2021/06/18 by Yitao Yang, Yang, Yitao, Bin Jia +9 · 1 citation
Engineering · Social Sciences · #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Other Computer Science (cs.OH) #Urban Transport and Accessibility #Urban and Freight Transport Logistics
paper · pdf · doi:10.48550/arxiv.2106.09881
openalex publication_date 2021/06/18 · openalex created_date 2021/07/05 · openalex updated_date 2026/07/28
Intracity heavy truck freight trips are basic data in city freight system planning and management. In the big data era, massive heavy truck GPS trajectories can be acquired cost effectively in real-time. Identifying freight trip ends (origins and destinations) from heavy truck GPS trajectories is an outstanding problem. Although previous studies proposed a variety of trip end identification methods from different perspectives, these studies subjectively defined key threshold parameters and ignored the complex intracity heavy truck travel characteristics. Here, we propose a data-driven trip end identification method in which the speed threshold for identifying truck stops and the multilevel time thresholds for distinguishing temporary stops and freight trip ends are objectively defined. Moreover, an appropriate time threshold level is dynamically selected by considering the intracity activity patterns of heavy trucks. Furthermore, we use urban road networks and point-of-interest (POI) data to eliminate misidentified trip ends to improve method accuracy. The validation results show that the accuracy of the method we propose is 87.45%. Our method incorporates the impact of the city freight context on truck trajectory characteristics, and its results can reflect the spatial distribution and chain patterns of intracity heavy truck freight trips, which have a wide range of practical applications.