Short-term urban traffic flow prediction using deep spatio-temporal residual networks

Xingming Wu; Siyi Ding; Weihai Chen; Jianhua Wang; Peter C.Y. Chen

doi:10.1109/ICIEA.2018.8397870

Short-term urban traffic flow prediction using deep spatio-temporal residual networks

Xingming Wu
, Siyi Ding
, Weihai Chen^*
, Jianhua Wang
, Peter C.Y. Chen

^*Corresponding author for this work

School of Automation Science and Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1073-1078
Number of pages	6
ISBN (Electronic)	9781538637579
DOIs	https://doi.org/10.1109/ICIEA.2018.8397870
State	Published - 26 Jun 2018
Event	13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018 - Wuhan, China Duration: 31 May 2018 → 2 Jun 2018

Publication series

Name	Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018

Conference

Conference	13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018
Country/Territory	China
City	Wuhan
Period	31/05/18 → 2/06/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy
SDG 9 Industry, Innovation, and Infrastructure
SDG 11 Sustainable Cities and Communities

Keywords

deep residual networks
spatio-temporal relationship analysis
traffic flow prediction

Access to Document

10.1109/ICIEA.2018.8397870

Cite this

Wu, X., Ding, S., Chen, W., Wang, J., & Chen, P. C. Y. (2018). Short-term urban traffic flow prediction using deep spatio-temporal residual networks. In Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018 (pp. 1073-1078). (Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIEA.2018.8397870

Wu, Xingming ; Ding, Siyi ; Chen, Weihai et al. / Short-term urban traffic flow prediction using deep spatio-temporal residual networks. Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1073-1078 (Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018).

@inproceedings{ac372464fbb546bebee1bcdc5985c027,

title = "Short-term urban traffic flow prediction using deep spatio-temporal residual networks",

abstract = "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.",

keywords = "deep residual networks, spatio-temporal relationship analysis, traffic flow prediction",

author = "Xingming Wu and Siyi Ding and Weihai Chen and Jianhua Wang and Chen, \{Peter C.Y.\}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018 ; Conference date: 31-05-2018 Through 02-06-2018",

year = "2018",

month = jun,

day = "26",

doi = "10.1109/ICIEA.2018.8397870",

language = "英语",

series = "Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1073--1078",

booktitle = "Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018",

address = "美国",

}

Wu, X, Ding, S, Chen, W, Wang, J & Chen, PCY 2018, Short-term urban traffic flow prediction using deep spatio-temporal residual networks. in Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018. Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1073-1078, 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018, Wuhan, China, 31/05/18. https://doi.org/10.1109/ICIEA.2018.8397870

Short-term urban traffic flow prediction using deep spatio-temporal residual networks. / Wu, Xingming; Ding, Siyi; Chen, Weihai et al.
Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1073-1078 (Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Short-term urban traffic flow prediction using deep spatio-temporal residual networks

AU - Wu, Xingming

AU - Ding, Siyi

AU - Chen, Weihai

AU - Wang, Jianhua

AU - Chen, Peter C.Y.

PY - 2018/6/26

Y1 - 2018/6/26

N2 - 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.

AB - 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.

KW - deep residual networks

KW - spatio-temporal relationship analysis

KW - traffic flow prediction

UR - https://www.scopus.com/pages/publications/85050100837

U2 - 10.1109/ICIEA.2018.8397870

DO - 10.1109/ICIEA.2018.8397870

M3 - 会议稿件

AN - SCOPUS:85050100837

T3 - Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018

SP - 1073

EP - 1078

BT - Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018

Y2 - 31 May 2018 through 2 June 2018

ER -

Wu X, Ding S, Chen W, Wang J, Chen PCY. Short-term urban traffic flow prediction using deep spatio-temporal residual networks. In Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1073-1078. (Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018). doi: 10.1109/ICIEA.2018.8397870

Short-term urban traffic flow prediction using deep spatio-temporal residual networks

Abstract

Publication series

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UN SDGs

Keywords

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