Abstract

This study investigates the influence of spatial heterogeneity on urban transport demand forecasting. A two-stage framework combining cluster analysis and machine learning was applied to origin-destination trip data generated by a calibrated transport model of a large city. Origin-destination pairs were grouped according to travel conditions using K-Means clustering based on private transport trip length and public transport travel time. The identified clusters were subsequently analyzed with respect to travel costs, modal split, spatial distribution, and transport demand characteristics. Machine learning models were then developed to predict transport demand for public transport, private transport, and walking. Two forecasting strategies were compared: a unified model trained on the entire dataset and cluster-specific models trained separately for each identified cluster. The results revealed significant spatial heterogeneity in travel conditions and transport demand structure. Cluster-specific machine-learning models reduced prediction errors for public and private transport demand, while the magnitude of improvement varied across travel modes and evaluation metrics. The findings demonstrate that accounting for spatial heterogeneity influences transport demand prediction performance, with the greatest improvements observed for public and private transport demand, whereas the effect was less pronounced for walking demand.

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