aiqbench analysis
This research proposes a Graph Neural Network (GNN) architecture specifically designed to optimize crowd delivery logistics by predicting traffic congestion and selecting efficient vehicles. By integrating neighbor-embedded features, the model aims to reduce computational costs and carbon emissions within smart city ecosystems. The work represents a practical application of machine learning to solve complex urban mobility challenges rather than advancing core AI theory or quantum capabilities.
Neighbor-embedded Graph Neural Network-based Crowd Delivery Traffic Management in Smart City
arXiv:2608.22555v1 Announce Type: cross Abstract: The significant upsurge in vehicle traffic presents a considerable challenge in the pursuit of smart mobilization and transportation (SMT) worldwide. Current approaches primarily focus on vehicular traffic management through congestion prediction but fall short in addressing essential objectives such as traffic reduction and appropriate vehicle selection to alleviate congestion in smart cities ($SmCt$). To address these concerns, this work introduces a novel \textit{Neighbor-Embedded Graph Neural Network-based Crowd Delivery Traffic Management} (NeCDM) Model, comprising two key components: the Traffic Congestion Prediction Unit (TCPu) and the Traffic Observation and Management Unit (TOMu). The TCPu utilizes Graph Neural Network (GNN) optimization to accurately predict traffic flow levels at various delivery stations within $SmCt$ ecosystems. Additionally, the TOMu facilitates the intelligent selection of the most suitable delivery vehicles for fulfilling crowd delivery requests ($CDR$). This work emphasizes the potential of crowd delivery as a feasible solution for achieving SMT goals while adhering to smart city parameters ($\mathcal{SCP}$s), such as reduced carbon emissions, shorter travel times, and minimized travel distances. The proposed model achieves notable improvements in computational efficiency, including reductions of up to 4.03\% in L1 loss ($\pounds$), 16.66\% in L2 loss ($\pounds_{rmse}$), and 7.64\% in computation time.
Abstract:The significant upsurge in vehicle traffic presents a considerable challenge in the pursuit of smart mobilization and transportation (SMT) worldwide. Current approaches primarily focus on vehicular traffic management through congestion prediction but fall short in addressing essential objectives such as traffic reduction and appropriate vehicle selection to alleviate congestion in smart cities ($SmCt$). To address these concerns, this work introduces a novel \textit{Neighbor-Embedded Graph Neural Network-based Crowd Delivery Traffic Management} (NeCDM) Model, comprising two key components: the Traffic Congestion Prediction Unit (TCPu) and the Traffic Observation and Management Unit (TOMu). The TCPu utilizes Graph Neural Network (GNN) optimization to accurately predict traffic flow levels at various delivery stations within $SmCt$ ecosystems. Additionally, the TOMu facilitates the intelligent selection of the most suitable delivery vehicles for fulfilling crowd delivery requests ($CDR$). This work emphasizes the potential of crowd delivery as a feasible solution for achieving SMT goals while adhering to smart city parameters ($\mathcal{SCP}$s), such as reduced carbon emissions, shorter travel times, and minimized travel distances. The proposed model achieves notable improvements in computational efficiency, including reductions of up to 4.03\% in L1 loss ($£$), 16.66\% in L2 loss ($£_{rmse}$), and 7.64\% in computation time.
Cryptography and Security (cs.CR); Machine Learning (cs.LG)
arXiv:2608.22555 [cs.CR]
(or arXiv:2608.22555v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2608.22555 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 10, no. 3, pp. 2391-2403, June 2026
https://doi.org/10.1109/TETCI.2026.3670691 Focus to learn more DOI(s) linking to related resources
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