# Neighbor-embedded Graph Neural Network-based Crowd Delivery Traffic Management in Smart City

arXiv: https://arxiv.org/abs/2608.22555 · HTML: https://mlsanity.com/paper/2608.22555/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 29 references that are in the map.
- First posted on arXiv 23 Aug 2026.

## Builds on (most cited first, 3 of 3)

- [PCNN: Deep Convolutional Networks for Short-term Traffic Congestion Prediction](https://mlsanity.com/paper/2003.07033/) (2020)
- [An Intelligent Quantum Cyber-Security Framework for Healthcare Data Management](https://mlsanity.com/paper/2410.03217/) (2024)
- [Scaling and Placing Distributed Services on Vehicle Clusters in Urban Environments](https://mlsanity.com/paper/2110.09471/) (2021)

## Abstract (arXiv)

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 textitNeighbor-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 (SCPs), 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 (poundsrmse), and 7.64% in computation time.