Freight vehicle travel time prediction using sparse Gaussian processes regression with trajectory data

Xia Li; Ruibin Bai

doi:10.1007/978-3-319-46257-8_16

Freight vehicle travel time prediction using sparse Gaussian processes regression with trajectory data

Xia Li^*
, Ruibin Bai

^*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

Travel time prediction is important for freight transportation companies. Accurate travel time prediction can help these companies make better planning and task scheduling. For several reasons, most companies are not able to obtain traffic flow data from traffic management authorities, but a large amount of trajectory data were collected everyday which has not been fully utilised. In this study, we aim to fill this gap and performed travel time prediction for freight vehicles at individual level using sparse Gaussian processes regression (SGPR) models with trajectory data. The results show that the prediction performance can be gradually improved by adding more mean speed estimates of traveled distance from the first 5 min as the real-time information. The overall performances of SGPR models are very similar to full GP, supported vector regression (SVR) and artificial neural network (ANN) models. The computational complexity of SGPR models is O(mn2), and it does not require lengthy model fitting process as SVR and ANN. This makes GP models more practicable for real-world practice in large-scale transportation data analyses.

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning - 17th International Conference, IDEAL 2016, Proceedings
Editors	Daoqiang Zhang, Yang Gao, Hujun Yin, Bin Li, Yun Li, Ming Yang, Frank Klawonn, Antonio J. Tallón-Ballesteros
Publisher	Springer Verlag
Pages	143-153
Number of pages	11
ISBN (Print)	9783319462561
DOIs	https://doi.org/10.1007/978-3-319-46257-8_16
Publication status	Published - 2016
Event	17th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2016 - Yangzhou, China Duration: 12 Oct 2016 → 14 Oct 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9937 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2016
Country/Territory	China
City	Yangzhou
Period	12/10/16 → 14/10/16

UN SDGs

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

SDG 11 Sustainable Cities and Communities

Free Keywords

Freight vehicle
Gaussian Processes
Machine learning
Sparse approximation
Trajectory data
Travel time prediction

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-46257-8_16

Cite this

Li, X., & Bai, R. (2016). Freight vehicle travel time prediction using sparse Gaussian processes regression with trajectory data. In D. Zhang, Y. Gao, H. Yin, B. Li, Y. Li, M. Yang, F. Klawonn, & A. J. Tallón-Ballesteros (Eds.), Intelligent Data Engineering and Automated Learning - 17th International Conference, IDEAL 2016, Proceedings (pp. 143-153). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9937 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46257-8_16

Li, Xia ; Bai, Ruibin. / Freight vehicle travel time prediction using sparse Gaussian processes regression with trajectory data. Intelligent Data Engineering and Automated Learning - 17th International Conference, IDEAL 2016, Proceedings. editor / Daoqiang Zhang ; Yang Gao ; Hujun Yin ; Bin Li ; Yun Li ; Ming Yang ; Frank Klawonn ; Antonio J. Tallón-Ballesteros. Springer Verlag, 2016. pp. 143-153 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{bc71df1c5a844c0daa26aa80b4ce1f4f,

title = "Freight vehicle travel time prediction using sparse Gaussian processes regression with trajectory data",

abstract = "Travel time prediction is important for freight transportation companies. Accurate travel time prediction can help these companies make better planning and task scheduling. For several reasons, most companies are not able to obtain traffic flow data from traffic management authorities, but a large amount of trajectory data were collected everyday which has not been fully utilised. In this study, we aim to fill this gap and performed travel time prediction for freight vehicles at individual level using sparse Gaussian processes regression (SGPR) models with trajectory data. The results show that the prediction performance can be gradually improved by adding more mean speed estimates of traveled distance from the first 5 min as the real-time information. The overall performances of SGPR models are very similar to full GP, supported vector regression (SVR) and artificial neural network (ANN) models. The computational complexity of SGPR models is O(mn2), and it does not require lengthy model fitting process as SVR and ANN. This makes GP models more practicable for real-world practice in large-scale transportation data analyses.",

keywords = "Freight vehicle, Gaussian Processes, Machine learning, Sparse approximation, Trajectory data, Travel time prediction",

author = "Xia Li and Ruibin Bai",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 17th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2016 ; Conference date: 12-10-2016 Through 14-10-2016",

year = "2016",

doi = "10.1007/978-3-319-46257-8\_16",

language = "English",

isbn = "9783319462561",

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

publisher = "Springer Verlag",

pages = "143--153",

editor = "Daoqiang Zhang and Yang Gao and Hujun Yin and Bin Li and Yun Li and Ming Yang and Frank Klawonn and Tall{\'o}n-Ballesteros, \{Antonio J.\}",

booktitle = "Intelligent Data Engineering and Automated Learning - 17th International Conference, IDEAL 2016, Proceedings",

address = "Germany",

}

Li, X & Bai, R 2016, Freight vehicle travel time prediction using sparse Gaussian processes regression with trajectory data. in D Zhang, Y Gao, H Yin, B Li, Y Li, M Yang, F Klawonn & AJ Tallón-Ballesteros (eds), Intelligent Data Engineering and Automated Learning - 17th International Conference, IDEAL 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9937 LNCS, Springer Verlag, pp. 143-153, 17th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2016, Yangzhou, China, 12/10/16. https://doi.org/10.1007/978-3-319-46257-8_16

Freight vehicle travel time prediction using sparse Gaussian processes regression with trajectory data. / Li, Xia; Bai, Ruibin.
Intelligent Data Engineering and Automated Learning - 17th International Conference, IDEAL 2016, Proceedings. ed. / Daoqiang Zhang; Yang Gao; Hujun Yin; Bin Li; Yun Li; Ming Yang; Frank Klawonn; Antonio J. Tallón-Ballesteros. Springer Verlag, 2016. p. 143-153 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9937 LNCS).

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

TY - GEN

T1 - Freight vehicle travel time prediction using sparse Gaussian processes regression with trajectory data

AU - Li, Xia

AU - Bai, Ruibin

N1 - Publisher Copyright: © Springer International Publishing AG 2016.

PY - 2016

Y1 - 2016

N2 - Travel time prediction is important for freight transportation companies. Accurate travel time prediction can help these companies make better planning and task scheduling. For several reasons, most companies are not able to obtain traffic flow data from traffic management authorities, but a large amount of trajectory data were collected everyday which has not been fully utilised. In this study, we aim to fill this gap and performed travel time prediction for freight vehicles at individual level using sparse Gaussian processes regression (SGPR) models with trajectory data. The results show that the prediction performance can be gradually improved by adding more mean speed estimates of traveled distance from the first 5 min as the real-time information. The overall performances of SGPR models are very similar to full GP, supported vector regression (SVR) and artificial neural network (ANN) models. The computational complexity of SGPR models is O(mn2), and it does not require lengthy model fitting process as SVR and ANN. This makes GP models more practicable for real-world practice in large-scale transportation data analyses.

AB - Travel time prediction is important for freight transportation companies. Accurate travel time prediction can help these companies make better planning and task scheduling. For several reasons, most companies are not able to obtain traffic flow data from traffic management authorities, but a large amount of trajectory data were collected everyday which has not been fully utilised. In this study, we aim to fill this gap and performed travel time prediction for freight vehicles at individual level using sparse Gaussian processes regression (SGPR) models with trajectory data. The results show that the prediction performance can be gradually improved by adding more mean speed estimates of traveled distance from the first 5 min as the real-time information. The overall performances of SGPR models are very similar to full GP, supported vector regression (SVR) and artificial neural network (ANN) models. The computational complexity of SGPR models is O(mn2), and it does not require lengthy model fitting process as SVR and ANN. This makes GP models more practicable for real-world practice in large-scale transportation data analyses.

KW - Freight vehicle

KW - Gaussian Processes

KW - Machine learning

KW - Sparse approximation

KW - Trajectory data

KW - Travel time prediction

UR - https://www.scopus.com/pages/publications/84989837654

U2 - 10.1007/978-3-319-46257-8_16

DO - 10.1007/978-3-319-46257-8_16

M3 - Conference contribution

AN - SCOPUS:84989837654

SN - 9783319462561

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

SP - 143

EP - 153

BT - Intelligent Data Engineering and Automated Learning - 17th International Conference, IDEAL 2016, Proceedings

A2 - Zhang, Daoqiang

A2 - Gao, Yang

A2 - Yin, Hujun

A2 - Li, Bin

A2 - Li, Yun

A2 - Yang, Ming

A2 - Klawonn, Frank

A2 - Tallón-Ballesteros, Antonio J.

PB - Springer Verlag

T2 - 17th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2016

Y2 - 12 October 2016 through 14 October 2016

ER -

Li X, Bai R. Freight vehicle travel time prediction using sparse Gaussian processes regression with trajectory data. In Zhang D, Gao Y, Yin H, Li B, Li Y, Yang M, Klawonn F, Tallón-Ballesteros AJ, editors, Intelligent Data Engineering and Automated Learning - 17th International Conference, IDEAL 2016, Proceedings. Springer Verlag. 2016. p. 143-153. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46257-8_16

Freight vehicle travel time prediction using sparse Gaussian processes regression with trajectory data

Abstract

Publication series

Conference

UN SDGs

Free Keywords

ASJC Scopus subject areas

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