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Passenger Flow Prediction Based on Land Use around Metro Stations: A Case Study

Author

Listed:
  • Ciyun Lin

    (Department of Traffic Information and Control Engineering, Jilin University, Changchun 130022, China)

  • Kang Wang

    (Department of Traffic Information and Control Engineering, Jilin University, Changchun 130022, China)

  • Dayong Wu

    (Texas A&M Transportation Institute, Texas A&M University, College Station, Texas, TX 77843, USA)

  • Bowen Gong

    (Department of Traffic Information and Control Engineering, Jilin University, Changchun 130022, China
    Jilin Engineering Research Center for ITS, Jilin University, Changchun 130022, China)

Abstract

High-density land uses cause high-intensity traffic demand. Metro as an urban mass transit mode is considered as a sustainable strategy to balance the urban high-density land uses development and the high-intensity traffic demand. However, the capacity of the metro cannot always meet the traffic demand during rush hours. It calls for traffic agents to reinforce the operation and management standard to improve the service level. Passenger flow prediction is the foremost and pivotal technology in improving the management standard and service level of metro. It is an important technological means in ensuring sustainable and steady development of urban transportation. This paper uses mathematical and neural network modeling methods to predict metro passenger flow based on the land uses around the metro stations, along with considering the spatial correlation of metro stations within the metro line and the temporal correlation of time series in passenger flow prediction. It aims to provide a feasible solution to predict the passenger flow based on land uses around the metro stations and then potentially improving the understanding of the land uses around the metro station impact on the metro passenger flow, and exploring the potential association between the land uses and the metro passenger flow. Based on the data source from metro line 2 in Qingdao, China, the perdition results show the proposed methods have a good accuracy, with Mean Absolute Percentage Errors (MAPEs) of 11.6%, 3.24%, and 3.86 corresponding to the metro line prediction model with Categorical Regression (CATREG), single metro station prediction model with Artificial Neural Network (ANN), and single metro station prediction model with Long Short-Term Memory (LSTM), respectively.

Suggested Citation

  • Ciyun Lin & Kang Wang & Dayong Wu & Bowen Gong, 2020. "Passenger Flow Prediction Based on Land Use around Metro Stations: A Case Study," Sustainability, MDPI, vol. 12(17), pages 1-22, August.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:17:p:6844-:d:402895
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    Cited by:

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    2. Yangyang Meng & Qingjie Qi & Jianzhong Liu & Wei Zhou, 2022. "Dynamic Evolution Analysis of Complex Topology and Node Importance in Shenzhen Metro Network from 2004 to 2021," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    3. Marek Drliciak & Jan Celko & Michal Cingel & Dusan Jandacka, 2020. "Traffic Volumes as a Modal Split Parameter," Sustainability, MDPI, vol. 12(24), pages 1-21, December.
    4. Pei Yin & Jing Cheng & Miaojuan Peng, 2022. "Analyzing the Passenger Flow of Urban Rail Transit Stations by Using Entropy Weight-Grey Correlation Model: A Case Study of Shanghai in China," Mathematics, MDPI, vol. 10(19), pages 1-23, September.
    5. Antonio A. Barreda-Luna & Juvenal Rodríguez-Reséndiz & Alejandro Flores Rangel & Omar Rodríguez-Abreo, 2022. "Neural Network and Spatial Model to Estimate Sustainable Transport Demand in an Extensive Metropolitan Area," Sustainability, MDPI, vol. 14(9), pages 1-14, April.

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