Neighbor-embedded Graph Neural Network-based Crowd Delivery Traffic Management in Smart City
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)...
| Authors | Kishu Gupta, Deepika Saxena, Ashutosh Kumar Singh et al. |
|---|---|
| Primary category | cs.CR |
| Published | 23 August 2026 |
| Last updated at source | 23 August 2026 |
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