Using Deep Learning to Predict Short Term Traffic Flow: A Systematic Literature Review

被引:11
作者
Ali, Usman [1 ]
Mahmood, Tariq [1 ]
机构
[1] Inst Business Adm IBA, Karachi, Pakistan
来源
INTELLIGENT TRANSPORT SYSTEMS - FROM RESEARCH AND DEVELOPMENT TO THE MARKET UPTAKE, INTSYS 2017 | 2018年 / 222卷
关键词
Traffic flow prediction; Deep learning; Intelligent transport systems; Big data; MODEL;
D O I
10.1007/978-3-319-93710-6_11
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要
This paper systematically reviews Deep Learning-based methods for traffic flow prediction. We extracted 26 articles using a concrete methodology and reviewed them from two perspectives: first, the deep learning architecture used; and second, the datasets and data dimensions incorporated. Recent big data explosion caused by sensors, IoV, IoT and GPS technology needs traffic analytics using deep architectures. This survey reveals that the LSTM (Long Short-Term Memory) Neural Networks are the most commonly used architecture for short term traffic flow prediction due to their inherent ability to handle sequential data. Among the datasets, PeMS is the most commonly used for traffic flow prediction task. Today, Intelligent Transport Systems (ITS) are not limited to temporal data; spatial dimension is also incorporated along with weather data, and traffic sentiments from twitter, Facebook and Instagram to get better results. In the authors' knowledge, this is the first deep learning review in ITS domain.
引用
收藏
页码:90 / 101
页数:12
相关论文
共 43 条
[1]  
Alpaydin E, 2014, ADAPT COMPUT MACH LE, P1
[2]  
AMAP, WEB BAS SERV PROV CH
[3]  
Babu R.V., 2016, 10 IND C COMP VIS GR, P46
[4]   DYNAMIC PROGRAMMING [J].
BELLMAN, R .
SCIENCE, 1966, 153 (3731) :34-&
[5]   The Dempster-Shafer theory of evidence: an alternative approach to multicriteria decision modelling [J].
Beynon, M ;
Curry, B ;
Morgan, P .
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE, 2000, 28 (01) :37-50
[6]   Large-Scale Machine Learning with Stochastic Gradient Descent [J].
Bottou, Leon .
COMPSTAT'2010: 19TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STATISTICS, 2010, :177-186
[7]   Root mean square error (RMSE) or mean absolute error (MAE)? - Arguments against avoiding RMSE in the literature [J].
Chai, T. ;
Draxler, R. R. .
GEOSCIENTIFIC MODEL DEVELOPMENT, 2014, 7 (03) :1247-1250
[8]  
Chen YY, 2016, 2016 IEEE 19TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), P132, DOI 10.1109/ITSC.2016.7795543
[9]   An efficient realization of deep learning for traffic data imputation [J].
Duan, Yanjie ;
Lv, Yisheng ;
Liu, Yu-Liang ;
Wang, Fei-Yue .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2016, 72 :168-181
[10]  
Duan YJ, 2016, PROCEEDINGS 2016 IEEE INTERNATIONAL CONFERENCE ON SERVICE OPERATIONS AND LOGISTICS, AND INFORMATICS (SOLI), P223, DOI 10.1109/SOLI.2016.7551691