基于深度学习的城市道路旅行时间预测

Travel Time Prediction of Urban Road Based on Deep Learning

城市道路旅行时间预测是城市智能交通系统的重要支撑。选择深度学习中的四种长短期记忆神经网络(Long Short-Term Memory,LSTM)架构进行道路旅行时间的预测。固定LSTM隐藏层的节点数以确定模型的最佳输入长度;固定模型的输入长度,分别测试在不同的隐藏层节点数和考虑空间相关性的条件下四种LSTM模型的预测性能;将空间LSTM模型与传统BP(Back Propagation)神经网络等四种模型进行了对比和分析。结果表明相对于其他四种模型,考虑空间相关性的LSTM模型具有更好的拟合和训练能力。

Travel time prediction of urban road is a significant support for urban intelligent transportation system. Four types of LSTM neural network architecture were selected to predict the urban road travel time. The number of nodes in the LSTM hidden layer was fixed to determine the optimal input length of the model. The input length of the model was fixed and the predictive performance of the four LSTM models under different hidden layer nodes and considering spatial correlation were tested respectively. The performance of spatial LSTM model was compared with four traditional models, for example, BP neural network. The results show that the LSTM model with spatial correlation has better fitting and training ability than the four traditional models.