STCNN : A Spatio-Temporal Convolutional Neural Network for Long-Term Traffic Prediction

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationProceedings - 2019 20th International Conference on Mobile Data Management, MDM 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages226-233
ISBN (Print)9781728133638
Publication statusPublished - Jun 2019

Publication series

NameProceedings - IEEE International Conference on Mobile Data Management
Volume2019-June
ISSN (Print)1551-6245
ISSN (Electronic)2375-0324

Conference

Title20th IEEE International Conference on Mobile Data Management (MDM 2019)
LocationHong Kong Baptist University
PlaceHong Kong
Period10 - 13 June 2019

Abstract

As many location-based applications provide services for users based on traffic conditions, an accurate traffic prediction model is very significant, particularly for long-term traffic predictions (e.g., one week in advance). As far, long-term traffic predictions are still very challenging due to the dynamic nature of traffic. In this paper, we propose a model, called Spatio-Temporal Convolutional Neural Network (STCNN) based on convolutional long short-term memory units to address this challenge. STCNN aims to learn the spatio-temporal correlations from historical traffic data for long-term traffic predictions. Specifically, STCNN captures the general spatio-temporal traffic dependencies and the periodic traffic pattern. Further, STCNN integrates both traffic dependencies and traffic patterns to predict the long-term traffic. Finally, we conduct extensive experiments to evaluate STCNN on two real-world traffic datasets. Experimental results show that STCNN is significantly better than other state-of-the-art models.

Research Area(s)

  • Convolutional neural network, Long term traffic predictions, Periodic traffic patterns, Spatio-Temporal dependencies

Bibliographic Note

Information for this record is supplemented by the author(s) concerned.

Citation Format(s)

STCNN : A Spatio-Temporal Convolutional Neural Network for Long-Term Traffic Prediction. / He, Zhixiang; Chow, Chi-Yin; Zhang, Jia-Dong.

Proceedings - 2019 20th International Conference on Mobile Data Management, MDM 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 226-233 8788794 (Proceedings - IEEE International Conference on Mobile Data Management; Vol. 2019-June).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review