Computer Science ›› 2019, Vol. 46 ›› Issue (7): 322-326.doi: 10.11896/j.issn.1002-137X.2019.07.049

• Interdiscipline & Frontier • Previous Articles     Next Articles

Traffic Flow Prediction Method Based on Spatio-Temporal Feature Mining

KONG Fan-yu1,ZHOU Yu-feng1,2,CHEN Gang3   

  1. (Chongqing Engineering Technology Research Center for Development Information Management, Chongqing Technology and Business University,Chongqing 400067,China)1
    (Postdoctoral Research Station of Management Science and Engineering,Nanjing University of Aeronautics & Astronautics,Nanjing 210016,China)2
    (College of Architecture and Urban Planning,Chongqing University,Chongqing 400045,China)3
  • Received:2018-06-29 Online:2019-07-15 Published:2019-07-15

Abstract: Traffic forecasting methods using neural networks and big data are emerging in an endless stream,but their prediction accuracy for traffic flow is usually inaccurate.In order to solve this problem,this paper proposed a traffic flow forecasting method based on spatio-temporal feature mining.This method makes use of improving convolutional neural network(CNN) to mine the spatial features of traffic flow,and utilizes recursive neural network to mine the temporal features of traffic flow,so that it can make full use of weekly/daily periodicity and spatial-temporal characteristics of traffic flow.In addition,the method also introduces a correlation-based model that can achieve automatic learning according to the past traffic flow.Experiment results show that the proposed method has higher prediction accuracy for traffic flow compared with some novel methods.

Key words: Automatic learning, Big data, Deep neural network, Improved convolutional neural network, Temporal-Spatial features, Traffic flow prediction

CLC Number: 

  • TP391
[1]RUI L L,LI Q M.Short-term Traffic Flow Prediction Algo- rithm Based on Combined Model [J].Journal of Electronics & Information Technology,2016,38(5):1227-1233.(in Chinese)
芮兰兰,李钦铭.基于组合模型的短时交通流量预测算法[J].电子与信息学报,2016,38(5):1227-1233.
[2]ZHENG X,CHEN W,WANG P,et al.Big Data for Social Transportation[J].IEEE Transactions on Intelligent Transportation Systems,2016,17(3):620-630.
[3]LV Y, DUAN Y, KANG W,et al.Traffic Flow Prediction With Big Data:A Deep Learning Approach[J].IEEE Transactions on Intelligent Transportation Systems,2015,16(2):865-873.
[4]LI L,LI Y,LI Z.Efficient missing data imputing for traffic flow by considering temporal and spatial dependence[J].Tramsportation Research Part C Emerging Technologies,2013,34(9):108-120.
[5]YU H B,SHEN Q,FENG G C.Introduce Numerical Solution to Visualize Convolutional Neuron Networks Based on Numerical Solution [J].Computer Science,2017,44(S1):146-150.(in Chinese)
俞海宝,沈琦,冯国灿.在反卷积网络中引入数值解可视化卷积神经网络[J].计算机科学,2017,44(S1):146-150.
[6]LUO J,JIANG Y,LIU X,et al.Multi-scale convolutional-recursive neural networks for RGB-D object recognition [J].Application Research of Computers,2017,34(9):2834-2837.(in Chinese)
骆健,蒋旻,刘星,等.多尺度卷积递归神经网络的RGB-D物体识别[J].计算机应用研究,2017,34(9):2834-2837.
[7]JIANG X,ADELI H.Dynamic Wavelet Neural Network Model for Traffic Flow Forecasting[J].Journal of Transportation Engineering,2005,131(10):771-779.
[8]MA X,TAO Z,WANG Y,et al.Long short-term memory neural network for traffic speed prediction using remote microwave sensor data[J].Transportation Research Part C,2015,54(3):187-197.
[9]QIAN W,YANG H H,SUN Y J.Kalman filtering traffic flow prediction research based on phase space re-construction [J].Computer Engineering and Applications,2016,52(14):37-41.(in Chinese)
钱伟,杨慧慧,孙玉娟.相空间重构的卡尔曼滤波交通流预测研究[J].计算机工程与应用,2016,52(14):37-41.
[10]CAO C T,LIN X H,XU L H.Short-term Traffic Flow Prediction Algorithm Based on FCM and Optimized SVR with Social Spider Optimization Algorithm [J].Journal of China Academy of Electronics and Information Technology,2017,12(1):52-59.(in Chinese)
曹成涛,林晓辉,许伦辉.联合FCM与群集蜘蛛优化SVR的短时交通流量预测[J].中国电子科学研究院学报,2017,12(1):52-59.
[11]ZHANG Y,HAGHANI A.A gradient boosting method to improve travel time prediction[J].Transportation Research Part C,2015,58(2):308-324.
[12]HUANG W,SONG G,HONG H,et al.Deep Architecture for Traffic Flow Prediction:Deep Belief Networks with Multitask Learning[J].IEEE Transactions on Intelligent Transportation Systems,2014,15(5):2191-2201.
[13]YU D,LIU Y,YU X.A Data Grouping CNN Algorithm for Short-Term Traffic Flow Forecasting.Web Technologies and Applications,2016,9931:92-103.
[14]DUAN Y,LV Y,WANG F Y.Travel time prediction with LSTM neural network[C]∥IEEE International Conference on Intelligent Transportation Systems.IEEE,2016:1053-1058.
[15]CHEN Z H,LAN Y Y,GUO J F,et al.Distributed Stochastic Gradient Descent with Discriminative Aggregating [J].Chinese Journal of Computers,2015,38(10):2054-2063.(in Chinese)
陈振宏,兰艳艳,郭嘉丰,等.基于差异合并的分布式随机梯度下降算法[J].计算机学报,2015,38(10):2054-2063.
[16]WEI S,WYNTER L.Rejoinder:real-time road traffic forecasting using regime-switching space-time models and adaptive lasso[M].John Wiley and Sons Ltd,2012:297-315. HAO Y,BAI Y P,ZHANG X F,et al.Application of Convolution Neural Network in SAR Target Recognition.Journal of Chongqing University of Technology(Natural Science),2018,32(5):210-215.(in Chinese)
郝岩,艳萍,张校非,等.卷积神经网络在SAR目标识别中的应用.重庆理工大学学报(自然科学),2018,32(5):210-215.
[1] HE Qiang, YIN Zhen-yu, HUANG Min, WANG Xing-wei, WANG Yuan-tian, CUI Shuo, ZHAO Yong. Survey of Influence Analysis of Evolutionary Network Based on Big Data [J]. Computer Science, 2022, 49(8): 1-11.
[2] WANG Ming, PENG Jian, HUANG Fei-hu. Multi-time Scale Spatial-Temporal Graph Neural Network for Traffic Flow Prediction [J]. Computer Science, 2022, 49(8): 40-48.
[3] CHEN Jing, WU Ling-ling. Mixed Attribute Feature Detection Method of Internet of Vehicles Big Datain Multi-source Heterogeneous Environment [J]. Computer Science, 2022, 49(8): 108-112.
[4] WEI Hui, CHEN Ze-mao, ZHANG Li-qiang. Anomaly Detection Framework of System Call Trace Based on Sequence and Frequency Patterns [J]. Computer Science, 2022, 49(6): 350-355.
[5] GAO Jie, LIU Sha, HUANG Ze-qiang, ZHENG Tian-yu, LIU Xin, QI Feng-bin. Deep Neural Network Operator Acceleration Library Optimization Based on Domestic Many-core Processor [J]. Computer Science, 2022, 49(5): 355-362.
[6] JIAO Xiang, WEI Xiang-lin, XUE Yu, WANG Chao, DUAN Qiang. Automatic Modulation Recognition Based on Deep Learning [J]. Computer Science, 2022, 49(5): 266-278.
[7] SUN Xuan, WANG Huan-xiao. Capability Building for Government Big Data Safety Protection:Discussions from Technologicaland Management Perspectives [J]. Computer Science, 2022, 49(4): 67-73.
[8] WANG Mei-shan, YAO Lan, GAO Fu-xiang, XU Jun-can. Study on Differential Privacy Protection for Medical Set-Valued Data [J]. Computer Science, 2022, 49(4): 362-368.
[9] FAN Hong-jie, LI Xue-dong, YE Song-tao. Aided Disease Diagnosis Method for EMR Semantic Analysis [J]. Computer Science, 2022, 49(1): 153-158.
[10] WANG Jun, WANG Xiu-lai, PANG Wei, ZHAO Hong-fei. Research on Big Data Governance for Science and Technology Forecast [J]. Computer Science, 2021, 48(9): 36-42.
[11] YU Yue-zhang, XIA Tian-yu, JING Yi-nan, HE Zhen-ying, WANG Xiao-yang. Smart Interactive Guide System for Big Data Analytics [J]. Computer Science, 2021, 48(9): 110-117.
[12] WANG Li-mei, ZHU Xu-guang, WANG De-jia, ZHANG Yong, XING Chun-xiao. Study on Judicial Data Classification Method Based on Natural Language Processing Technologies [J]. Computer Science, 2021, 48(8): 80-85.
[13] ZHOU Xin, LIU Shuo-di, PAN Wei, CHEN Yuan-yuan. Vehicle Color Recognition in Natural Traffic Scene [J]. Computer Science, 2021, 48(6A): 15-20.
[14] LIU Dong, WANG Ye-fei, LIN Jian-ping, MA Hai-chuan, YANG Run-yu. Advances in End-to-End Optimized Image Compression Technologies [J]. Computer Science, 2021, 48(3): 1-8.
[15] WANG Xue-cen, ZHANG Yu, LIU Ying-jie, YU Ge. Evaluation of Quality of Interaction in Online Learning Based on Representation Learning [J]. Computer Science, 2021, 48(2): 207-211.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!