TDCM: Transport Destination Calibrating Based on Multi-task Learning

Tao Wu; Kaixuan Zhu; Jiali Mao; Miaomiao Yang; Aoying Zhou

doi:10.1007/978-3-031-43430-3_17

TDCM: Transport Destination Calibrating Based on Multi-task Learning

Tao Wu, Kaixuan Zhu, Jiali Mao, Miaomiao Yang, Aoying Zhou

School of Data Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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.

Original language	English
Title of host publication	Machine Learning and Knowledge Discovery in Databases
Subtitle of host publication	Applied Data Science and Demo Track - European Conference, ECML PKDD 2023, Proceedings
Editors	Gianmarco De Francisci Morales, Francesco Bonchi, Claudia Perlich, Natali Ruchansky, Nicolas Kourtellis, Elena Baralis
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	276-292
Number of pages	17
ISBN (Print)	9783031434297
DOIs	https://doi.org/10.1007/978-3-031-43430-3_17
State	Published - 2023
Event	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023 - Turin, Italy Duration: 18 Sep 2023 → 22 Sep 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14175 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023
Country/Territory	Italy
City	Turin
Period	18/09/23 → 22/09/23

Keywords

Attention mechanism
Multi-task learning
Road turn-off location
Transport destination calibration

Access to Document

10.1007/978-3-031-43430-3_17

Cite this

Wu, T., Zhu, K., Mao, J., Yang, M., & Zhou, A. (2023). TDCM: Transport Destination Calibrating Based on Multi-task Learning. In G. De Francisci Morales, F. Bonchi, C. Perlich, N. Ruchansky, N. Kourtellis, & E. Baralis (Eds.), Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track - European Conference, ECML PKDD 2023, Proceedings (pp. 276-292). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14175 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-43430-3_17

Wu, Tao ; Zhu, Kaixuan ; Mao, Jiali et al. / TDCM : Transport Destination Calibrating Based on Multi-task Learning. Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track - European Conference, ECML PKDD 2023, Proceedings. editor / Gianmarco De Francisci Morales ; Francesco Bonchi ; Claudia Perlich ; Natali Ruchansky ; Nicolas Kourtellis ; Elena Baralis. Springer Science and Business Media Deutschland GmbH, 2023. pp. 276-292 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{2711851c1e544fccaec849bf652146fd,

title = "TDCM: Transport Destination Calibrating Based on Multi-task Learning",

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{\textquoteright}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{\textquoteright} instances and meanwhile can match them with the corresponding waybills{\textquoteright} addresses respectively. Experimental results on real-world steel logistics data demonstrate the effectiveness and superiority of TDCM.",

keywords = "Attention mechanism, Multi-task learning, Road turn-off location, Transport destination calibration",

author = "Tao Wu and Kaixuan Zhu and Jiali Mao and Miaomiao Yang and Aoying Zhou",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023 ; Conference date: 18-09-2023 Through 22-09-2023",

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doi = "10.1007/978-3-031-43430-3\_17",

language = "英语",

isbn = "9783031434297",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "276--292",

editor = "\{De Francisci Morales\}, Gianmarco and Francesco Bonchi and Claudia Perlich and Natali Ruchansky and Nicolas Kourtellis and Elena Baralis",

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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 G De Francisci Morales, F Bonchi, C Perlich, N Ruchansky, N Kourtellis & E Baralis (eds), Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track - European Conference, ECML PKDD 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14175 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 276-292, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023, Turin, Italy, 18/09/23. https://doi.org/10.1007/978-3-031-43430-3_17

TDCM: Transport Destination Calibrating Based on Multi-task Learning. / Wu, Tao; Zhu, Kaixuan; Mao, Jiali et al.
Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track - European Conference, ECML PKDD 2023, Proceedings. ed. / Gianmarco De Francisci Morales; Francesco Bonchi; Claudia Perlich; Natali Ruchansky; Nicolas Kourtellis; Elena Baralis. Springer Science and Business Media Deutschland GmbH, 2023. p. 276-292 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14175 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - TDCM

T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023

AU - Wu, Tao

AU - Zhu, Kaixuan

AU - Mao, Jiali

AU - Yang, Miaomiao

AU - Zhou, Aoying

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Attention mechanism

KW - Multi-task learning

KW - Road turn-off location

KW - Transport destination calibration

UR - https://www.scopus.com/pages/publications/85174439492

U2 - 10.1007/978-3-031-43430-3_17

DO - 10.1007/978-3-031-43430-3_17

M3 - 会议稿件

AN - SCOPUS:85174439492

SN - 9783031434297

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 276

EP - 292

BT - Machine Learning and Knowledge Discovery in Databases

A2 - De Francisci Morales, Gianmarco

A2 - Bonchi, Francesco

A2 - Perlich, Claudia

A2 - Ruchansky, Natali

A2 - Kourtellis, Nicolas

A2 - Baralis, Elena

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 18 September 2023 through 22 September 2023

ER -

Wu T, Zhu K, Mao J, Yang M, Zhou A. TDCM: Transport Destination Calibrating Based on Multi-task Learning. In De Francisci Morales G, Bonchi F, Perlich C, Ruchansky N, Kourtellis N, Baralis E, editors, Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track - European Conference, ECML PKDD 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 276-292. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-43430-3_17