基于深度学习的城市道路旅行时间预测

doi:10.16182/j.issn1004731x.joss.201710011

系统仿真学报 ›› 2017, Vol. 29 ›› Issue (10): 2309-2316.doi: 10.16182/j.issn1004731x.joss.201710011

基于深度学习的城市道路旅行时间预测

张威威^1,2, 李瑞敏^1,2, 谢中教³

1.清华大学土木工程系,北京 100084;
2.国家道路交通管理工程技术研究中心,北京 100084;
3.山东省公安厅交通管理局,山东济南 250031

收稿日期:2017-05-15 发布日期:2020-06-04
作者简介:张威威(1990-),男,河南新蔡,硕士生,研究方向智能交通系统;李瑞敏(1979-),男,山东莱州,博士,副教授,研究方向为智能交通系统;谢中教(1969-),男,山东泰安,硕士,工程师,研究方向为交通与计算机应用。
基金资助:
北京市自然科学基金(8162024)

Travel Time Prediction of Urban Road Based on Deep Learning

Zhang Weiwei^1,2, Li Ruimin^{1,2, *}, Xie Zhongjiao³

1. Department of Civil Engineering, Tsinghua University, Beijing 100084, China;
2. National Road Traffic Management Engineering Technology Research Center, Beijing 100084, China;
3. Traffic Management Bureau of Shandong Public Security Bureau, Ji'nan 250031, China

Received:2017-05-15 Published:2020-06-04

摘要/Abstract

摘要： 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。

关键词: 交通工程, LSTM, 旅行时间预测, 空间相关性, 深度学习

Abstract: 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.

Key words: traffic engineering, LSTM, travel time prediction, spatial correlation, deep learning

中图分类号:

TP391

张威威, 李瑞敏, 谢中教. 基于深度学习的城市道路旅行时间预测[J]. 系统仿真学报, 2017, 29(10): 2309-2316.

Zhang Weiwei, Li Ruimin, Xie Zhongjiao. Travel Time Prediction of Urban Road Based on Deep Learning[J]. Journal of System Simulation, 2017, 29(10): 2309-2316.

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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

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