Driver anomaly detection in cargo terminal
The Iranian road transportation sector, comprising about 500,000 owner-operator drivers, faces rising syndication challenges, leading to disruptions and driver refusals in some provinces. Drivers highlight the urgent need for load distribution improvements within terminals. This study investigates a...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024175986 |
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| _version_ | 1832573142893068288 |
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| author | Shahab Emaani Abbas Saghaei |
| author_facet | Shahab Emaani Abbas Saghaei |
| author_sort | Shahab Emaani |
| collection | DOAJ |
| description | The Iranian road transportation sector, comprising about 500,000 owner-operator drivers, faces rising syndication challenges, leading to disruptions and driver refusals in some provinces. Drivers highlight the urgent need for load distribution improvements within terminals. This study investigates anomaly detection by drivers in cargo terminals, starting with the evaluation of driver assumptions through K-means clustering. The study confirms drivers' assertions regarding those who handle more cargo in less waiting time. Subsequently, Isolation Forest, KNN, and HBOS algorithms are applied to detect abnormal behavior using data mining techniques. Results reveal three distinct driver groups, with a notable proportion (98 %) of anomalies concentrated in one group. This study sheds light on the critical syndication issue of anomaly detection in cargo terminals by drivers, offering valuable insights for researchers and shipping practitioners. Moreover, limited research on theft prevention renders conventional methods ineffective, highlighting the overlooked use of clustering in prior literature reviews focused on case study analysis. |
| format | Article |
| id | doaj-art-500bf40f72064e4fab8f27ea6895309e |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-500bf40f72064e4fab8f27ea6895309e2025-02-02T05:27:50ZengElsevierHeliyon2405-84402025-01-01112e41567Driver anomaly detection in cargo terminalShahab Emaani0Abbas Saghaei1Master's degree, Department of Industrial Engineering, Technical and Engineering Faculty, Research Science Unit, Islamic Azad University, Tehran, Iran; Corresponding author.Professor, Department of Industrial Engineering, Technical and Engineering Faculty, Research Science Unit, Islamic Azad University, Tehran, IranThe Iranian road transportation sector, comprising about 500,000 owner-operator drivers, faces rising syndication challenges, leading to disruptions and driver refusals in some provinces. Drivers highlight the urgent need for load distribution improvements within terminals. This study investigates anomaly detection by drivers in cargo terminals, starting with the evaluation of driver assumptions through K-means clustering. The study confirms drivers' assertions regarding those who handle more cargo in less waiting time. Subsequently, Isolation Forest, KNN, and HBOS algorithms are applied to detect abnormal behavior using data mining techniques. Results reveal three distinct driver groups, with a notable proportion (98 %) of anomalies concentrated in one group. This study sheds light on the critical syndication issue of anomaly detection in cargo terminals by drivers, offering valuable insights for researchers and shipping practitioners. Moreover, limited research on theft prevention renders conventional methods ineffective, highlighting the overlooked use of clustering in prior literature reviews focused on case study analysis.http://www.sciencedirect.com/science/article/pii/S2405844024175986Anomaly detectionFraud detectionCargoTerminalMachine learning |
| spellingShingle | Shahab Emaani Abbas Saghaei Driver anomaly detection in cargo terminal Heliyon Anomaly detection Fraud detection Cargo Terminal Machine learning |
| title | Driver anomaly detection in cargo terminal |
| title_full | Driver anomaly detection in cargo terminal |
| title_fullStr | Driver anomaly detection in cargo terminal |
| title_full_unstemmed | Driver anomaly detection in cargo terminal |
| title_short | Driver anomaly detection in cargo terminal |
| title_sort | driver anomaly detection in cargo terminal |
| topic | Anomaly detection Fraud detection Cargo Terminal Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024175986 |
| work_keys_str_mv | AT shahabemaani driveranomalydetectionincargoterminal AT abbassaghaei driveranomalydetectionincargoterminal |