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Modeling Population Spatial-Temporal Distribution Using Taxis Origin and Destination Data

Author

Listed:
  • Fatema Rahimi

    (Geoinformation Technology Center of Excellence, Faculty of Geodesy & Geomatics Engineering, K.N. Toosi University of Technology, Tehran 19697, Iran
    These authors contributed equally to this work.)

  • Abolghasem Sadeghi-Niaraki

    (Geoinformation Technology Center of Excellence, Faculty of Geodesy & Geomatics Engineering, K.N. Toosi University of Technology, Tehran 19697, Iran
    Department of Computer Science and Engineering, and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 143-747, Korea
    These authors contributed equally to this work.)

  • Mostafa Ghodousi

    (Geoinformation Technology Center of Excellence, Faculty of Geodesy & Geomatics Engineering, K.N. Toosi University of Technology, Tehran 19697, Iran)

  • Soo-Mi Choi

    (Department of Computer Science and Engineering, and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 143-747, Korea)

Abstract

During dangerous circumstances, knowledge about population distribution is essential for urban infrastructure architecture, policy-making, and urban planning with the best Spatial-temporal resolution. The spatial-temporal modeling of the population distribution of the case study was investigated in the present study. In this regard, the number of generated trips and absorbed trips using the taxis pick-up and drop-off location data was calculated first, and the census population was then allocated to each neighborhood. Finally, the Spatial-temporal distribution of the population was calculated using the developed model. In order to evaluate the model, a regression analysis between the census population and the predicted population for the time period between 21:00 to 23:00 was used. Based on the calculation of the number of generated and the absorbed trips, it showed a different spatial distribution for different hours in one day. The spatial pattern of the population distribution during the day was different from the population distribution during the night. The coefficient of determination of the regression analysis for the model (R 2 ) was 0.9998, and the mean squared error was 10.78. The regression analysis showed that the model works well for the nighttime population at the neighborhood level, so the proposed model will be suitable for the day time population.

Suggested Citation

  • Fatema Rahimi & Abolghasem Sadeghi-Niaraki & Mostafa Ghodousi & Soo-Mi Choi, 2021. "Modeling Population Spatial-Temporal Distribution Using Taxis Origin and Destination Data," Sustainability, MDPI, vol. 13(7), pages 1-13, March.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:7:p:3727-:d:525016
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    Cited by:

    1. Lianchun Zhao & Chengzhang Zhao & Jiajing Huang, 2022. "Spatial Dynamics and Determinants of Population Urbanization in the Upper Reaches of the Yellow River," Land, MDPI, vol. 11(9), pages 1-12, August.

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