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Towards a General Prediction System for the Primary Delay in Urban Railways

Conference Paper


Abstract


  • Nowadays a large amount of data is collected from

    sensor devices across the cyber-physical networks. Accurate and

    reliable primary delay predictions are essential for rail operations

    management and planning. However, very few existing ‘big data’

    methods meet the specific needs in railways. We propose a

    comprehensive and general data-driven Primary Delay Prediction

    System (PDPS) framework, which combines General Transit Feed

    Specification (GTFS), Critical Point Search (CPS), and deep

    learning models to leverage the data fusion. Based on this

    framework, we have also developed an open source data collection

    and processing tool that reduces the barrier to the use of the different

    open data sources. Finally, we demonstrate an advanced deep

    learning model, the novel ConvLSTM Encoder-Decoder model with

    CPS for better primary delay predictions.

Authors


  •   Wu, Jianqing (external author)
  •   Zhou, Luping
  •   Cai, Chen (external author)
  •   Dong, Fang (external author)
  •   Shen, Jun
  •   Sun, Geng (external author)

Publication Date


  • 2019

Citation


  • Wu, J., Zhou, L., Cai, C., Dong, F., Shen, J. & Sun, G. (2019). Towards a General Prediction System for the Primary Delay in Urban Railways. 2019 IEEE Intelligent Transportation Systems Conference (ITSC) (pp. 3482-3487). United States: IEEE.

Scopus Eid


  • 2-s2.0-85076820873

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/3300

Start Page


  • 3482

End Page


  • 3487

Place Of Publication


  • United States

Abstract


  • Nowadays a large amount of data is collected from

    sensor devices across the cyber-physical networks. Accurate and

    reliable primary delay predictions are essential for rail operations

    management and planning. However, very few existing ‘big data’

    methods meet the specific needs in railways. We propose a

    comprehensive and general data-driven Primary Delay Prediction

    System (PDPS) framework, which combines General Transit Feed

    Specification (GTFS), Critical Point Search (CPS), and deep

    learning models to leverage the data fusion. Based on this

    framework, we have also developed an open source data collection

    and processing tool that reduces the barrier to the use of the different

    open data sources. Finally, we demonstrate an advanced deep

    learning model, the novel ConvLSTM Encoder-Decoder model with

    CPS for better primary delay predictions.

Authors


  •   Wu, Jianqing (external author)
  •   Zhou, Luping
  •   Cai, Chen (external author)
  •   Dong, Fang (external author)
  •   Shen, Jun
  •   Sun, Geng (external author)

Publication Date


  • 2019

Citation


  • Wu, J., Zhou, L., Cai, C., Dong, F., Shen, J. & Sun, G. (2019). Towards a General Prediction System for the Primary Delay in Urban Railways. 2019 IEEE Intelligent Transportation Systems Conference (ITSC) (pp. 3482-3487). United States: IEEE.

Scopus Eid


  • 2-s2.0-85076820873

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/3300

Start Page


  • 3482

End Page


  • 3487

Place Of Publication


  • United States