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

Pouria Golshanrad, Hamid Mahini, Behnam Bahrak

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.