Abstract
Accurate transport destination is significant for improving efficiency in bulk logistics. But actually, the address information and the coordinate of latitude and longitude of transport destination are often incorrect due to manual input errors or vague address. Constantly generated logistics data including trucks’ trajectories and waybills allows for the possibility of calibrating transport destination by analyzing staying behaviors of trucks. In this paper, we propose a transport destination calibration framework, called TDCT. Through clustering stay points into stay areas and then merging nearest stay areas based on turn-off location, stay hotspot can be ensured properly located. Further, a binary classification method by combining behavior features and area features is present to distinguish transport destination from other types of stay hotspots. Finally, a demo system is built for a steel logistics company to showcase the effectiveness of TDCT.
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Acknowledgments
This research was supported by NSFC (Nos.62072180, U1911203 and U1811264).
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Zhu, K., Wu, T., Shen, W., Mao, J., Shi, Y. (2023). TDCT: Transport Destination Calibration Based on Waybill Trajectories of Trucks. In: Li, B., Yue, L., Tao, C., Han, X., Calvanese, D., Amagasa, T. (eds) Web and Big Data. APWeb-WAIM 2022. Lecture Notes in Computer Science, vol 13423. Springer, Cham. https://doi.org/10.1007/978-3-031-25201-3_33
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DOI: https://doi.org/10.1007/978-3-031-25201-3_33
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