Revealing Freight Vehicle Trip Chains and Travel Behavior: Insights from Heavy Duty Vehicle GPS Data

High-quality, well-structured trip chain data are essential for analyzing the daily activity patterns, travel behaviors, and logistical decisions of commercial vehicles, as well as for supporting sustainability-oriented freight management and low-carbon urban logistics. This study introduces a novel methodology for analyzing truck travel patterns using extensive GPS data, focusing on identifying freight trip chains and enhancing urban freight systems. A road-constrained clustering approach was developed to accurately identify vehicle stops and truck stop locations, addressing limitations in previous studies that struggled with misclassification. A trip chain reconstruction methodology was formulated, key characteristics were extracted and clustering techniques were applied to categorize trucks based on their travel behavior. A case study in Chongqing demonstrates that the proposed method outperforms traditional clustering algorithms, reducing misclassification rates in stop location identification. The findings reveal consistent trip chain patterns and distinct travel behaviors within truck groups. This research presents a data-driven framework that provides a foundation for optimizing logistics, fleet management, and low-carbon freight system planning. By enhancing the accuracy of trip chain analysis, this methodology contributes to the design of energy-efficient and sustainable urban freight systems, helping reduce emissions and foster eco-friendly logistics solutions.