Road-Rail Intermodal Hubs Site Selection Based on Road Freight Demand Mining – A Case from Beijing-Tianjin-Hebei Region

Abstract

This study introduces a holistic framework for optimising road-rail intermodal hub locations based on real regional freight data and railway station information. The primary objective is to enhance railway transportation capacity, thereby facilitating the development of a low-carbon transport system. Research begins by scrutinising the freight landscape in the region, focusing on transport volume, freight intensity, goods types and average delivery distances. Subsequently, data mining techniques, including DBSCAN clustering and frequent itemset mining, are employed to uncover freight demand hotspots across both spatial and temporal dimensions. Based on these findings, a mathematical model for hub location selection is constructed, along with criteria for goods categories suitable for rail transportation. Ultimately, using the Beijing-Tianjin-Hebei region as a case study, 12 road-rail intermodal hubs are identified, along with the main cargo types best suited for rail transport within their respective service areas. This transition is expected to result in an annual reduction of 470,000 tons of regional carbon emissions. The proposed method framework provides valuable guidance and practical insights for the optimisation of freight structures in various regions. Furthermore, it aligns with contemporary environmental and sustainability objectives, contributing to the broader goal of establishing low-carbon transport systems.

Keywords

• intermodal transportation
• transportation site selection
• carbon emission
• data mining