An Express Management System With Graph Recurrent Neural Network for Estimated Time of Arrival

Su, X; Alatas, B; Sohaib, O

An Express Management System With Graph Recurrent Neural Network for Estimated Time of Arrival

Su, X Alatas, B Sohaib, O

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Publisher:: IGI GLOBAL
Publication Type:: Journal Article
Citation:: Journal of Organizational and End User Computing, 2025, 37, (1)
Issue Date:: 2025-01-01

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Field	Value	Language
dc.contributor.author	Su, X
dc.contributor.author	Alatas, B
dc.contributor.author	Sohaib, O https://orcid.org/0000-0001-9287-5995
dc.date.accessioned	2025-05-07T06:33:30Z
dc.date.available	2025-05-07T06:33:30Z
dc.date.issued	2025-01-01
dc.identifier.citation	Journal of Organizational and End User Computing, 2025, 37, (1)
dc.identifier.issn	1546-2234
dc.identifier.issn	1546-5012
dc.identifier.uri	http://hdl.handle.net/10453/187230
dc.description.abstract	Estimated Time of Arrival (ETA) is a crucial task in the logistics and transportation industry, aiding businesses and individuals in optimizing time management and improving operational efficiency. This study proposes a novel Graph Recurrent Neural Network (GRNN) model that integrates external factor data. The model first employs a Multilayer Perceptron (MLP)-based external factor data embedding layer to categorize and combine influencing factors into a vector representation. A Graph Recurrent Neural Network, combining Long Short-Term Memory (LSTM) and GNN models, is then used to predict ETA based on historical data. The model undergoes both offline and online evaluation experiments. Specifically, the offline experiments demonstrate a 5.3% reduction in RMSE on the BikeNYC dataset and a 6.1% reduction on the DidiShenzhen dataset, compared to baseline models. Online evaluation using Baidu Maps data further validates the model’s effectiveness in real-time scenarios. These results underscore the model’s potential in improving ETA predictions for urban traffic systems.
dc.language	English
dc.publisher	IGI GLOBAL
dc.relation.ispartof	Journal of Organizational and End User Computing
dc.relation.isbasedon	10.4018/JOEUC.370912
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0806 Information Systems, 1503 Business and Management, 1702 Cognitive Sciences
dc.subject.classification	Information Systems
dc.subject.classification	35 Commerce, management, tourism and services
dc.subject.classification	46 Information and computing sciences
dc.title	An Express Management System With Graph Recurrent Neural Network for Estimated Time of Arrival
dc.type	Journal Article
utslib.citation.volume	37
utslib.for	0806 Information Systems
utslib.for	1503 Business and Management
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-05-07T06:33:28Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	37
utslib.citation.issue	1

Abstract:

Estimated Time of Arrival (ETA) is a crucial task in the logistics and transportation industry, aiding businesses and individuals in optimizing time management and improving operational efficiency. This study proposes a novel Graph Recurrent Neural Network (GRNN) model that integrates external factor data. The model first employs a Multilayer Perceptron (MLP)-based external factor data embedding layer to categorize and combine influencing factors into a vector representation. A Graph Recurrent Neural Network, combining Long Short-Term Memory (LSTM) and GNN models, is then used to predict ETA based on historical data. The model undergoes both offline and online evaluation experiments. Specifically, the offline experiments demonstrate a 5.3% reduction in RMSE on the BikeNYC dataset and a 6.1% reduction on the DidiShenzhen dataset, compared to baseline models. Online evaluation using Baidu Maps data further validates the model’s effectiveness in real-time scenarios. These results underscore the model’s potential in improving ETA predictions for urban traffic systems.

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http://hdl.handle.net/10453/187230