因此大多数现有研究倾向于简化假设。本文采用长短期记忆神经网络（LSTM NN）来解释交通拥堵的交通速度。通过同时考虑时间和空间相关信息来做出交通速度预测。该方法在中国北京的一个道路网中的不同链路上进行了测试。结果表明，LSTM NN的优势在于分析交通速度的复杂非线性变化及其有希望的预测准确性。

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