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Intracity heavy truck freight trips are basic data in city freight systemplanning and management. In the big data era, massive heavy truck GPStrajectories can be acquired cost effectively in real-time. Identifying freighttrip ends (origins and destinations) from heavy truck GPS trajectories is anoutstanding problem. Although previous studies proposed a variety of trip endidentification methods from different perspectives, these studies subjectivelydefined key threshold parameters and ignored the complex intracity heavy trucktravel characteristics. Here, we propose a data-driven trip end identificationmethod in which the speed threshold for identifying truck stops and themultilevel time thresholds for distinguishing temporary stops and freight tripends are objectively defined. Moreover, an appropriate time threshold level isdynamically selected by considering the intracity activity patterns of heavytrucks. Furthermore, we use urban road networks and point-of-interest (POI)data to eliminate misidentified trip ends to improve method accuracy. Thevalidation results show that the accuracy of the method we propose is 87.45%.Our method incorporates the impact of the city freight context on trucktrajectory characteristics, and its results can reflect the spatialdistribution and chain patterns of intracity heavy truck freight trips, whichhave a wide range of practical applications.

Identifying intracity freight trip ends from heavy truck GPS trajectories