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author:

Fang, J. (Fang, J..) [1] | Chen, W. (Chen, W..) [2] | Xu, M. (Xu, M..) [3] | Liu, Y. (Liu, Y..) [4] | Bi, T. (Bi, T..) [5]

Indexed by:

Scopus

Abstract:

As a critical application in intelligent transportation systems, traffic state prediction still faces various challenges, such as unsatisfactory capability of utilizing multi-source data and modeling spatiotemporal network relevancies. Therefore, we propose a trajectory-based multi-task multi-graph convolutional network (Tr-MTMGN), a novel spatiotemporal deep learning framework for traffic state prediction on a citywide scale. This method firstly mines the underlying information from vehicle trajectories and designs a multi-graph convolution block to investigate spatial correlations. Sequentially, the multi-head self-attention layer is integrated into the multi-task learning framework to capture the temporal dependencies of the traffic state. The proposed model was evaluated on field data collected in Zhangzhou, China, and demonstrated superior performance when compared with existing state-of-the-art baselines. © National Academy of Sciences: Transportation Research Board 2023.

Keyword:

artificial intelligence big data data analytics data and data science deep learning neural networks

Community:

  • [ 1 ] [Fang J.]Department of Transportation Engineering, Fuzhou University, Fujian, Fuzhou, China
  • [ 2 ] [Chen W.]Digital Development Department, Fujian Expressway Network Operation Co., Ltd, Fujian, Fuzhou, China
  • [ 3 ] [Xu M.]Intelligent Transport System Research Center, Wuhan University of Technology, Hubei, Wuhan, China
  • [ 4 ] [Liu Y.]Hajim School of Engineering and Applied Sciences, University of Rochester, NY, Rochester, China
  • [ 5 ] [Bi T.]Department of Computer Science, Maynooth University, North Kildare, Ireland

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Source :

Transportation Research Record

ISSN: 0361-1981

Year: 2023

Issue: 4

Volume: 2678

Page: 659-673

1 . 6

JCR@2023

1 . 6 0 0

JCR@2023

ESI HC Threshold:35

JCR Journal Grade:3

CAS Journal Grade:4

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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