Identifying intercity freight trip ends of heavy trucks from GPS data

Yitao Yang, Bin Jia, Xiao-Yong Yan, Jiangtao Li, Zhenzhen Yang and Ziyou Gao

Transportation Research Part E: Logistics and Transportation Review, 2022, vol. 157, issue C

Abstract: The intercity freight trips of heavy trucks are basic data for transportation system planning and management. In recent decades, extracting intercity freight trips from GPS data has gradually become the main alternative to traditional surveys. Identifying freight trip ends (origin and destination) is the first task in trip extraction. Although many trip end identification methods have been proposed in previous studies, most of these studies subjectively determined key parameters and ignored the complex characteristics of truck trajectory and freight activities. In this paper, we propose a data-driven trip end identification method based on massive GPS data of heavy trucks in China. First, we capture heavy truck trajectory characteristics under the influence of GPS drift to identify truck stops from GPS data. Second, we analyze the temporal characteristics of truck activities and use freight-related point-of-interest (POI) data and highway network GIS data to identify valid trip ends from truck stops. The results of method validation suggest that the accuracy of our proposed method is significantly improved in comparison with the benchmark methods. We further extract intercity freight trips from the identified trip ends and analyze the spatiotemporal characteristics of intercity freight trips in China.

Keywords: Intercity freight trip ends; Heavy truck; GPS data; Time threshold method; Spatiotemporal characteristics (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (8)

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DOI: 10.1016/j.tre.2021.102590

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