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Rapid Transit Systems: Smarter Urban Planning Using Big Data, In-Memory Computing, Deep Learning, and GPUs

Author

Listed:
  • Muhammad Aqib

    (Department of Computer Science, FCIT, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Rashid Mehmood

    (High-Performance Computing Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Ahmed Alzahrani

    (Department of Computer Science, FCIT, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Iyad Katib

    (Department of Computer Science, FCIT, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Aiiad Albeshri

    (Department of Computer Science, FCIT, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Saleh M. Altowaijri

    (Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi Arabia)

Abstract

Rapid transit systems or metros are a popular choice for high-capacity public transport in urban areas due to several advantages including safety, dependability, speed, cost, and lower risk of accidents. Existing studies on metros have not considered appropriate holistic urban transport models and integrated use of cutting-edge technologies. This paper proposes a comprehensive approach toward large-scale and faster prediction of metro system characteristics by employing the integration of four leading-edge technologies: big data, deep learning, in-memory computing, and Graphics Processing Units (GPUs). Using London Metro as a case study, and the Rolling Origin and Destination Survey (RODS) (real) dataset, we predict the number of passengers for six time intervals (a) using various access transport modes to reach the train stations (buses, walking, etc.); (b) using various egress modes to travel from the metro station to their next points of interest (PoIs); (c) traveling between different origin-destination (OD) pairs of stations; and (d) against the distance between the OD stations. The prediction allows better spatiotemporal planning of the whole urban transport system, including the metro subsystem, and its various access and egress modes. The paper contributes novel deep learning models, algorithms, implementation, analytics methodology, and software tool for analysis of metro systems.

Suggested Citation

  • Muhammad Aqib & Rashid Mehmood & Ahmed Alzahrani & Iyad Katib & Aiiad Albeshri & Saleh M. Altowaijri, 2019. "Rapid Transit Systems: Smarter Urban Planning Using Big Data, In-Memory Computing, Deep Learning, and GPUs," Sustainability, MDPI, vol. 11(10), pages 1-33, May.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:10:p:2736-:d:230862
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    References listed on IDEAS

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    3. Pei Yin & Miaojuan Peng, 2023. "Station Layout Optimization and Route Selection of Urban Rail Transit Planning: A Case Study of Shanghai Pudong International Airport," Mathematics, MDPI, vol. 11(6), pages 1-29, March.
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    5. Zening Wu & Yanxia Shen & Huiliang Wang, 2019. "Assessing Urban Areas’ Vulnerability to Flood Disaster Based on Text Data: A Case Study in Zhengzhou City," Sustainability, MDPI, vol. 11(17), pages 1-15, August.

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