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

Pouria Golshanrad (), Hamid Mahini and Behnam Bahrak
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Pouria Golshanrad: University of Tehran
Hamid Mahini: University of Tehran
Behnam Bahrak: University of Tehran

Public Transport, 2025, vol. 17, issue 1, No 5, 151 pages

Abstract: 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.

Keywords: Passenger origin–destination prediction; Origin–destination flow prediction; Recurrent neural networks; Online taxi-hailing (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s12469-024-00370-x

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