Traffic Flow Prediction with Improved SOPIO-SVR Algorithm

Xuejun Cheng; Lei Ren; Jin Cui; Zhiqiang Zhang

doi:10.1007/978-3-319-61994-1_17

Traffic Flow Prediction with Improved SOPIO-SVR Algorithm

Xuejun Cheng
, Lei Ren^*
, Jin Cui
, Zhiqiang Zhang

^*Corresponding author for this work

School of Automation Science and Electrical Engineering

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

3 Scopus citations

Abstract

In urban public transport, the traffic flow prediction is a classical non-linear complicated optimization problem, which is very important for public transport system. With the rapid development of the big data, Smart card data of bus which is provided by millions of passengers traveling by bus across several days plays a more and more important role in our daily life. The issue that we address is whether the data mining algorithm and the intelligent optimization algorithm can be applied to forecast the traffic flow from big data of bus. In this paper, a novel algorithm which called mixed support vector regression with sub-space orthogonal pigeon-Inspired Optimization (SOPIO-MSVR) is used to predict the traffic flow and optimize the algorithm progress. Results show the SOPIO-MSVR algorithm outperforms other algorithms by a margin and is a competitive algorithm. And the research can make the significant contribution to the improvement of the transportation.

Original language	English
Title of host publication	Challenges and Opportunity with Big Data - 19th Monterey Workshop 2016, Revised Selected Papers
Editors	Lin Zhang, Lei Ren, Fabrice Kordon
Publisher	Springer Verlag
Pages	184-197
Number of pages	14
ISBN (Print)	9783319619934
DOIs	https://doi.org/10.1007/978-3-319-61994-1_17
State	Published - 2017
Event	19th Monterey Workshop on Challenges and Opportunity with Big Data, 2016 - Beijing, China Duration: 8 Oct 2016 → 11 Oct 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10228 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th Monterey Workshop on Challenges and Opportunity with Big Data, 2016
Country/Territory	China
City	Beijing
Period	8/10/16 → 11/10/16

UN SDGs

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

SDG 11 Sustainable Cities and Communities

Keywords

Classification model
SOPIO-MSVR
Traffic flow prediction

Access to Document

10.1007/978-3-319-61994-1_17

Cite this

Cheng, X., Ren, L., Cui, J., & Zhang, Z. (2017). Traffic Flow Prediction with Improved SOPIO-SVR Algorithm. In L. Zhang, L. Ren, & F. Kordon (Eds.), Challenges and Opportunity with Big Data - 19th Monterey Workshop 2016, Revised Selected Papers (pp. 184-197). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10228 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-61994-1_17

Cheng, Xuejun ; Ren, Lei ; Cui, Jin et al. / Traffic Flow Prediction with Improved SOPIO-SVR Algorithm. Challenges and Opportunity with Big Data - 19th Monterey Workshop 2016, Revised Selected Papers. editor / Lin Zhang ; Lei Ren ; Fabrice Kordon. Springer Verlag, 2017. pp. 184-197 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{2d49319539ac4f96a85ba6c1518f480c,

title = "Traffic Flow Prediction with Improved SOPIO-SVR Algorithm",

abstract = "In urban public transport, the traffic flow prediction is a classical non-linear complicated optimization problem, which is very important for public transport system. With the rapid development of the big data, Smart card data of bus which is provided by millions of passengers traveling by bus across several days plays a more and more important role in our daily life. The issue that we address is whether the data mining algorithm and the intelligent optimization algorithm can be applied to forecast the traffic flow from big data of bus. In this paper, a novel algorithm which called mixed support vector regression with sub-space orthogonal pigeon-Inspired Optimization (SOPIO-MSVR) is used to predict the traffic flow and optimize the algorithm progress. Results show the SOPIO-MSVR algorithm outperforms other algorithms by a margin and is a competitive algorithm. And the research can make the significant contribution to the improvement of the transportation.",

keywords = "Classification model, SOPIO-MSVR, Traffic flow prediction",

author = "Xuejun Cheng and Lei Ren and Jin Cui and Zhiqiang Zhang",

note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 19th Monterey Workshop on Challenges and Opportunity with Big Data, 2016 ; Conference date: 08-10-2016 Through 11-10-2016",

year = "2017",

doi = "10.1007/978-3-319-61994-1\_17",

language = "英语",

isbn = "9783319619934",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "184--197",

editor = "Lin Zhang and Lei Ren and Fabrice Kordon",

booktitle = "Challenges and Opportunity with Big Data - 19th Monterey Workshop 2016, Revised Selected Papers",

address = "德国",

}

Cheng, X, Ren, L, Cui, J & Zhang, Z 2017, Traffic Flow Prediction with Improved SOPIO-SVR Algorithm. in L Zhang, L Ren & F Kordon (eds), Challenges and Opportunity with Big Data - 19th Monterey Workshop 2016, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10228 LNCS, Springer Verlag, pp. 184-197, 19th Monterey Workshop on Challenges and Opportunity with Big Data, 2016, Beijing, China, 8/10/16. https://doi.org/10.1007/978-3-319-61994-1_17

Traffic Flow Prediction with Improved SOPIO-SVR Algorithm. / Cheng, Xuejun; Ren, Lei; Cui, Jin et al.
Challenges and Opportunity with Big Data - 19th Monterey Workshop 2016, Revised Selected Papers. ed. / Lin Zhang; Lei Ren; Fabrice Kordon. Springer Verlag, 2017. p. 184-197 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10228 LNCS).

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

TY - GEN

T1 - Traffic Flow Prediction with Improved SOPIO-SVR Algorithm

AU - Cheng, Xuejun

AU - Ren, Lei

AU - Cui, Jin

AU - Zhang, Zhiqiang

PY - 2017

Y1 - 2017

N2 - In urban public transport, the traffic flow prediction is a classical non-linear complicated optimization problem, which is very important for public transport system. With the rapid development of the big data, Smart card data of bus which is provided by millions of passengers traveling by bus across several days plays a more and more important role in our daily life. The issue that we address is whether the data mining algorithm and the intelligent optimization algorithm can be applied to forecast the traffic flow from big data of bus. In this paper, a novel algorithm which called mixed support vector regression with sub-space orthogonal pigeon-Inspired Optimization (SOPIO-MSVR) is used to predict the traffic flow and optimize the algorithm progress. Results show the SOPIO-MSVR algorithm outperforms other algorithms by a margin and is a competitive algorithm. And the research can make the significant contribution to the improvement of the transportation.

AB - In urban public transport, the traffic flow prediction is a classical non-linear complicated optimization problem, which is very important for public transport system. With the rapid development of the big data, Smart card data of bus which is provided by millions of passengers traveling by bus across several days plays a more and more important role in our daily life. The issue that we address is whether the data mining algorithm and the intelligent optimization algorithm can be applied to forecast the traffic flow from big data of bus. In this paper, a novel algorithm which called mixed support vector regression with sub-space orthogonal pigeon-Inspired Optimization (SOPIO-MSVR) is used to predict the traffic flow and optimize the algorithm progress. Results show the SOPIO-MSVR algorithm outperforms other algorithms by a margin and is a competitive algorithm. And the research can make the significant contribution to the improvement of the transportation.

KW - Classification model

KW - SOPIO-MSVR

KW - Traffic flow prediction

UR - https://www.scopus.com/pages/publications/85028528410

U2 - 10.1007/978-3-319-61994-1_17

DO - 10.1007/978-3-319-61994-1_17

M3 - 会议稿件

AN - SCOPUS:85028528410

SN - 9783319619934

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 184

EP - 197

BT - Challenges and Opportunity with Big Data - 19th Monterey Workshop 2016, Revised Selected Papers

A2 - Zhang, Lin

A2 - Ren, Lei

A2 - Kordon, Fabrice

PB - Springer Verlag

T2 - 19th Monterey Workshop on Challenges and Opportunity with Big Data, 2016

Y2 - 8 October 2016 through 11 October 2016

ER -

Cheng X, Ren L, Cui J, Zhang Z. Traffic Flow Prediction with Improved SOPIO-SVR Algorithm. In Zhang L, Ren L, Kordon F, editors, Challenges and Opportunity with Big Data - 19th Monterey Workshop 2016, Revised Selected Papers. Springer Verlag. 2017. p. 184-197. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-61994-1_17

Traffic Flow Prediction with Improved SOPIO-SVR Algorithm

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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