Proposing a Model for Predicting Passenger Origin-Destination in Online Taxi-Hailing Systems

• URL: http://arxiv.org/abs/1910.08145v4
• Date: Sat, 3 Jun 2023 21:11:07 GMT
• Title: Proposing a Model for Predicting Passenger Origin-Destination in Online Taxi-Hailing Systems
• Authors: Pouria Golshanrad, Hamid Mahini, Behnam Bahrak
• Abstract summary: We present a model designed to forecast the origin and destination of travels within a specified time window. We employ K-means clustering in a four-dimensional space with a maximum cluster size constraint for origin and destination zones. 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-hour time windows and a 14% lower MAPE for 30-minute time windows.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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-hour time windows and a 14\% lower MAPE for 30-minute time windows.

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