Research on Freight Pricing Mechanism of Shipping Companies Considering Supply Chain Management

Bao, Xuexin; Xing, Xiangchun; Zhang, Dan

doi:10.2112/si94-112.1

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Figure 10 shows the city freight hotspots of four case cities, from which we can see that most freight hotspots are concentrated in suburban areas, reflecting the suburbanization trend of heavy truck freight activities (Cidell, 2010). The accurate identification of city freight hotspots can provide supporting information for authorities to effectively manage freight enterprises (Bao et al, 2019), monitor environmental pollution (Kovac et al, 2020) and plan land use (Stevens et al, 2020).…”

Section: Results Of Intracity Freight Trip End Identificationmentioning

confidence: 99%

Identifying intracity freight trip ends from heavy truck GPS trajectories

Yang¹,

Jia²,

Yan³

et al. 2021

Preprint

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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.

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