Exploring the Spatiotemporal Characteristics of Freight Transportation Using Truck GPS Data: A Case Study of Lianyungang City

Abstract

Freight transportation plays a critical role in national economic development. However, there remains a gap in understanding its spatiotemporal characteristics and their impact on efficiency and sustainability. Therefore, exploring the travel distribution and freight volumes of trucks is of significant support to the freight industry. Traditional road traffic estimation methods typically rely on video surveillance and traffic collection equipment. When faced with large-scale vehicle trajectory data, these methods often require complex trajectory reconstruction processes, which are both time-consuming and resource-intensive. In contrast, this study employs a data-driven approach utilizing truck GPS trajectory data from Lianyungang City to propose a novel method for estimating freight vehicle traffic volume. The method uses isochronous data extraction techniques, combined with key variables in traffic flow calculations, to comprehensively understand dynamic factors without the need for individual trajectory reconstruction. The error rate is kept within 5%, thus saving time and resources while maintaining robustness. The research findings indicate that, influenced by supply-side structural reforms in transportation, coastal port cities have a higher proportion of urban freight vehicle trips compared to inland cities. Inland cities, on the other hand, exhibit a greater proportion of intercity freight vehicle trips. In port cities, trucks are more likely to serve connections between freight yards and ports. Additionally, freight demand is closely linked to economic development levels, with high demand observed in urban core areas compared to surrounding regions. These findings provide strong support for optimizing freight structure, enhancing transportation efficiency, and reducing transportation costs.

Keywords

• GPS trajectory data
• traffic flow estimation
• spatiotemporal analysis