Short-Term Urban Traffic Flow Prediction Using Deep Spatio-Temporal Residual Networks

被引：0

作者：

Wu, Xingming ^{[1
]}

Ding, Siyi ^{[1
]}

Chen, Weihai ^{[1
]}

Wang, Jianhua ^{[1
]}

Chen, Peter C. Y. ^{[2
]}

机构：

[1] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China

[2] Natl Univ Singapore, Dept Mech Engn, Singapore 117576, Singapore

来源：

PROCEEDINGS OF THE 2018 13TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA 2018) | 2018年

关键词：

deep residual networks; spatio-temporal relationship analysis; traffic flow prediction; BAYESIAN NETWORK;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Traffic flow prediction, as the key technology of traffic guidance system (TGS), is of great importance to mitigate traffic congestion and city management. In view of the existing research mainly considering the adjacent area and ignoring the influence of far area on the current section, this paper presents a method, called ST-ResNet, for predicting urban traffic flow the predicts all roads in an area, which takes into account not only the time information but also the effects of nearby and beyond areas. We use two basic traffic parameters, volume and speed, as the input of the model to simultaneously predict the traffic volumes and average speed of the road segment. Experiment on all motorways and 'A' roads managed by the Highways Agency, known as the Strategic Road Network (SRN), in England demonstrate its great and precise accuracy of short-term traffic flow prediction.

引用

页码：1073 / 1078

页数：6

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