Data-Driven Approaches to Detecting Misdeliveries in Truck Logistics using GPS Data

Ayumu Hidaka; Ryota Shin; Atsushi Tsuchiya; Norihiko Nakabayashi; Yukihiko Okada; Yohei Shida

doi:10.1109/BigData59044.2023.10386214

Data-Driven Approaches to Detecting Misdeliveries in Truck Logistics using GPS Data

Ayumu Hidaka
, Ryota Shin
, Atsushi Tsuchiya
, Norihiko Nakabayashi
, Yukihiko Okada
, Yohei Shida

Department of Management

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

Abstract

Misdelivery in logistic services leads to increased costs and degradation of packages. For deliveries using trucks, erroneous deliveries are prevented by checking identification numbers on packages as the packages pass through delivery points. In recent years, it has become possible to monitor real-time location by attaching GPS receivers to packages, but few concrete efforts have been made to detect anomalies using truck transport data. This study introduces a basic framework for the detection of misdeliveries and a summary of the problems in actual truck misdelivery using special medical supply delivery data provided by a major Japanese logistics company. It is shown that the system can detect erroneous deliveries with high accuracy, even for actual delivery data with coarse resolution owing to the cost of installing the equipment. This study has the potential not only to improve logistics and reduce costs, but also to solve various social problems such as driver shortages.

Original language	English
Title of host publication	Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023
Editors	Jingrui He, Themis Palpanas, Xiaohua Hu, Alfredo Cuzzocrea, Dejing Dou, Dominik Slezak, Wei Wang, Aleksandra Gruca, Jerry Chun-Wei Lin, Rakesh Agrawal
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1922-1927
Number of pages	6
ISBN (Electronic)	9798350324457
DOIs	https://doi.org/10.1109/BigData59044.2023.10386214
Publication status	Published - 2023
Event	2023 IEEE International Conference on Big Data, BigData 2023 - Sorrento, Italy Duration: 15 Dec 2023 → 18 Dec 2023

Publication series

Name	Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023

Conference

Conference	2023 IEEE International Conference on Big Data, BigData 2023
Country/Territory	Italy
City	Sorrento
Period	15/12/23 → 18/12/23

Keywords

GPS
anomaly detection
misdelivery
trajectory method
transport data
truck logistics

Access to Document

10.1109/BigData59044.2023.10386214

Cite this

Hidaka, A., Shin, R., Tsuchiya, A., Nakabayashi, N., Okada, Y., & Shida, Y. (2023). Data-Driven Approaches to Detecting Misdeliveries in Truck Logistics using GPS Data. In J. He, T. Palpanas, X. Hu, A. Cuzzocrea, D. Dou, D. Slezak, W. Wang, A. Gruca, J. C.-W. Lin, & R. Agrawal (Eds.), Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023 (pp. 1922-1927). (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData59044.2023.10386214

Hidaka, Ayumu ; Shin, Ryota ; Tsuchiya, Atsushi et al. / Data-Driven Approaches to Detecting Misdeliveries in Truck Logistics using GPS Data. Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. editor / Jingrui He ; Themis Palpanas ; Xiaohua Hu ; Alfredo Cuzzocrea ; Dejing Dou ; Dominik Slezak ; Wei Wang ; Aleksandra Gruca ; Jerry Chun-Wei Lin ; Rakesh Agrawal. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 1922-1927 (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023).

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title = "Data-Driven Approaches to Detecting Misdeliveries in Truck Logistics using GPS Data",

abstract = "Misdelivery in logistic services leads to increased costs and degradation of packages. For deliveries using trucks, erroneous deliveries are prevented by checking identification numbers on packages as the packages pass through delivery points. In recent years, it has become possible to monitor real-time location by attaching GPS receivers to packages, but few concrete efforts have been made to detect anomalies using truck transport data. This study introduces a basic framework for the detection of misdeliveries and a summary of the problems in actual truck misdelivery using special medical supply delivery data provided by a major Japanese logistics company. It is shown that the system can detect erroneous deliveries with high accuracy, even for actual delivery data with coarse resolution owing to the cost of installing the equipment. This study has the potential not only to improve logistics and reduce costs, but also to solve various social problems such as driver shortages.",

keywords = "GPS, anomaly detection, misdelivery, trajectory method, transport data, truck logistics",

author = "Ayumu Hidaka and Ryota Shin and Atsushi Tsuchiya and Norihiko Nakabayashi and Yukihiko Okada and Yohei Shida",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Big Data, BigData 2023 ; Conference date: 15-12-2023 Through 18-12-2023",

year = "2023",

doi = "10.1109/BigData59044.2023.10386214",

language = "English",

series = "Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1922--1927",

editor = "Jingrui He and Themis Palpanas and Xiaohua Hu and Alfredo Cuzzocrea and Dejing Dou and Dominik Slezak and Wei Wang and Aleksandra Gruca and Lin, \{Jerry Chun-Wei\} and Rakesh Agrawal",

booktitle = "Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023",

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Hidaka, A, Shin, R, Tsuchiya, A, Nakabayashi, N, Okada, Y & Shida, Y 2023, Data-Driven Approaches to Detecting Misdeliveries in Truck Logistics using GPS Data. in J He, T Palpanas, X Hu, A Cuzzocrea, D Dou, D Slezak, W Wang, A Gruca, JC-W Lin & R Agrawal (eds), Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023, Institute of Electrical and Electronics Engineers Inc., pp. 1922-1927, 2023 IEEE International Conference on Big Data, BigData 2023, Sorrento, Italy, 15/12/23. https://doi.org/10.1109/BigData59044.2023.10386214

Data-Driven Approaches to Detecting Misdeliveries in Truck Logistics using GPS Data. / Hidaka, Ayumu; Shin, Ryota; Tsuchiya, Atsushi et al.
Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. ed. / Jingrui He; Themis Palpanas; Xiaohua Hu; Alfredo Cuzzocrea; Dejing Dou; Dominik Slezak; Wei Wang; Aleksandra Gruca; Jerry Chun-Wei Lin; Rakesh Agrawal. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1922-1927 (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023).

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

TY - GEN

T1 - Data-Driven Approaches to Detecting Misdeliveries in Truck Logistics using GPS Data

AU - Hidaka, Ayumu

AU - Shin, Ryota

AU - Tsuchiya, Atsushi

AU - Nakabayashi, Norihiko

AU - Okada, Yukihiko

AU - Shida, Yohei

PY - 2023

Y1 - 2023

N2 - Misdelivery in logistic services leads to increased costs and degradation of packages. For deliveries using trucks, erroneous deliveries are prevented by checking identification numbers on packages as the packages pass through delivery points. In recent years, it has become possible to monitor real-time location by attaching GPS receivers to packages, but few concrete efforts have been made to detect anomalies using truck transport data. This study introduces a basic framework for the detection of misdeliveries and a summary of the problems in actual truck misdelivery using special medical supply delivery data provided by a major Japanese logistics company. It is shown that the system can detect erroneous deliveries with high accuracy, even for actual delivery data with coarse resolution owing to the cost of installing the equipment. This study has the potential not only to improve logistics and reduce costs, but also to solve various social problems such as driver shortages.

AB - Misdelivery in logistic services leads to increased costs and degradation of packages. For deliveries using trucks, erroneous deliveries are prevented by checking identification numbers on packages as the packages pass through delivery points. In recent years, it has become possible to monitor real-time location by attaching GPS receivers to packages, but few concrete efforts have been made to detect anomalies using truck transport data. This study introduces a basic framework for the detection of misdeliveries and a summary of the problems in actual truck misdelivery using special medical supply delivery data provided by a major Japanese logistics company. It is shown that the system can detect erroneous deliveries with high accuracy, even for actual delivery data with coarse resolution owing to the cost of installing the equipment. This study has the potential not only to improve logistics and reduce costs, but also to solve various social problems such as driver shortages.

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KW - anomaly detection

KW - misdelivery

KW - trajectory method

KW - transport data

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DO - 10.1109/BigData59044.2023.10386214

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AN - SCOPUS:85184983159

T3 - Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023

SP - 1922

EP - 1927

BT - Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023

A2 - He, Jingrui

A2 - Palpanas, Themis

A2 - Hu, Xiaohua

A2 - Cuzzocrea, Alfredo

A2 - Dou, Dejing

A2 - Slezak, Dominik

A2 - Wang, Wei

A2 - Gruca, Aleksandra

A2 - Lin, Jerry Chun-Wei

A2 - Agrawal, Rakesh

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE International Conference on Big Data, BigData 2023

Y2 - 15 December 2023 through 18 December 2023

ER -

Hidaka A, Shin R, Tsuchiya A, Nakabayashi N, Okada Y, Shida Y. Data-Driven Approaches to Detecting Misdeliveries in Truck Logistics using GPS Data. In He J, Palpanas T, Hu X, Cuzzocrea A, Dou D, Slezak D, Wang W, Gruca A, Lin JCW, Agrawal R, editors, Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 1922-1927. (Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023). doi: 10.1109/BigData59044.2023.10386214

Data-Driven Approaches to Detecting Misdeliveries in Truck Logistics using GPS Data

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Keywords

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