基于门控递归单元神经网络的高速公路行程时间预测

刘松; 彭勇; 邵毅明; 宋乾坤

doi:10.21656/1000-0887.400187

基于门控递归单元神经网络的高速公路行程时间预测

doi: 10.21656/1000-0887.400187

¹重庆交通大学交通运输学院, 重庆 400074;²山地城市交通系统与安全实验室, 重庆 400074;³重庆交通大学数学系, 重庆 400074

基金项目: 教育部人文社会科学研究规划基金(17YJA630079)

详细信息

作者简介:
刘松(1986—)，男，博士生(E-mail: 515044261@qq.com);彭勇（1973—），男，教授，博士，硕士生导师(通讯作者. E-mail: pengyong@cqjtu.edu.cn);邵毅明(1955—)，男，教授，博士，博士生导师(E-mail: sym@cqjtu.edu.cn);宋乾坤( 1963—) ，男，教授，博士，博士生导师(E-mail: qiankunsong@163.com).

中图分类号: U491
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- 被引次数: 0
出版历程
- 收稿日期: 2019-06-14
- 修回日期: 2019-07-19
- 刊出日期: 2019-11-01

Expressway Travel Time Prediction Based on Gated Recurrent Unit Neural Networks

¹College of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, P.R.China;²Chongqing Key Lab of Traffic System & Safety in Mountain Cities, Chongqing 400074, P.R.China;³College of Mathematics and Statistics,Chongqing Jiaotong University, Chongqing 400074, P.R.China

摘要

摘要: 为了更高效地预测高速公路行程时间，以高速公路行程时间为研究对象，通过采集车辆在高速公路进出口收费站的刷卡数据获取行程时间，利用门控递归单元神经网络对行程时间进行预测.按照所设计的预测流程，利用广州市机场高速南线高速公路收费数据进行验证，结果显示，预测拟合效果较好，并与LSTM神经网路和BP神经网络进行了对比分析.结果表明：门控递归单元神经网络具有更好的预测准确度.
- 高速公路 /
- 行程时间预测 /
- 门控递归单元 /
- 神经网络
Abstract: To efficiently predict the travel time on the expressway, the travel time was studied with the gated recurrent neural network through collection of the swiping data of vehicles at toll gates on the expressway. By means of the developed prediction computer program, the effects of the proposed method were then tested with the charging data of the Guangzhou Airport south expressway. The results show that the prediction effects are satisfying. Comparison with the LSTM neural network and the BP neural network indicates that, the gated recurrent neural network is better in prediction accuracy.
- expressway /
- travel time prediction /
- gated recurrent unit /
- neural network

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参考文献(25)

[1]	JIWON M, KIM D K, KHO S Y, et al. Travel time prediction using K nearest neighbormethod with combined data from vehicle detector system and au-tomatic toll collection system[J]. Transportation Research Record Journal of the Transportation Research Board,2011,20(2256): 51-59．
[2]	ZHAO Jiandong, GAO Yuan, TANG Jinjin, et al. Highway travel time prediction using sparse tensor completion tactics and K -nearest neighbor pattern matching method[J]. Journal of Advanced Transportation,2018,2018： 5721058.
[3]	王翔, 陈小鸿, 杨祥妹. 基于K最近邻算法的高速公路短时行程时间预测[J]. 中国公路学报, 2015,28(1): 102-111.(WANG Xiang, CHEN Xiaohong, YANG Xiangmei. Short term prediction of expressway travel time based on K nearest neighbor algorithm[J]. China Journal of Highway and Transport,2015,28(1): 102-111.(in Chinese))
[4]	邢雪, 于德新, 田秀娟, 等. 基于数据挖掘的高速公路行程时间预测[J]. 华中科技大学学报(自然科学版), 2016,44(8): 36-40.(XING Xue, YU Dexin, TIAN Xiujuan, et al. Freeway travel time prediction based on clustering method with data mining[J]. Journal of Huazhong University of Science and Technology (Nature Science Edition),2016,44(8): 36-40.(in Chinese))
[5]	景立竹, 李群善, 许金良, 等. 基于v/C比和载重汽车混入率的高速公路基本路段车辆平均行程时间预测模型[J]. 长安大学学报(自然科学版), 2018,38(5): 106-113.(JING Lizhu, LI Qunshan, XU Jinliang, et al. Average travel time prediction model in basic expressway sections based on v/C ratio and truck percentage[J]. Journal of Chang’an University(Natural Science Edition),2018,38(5): 106-113.(in Chinese))
[6]	陈娇娜, 张翔, 张生瑞. 高速公路行程时间Bootstrap-KNN区间预测分析与实证[J]. 控制与决策, 2018,33(11): 2080-2086.(CHEN Jiaona, ZHANG Xiang, ZHANG Shengrui. Analysis and empirical study on highway travel time interval prediction based on Bootstrap-KNN[J]. Control and Decision,2018,33(11): 2080-2086.(in Chinese))
[7]	ZHANG Y, SHI W H, LIU Y C. Comparison of several traffic forecasting methods based on travel time index data on weekends[J]. Journal of Shanghai Jiaotong University (Science),2010,15(2): 188-193.
[8]	毕松, 车磊, 赵忠诚, 等. 城市路网路段行程时间预测研究综述[J]. 计算机仿真, 2014,31(7): 157-160.(BI Song, CHE Lei, ZHAO Zhongcheng, et al. A survey on the link travel time prediction for urban road net[J]. Computer Simulation,2014,31(7): 157-160.(in Chinese))
[9]	OH S, BYON Y J, JANG K, et al. Short-term travel-time prediction on highway: a review of the data-driven approach[J]. Transport Reviews,2015,35(1): 1-29.
[10]	KASAI M, WARITA H. Refinement of pattern-matching method for travel time prediction[J]. International Journal of Intelligent Transportation Systems Research,2014,13(2): 84-94.
[11]	王增平, 赵兵, 纪维佳, 等. 基于GRU-NN模型的短期负荷预测方法[J]. 电力系统自动化, 2019,43(5): 53-58.(WANG Zengping, ZHAO Bing, JI Weijia, et al. Short-term load forecasting method based on GRU-NN model[J]. Automation of Electric Power Systems,2019,43(5): 53-58.(in Chinese))
[12]	江安, 刘平礼, 李年银, 等. 小波神经网络模型预测二氧化碳+水溶液体系界面张力[J]. 应用数学和力学, 2017,38(10): 1136-1145.(JIANG An, LIU Pingli, LI Nianyin, et al. Prediction of interfacial tension between CO2 and brine with the wavelet neural network method[J]. Applied Mathematics and Mechanics,2017,38(10): 1136-1145.(in Chinese))
[13]	丁宏飞, 李演洪, 刘博, 等. 基于 BP神经网络与SVM的快速路行程时间组合预测研究[J]. 计算机应用研究, 2016,33(10): 2929-2932.(DING Hongfei, LI Yanhong, LIU Bo, et al. Expressway’s travel time prediction based on combined BP neural network and support vector machine approach[J]. Application Research of Computers,2016,33(10): 2929-2932.(in Chinese))
[14]	XIE J M, CHOI Y K. Hybrid traffic prediction scheme for intelligent transportation systems based on historical and real-time data[J]. International Journal of Distributed Sensor Networks,2017,13(11): 1-11.
[15]	李松江, 宋军芬, 杨华民, 等基于聚类分析的高速公路行程时间预测[J]. 计算机仿真, 2019,36(2): 384-388.(LI Songjiang, SONG Junfen, YANG Huamin, et al. Travel time prediction of freeway based on clustering analysis[J].Computer Simulation,2019,36(2): 384-388.(in Chinese))
[16]	HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation,1997,9(8) : 1735-1780.
[17]	PETERSEN N C, RODRIGUES F, PEREIRA F C. Multi-output bus travel time prediction with convolutional LSTM neural network[J]. Expert Systems With Applications,2019,120: 426-435.
[18]	DUAN Y, Lü Y, WANG F. Travel time prediction with LSTM neural network[C]//2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) . Rio de Janeiro, Brazil, USA, 2016.
[19]	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): 2309-2315.(ZHANG Weiwei, LI Ruimin, XIE Zhongjiao. Travel time prediction of urban road based on deep learning[J]. Journal of System Simulation,2017,29(10): 2309-2315.(in Chinese))
[20]	RAN X D, SHAN Z G, FANG Y F, et al. An LSTM-based method with attention mechanism for travel time prediction[J]. Sensors,2019,19(4): 861.
[21]	CHO K, MERRIENBOER B V, GULCEHRE C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[R/OL]. [2019-07-19]. https://arxiv.org/pdf/1406.1078.pdf.
[22]	CHUNG J, GULCEHRE C, CHO K H,et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[R/OL]. [2019-07-19]. https://arxiv.org/pdf/1412.3555v1.pdf.
[23]	王体迎, 时鹏超, 刘蒋琼, 等. 基于门限递归单元循环神经网络的交通流预测方法研究[J]. 重庆交通大学学报(自然科学版),2018,37(11): 76-82.(WANG Tiying, SHI Pengchao, LIU Jiangqiong, et al. Research on traffic flow prediction method based on gated recurrent unit recurrent neural network[J]. Journal of Chongqing Jiaotong University(Natural Science),2018,37(11): 76-82.(in Chinese))
[24]	刘明宇, 吴建平, 王钰博, 等. 基于深度学习的交通流量预测[J]. 系统仿真学报, 2018,30(11): 4100-4114.(LIU Mingyu, WU Jianping, WANG Yubo, et al. Traffic flow prediction based on deep learning[J]. Journal of System Simulation,2018,30(11): 4100-4114.(in Chinese))
[25]	KINGMA D P, BA J L. Adam: a method for stochastic optimization[R/OL]. [2019-07-19]. https://arxiv.org/pdf/1412.6980.pdf.