Abstract
Accurate location and address of destination are critical for bulk commodity transportation, which determines the service quality of the logistics applications such as transport task dispatching and route planning. But due to manual input errors of the operators and dynamic changes of the destination’s location, the address of destination is not always correct and complete. To tackle this issue, we propose Transport Destination Calibration framework based on Multi-task learning, called TDCM. To correctly pinpoint the locations of destinations that are close to each other but differ in size, we cluster stay points to get stay areas and then merge them based on road turn-off location to obtain stay hotspots. Further, to precisely recognize the transport destination for each waybill, we devise an end-to-end multi-task destination matching model by incorporating with an attention mechanism. It can identify all destinations’ instances and meanwhile can match them with the corresponding waybills’ addresses respectively. Experimental results on real-world steel logistics data demonstrate the effectiveness and superiority of TDCM.
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This work is supported by NSFC (Nos.62072180 and U191 1203).
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Ethical Statement
Our submission involves the development and evaluation of a multi-task learning methods for transport destination calibration. we have taken steps to ensure that our research does not infringe upon personal data privacy or contribute to any unethical practices such as those related to policing or military application.
We use trajectory, waybill and other datasets for experimental evaluation. Before the data set is used, some measures are implemented to ensure that any personal data has been anonymized or otherwise rendered non-identifiable. In summary, we recognize the importance of ethical considerations in machine learning and data mining and have taken steps to ensure that our work adheres to ethical principles.
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Wu, T., Zhu, K., Mao, J., Yang, M., Zhou, A. (2023). TDCM: Transport Destination Calibrating Based on Multi-task Learning. In: De Francisci Morales, G., Perlich, C., Ruchansky, N., Kourtellis, N., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14175. Springer, Cham. https://doi.org/10.1007/978-3-031-43430-3_17
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DOI: https://doi.org/10.1007/978-3-031-43430-3_17
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