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Proposing a model for predicting passenger origin–destination in online taxi-hailing systems

Author

Listed:
  • Pouria Golshanrad

    (University of Tehran)

  • Hamid Mahini

    (University of Tehran)

  • Behnam Bahrak

    (University of Tehran)

Abstract

Due to the significance of transportation planning, traffic management, and dispatch optimization, predicting passenger origin–destination has emerged as a crucial requirement for intelligent transportation systems management. In this study, we present a model designed to forecast the origin and destination of travels within a specified time window. To derive meaningful travel flows, we employ K-means clustering in a four-dimensional space with a maximum cluster size constraint for origin and destination zones. Given the large number of clusters, we utilize non-negative matrix factorization to reduce the number of travel clusters. Furthermore, we implement a stacked recurrent neural network model to predict the travel count in each cluster. A comparison of our results with existing models reveals that our proposed model achieves a 5–7% lower mean absolute percentage error (MAPE) for 1-h time windows and a 14% lower MAPE for 30-min time windows.

Suggested Citation

  • Pouria Golshanrad & Hamid Mahini & Behnam Bahrak, 2025. "Proposing a model for predicting passenger origin–destination in online taxi-hailing systems," Public Transport, Springer, vol. 17(1), pages 121-151, March.
    • Handle: RePEc:spr:pubtra:v:17:y:2025:i:1:d:10.1007_s12469-024-00370-x
    DOI: 10.1007/s12469-024-00370-x
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