Related papers: Neighbor-embedded Graph Neural Network-based Crowd Delivery Traffic Management in Smart City

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

URL: http://arxiv.org/abs/2608.22555v1
Date: Sun, 23 Aug 2026 18:58:06 GMT
Title: Neighbor-embedded Graph Neural Network-based Crowd Delivery Traffic Management in Smart City
Abstract summary: This work introduces a novel textitNeighbor-Embedded Graph Neural Network-based Crowd Delivery Traffic Management (NeCDM) Model.<n>The proposed model achieves notable improvements in computational efficiency, including reductions of up to 4.03% in L1 loss.
Score: 5.687232573267495
License: http://creativecommons.org/licenses/by/4.0/
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.

Related papers

Wireless Traffic Prediction with Large Language Model [54.07581399989292]
TIDES is a novel framework that captures spatial-temporal correlations for wireless traffic prediction.<n> TIDES achieves efficient adaptation to domain-specific patterns without incurring excessive training overhead.<n>Our results indicate that integrating spatial awareness into LLM-based predictors is the key to unlocking scalable and intelligent network management in future 6G systems.
arXiv Detail & Related papers (2025-12-19T04:47:40Z)
Network-Constrained Policy Optimization for Adaptive Multi-agent Vehicle Routing [1.4273866043218153]
We address dynamic vehicle routing through a multi-agent reinforcement learning (MARL) framework for coordinated, network-aware fleet navigation.<n>We first propose Adaptive Navigation (AN), a decentralized MARL model where each intersection agent provides routing guidance based on local traffic and neighborhood state.<n>To improve scalability in large networks, we further propose Hierarchical Hub-based Adaptive Navigation (HHAN), an extension of AN that assigns agents only to key intersections (hubs)<n> Experiments on synthetic grids and real urban maps (Toronto, Manhattan) show that AN reduces average travel time versus SPF and learning baselines, maintaining 100% routing success.
arXiv Detail & Related papers (2025-10-30T02:49:46Z)
Spatio-Temporal Graph Neural Network for Urban Spaces: Interpolating Citywide Traffic Volume [4.188237759092441]
We introduce the Graph Neural Network for Urban Interpolation (GNNUI), a novel urban traffic volume estimation approach.<n>GNNUI employs a masking algorithm to learn, integrates node features to capture functional roles, and uses a loss function tailored to zero-inflated traffic distributions.<n>In addition to the model, we introduce two new open-scale urban traffic volume benchmarks, covering different transportation modes.
arXiv Detail & Related papers (2025-05-07T13:34:00Z)
AIoT-based smart traffic management system [0.0]
This paper presents a novel AI-based smart traffic management system de-signed to optimize traffic flow and reduce congestion in urban environments.<n>By analysing live footage from existing CCTV cameras, this approach eliminates the need for additional hardware.<n>The AI model processes live video feeds to accurately count vehicles and assess traffic density, allowing for adaptive signal control.
arXiv Detail & Related papers (2025-02-04T11:38:42Z)
Improving Traffic Flow Predictions with SGCN-LSTM: A Hybrid Model for Spatial and Temporal Dependencies [55.2480439325792]
This paper introduces the Signal-Enhanced Graph Convolutional Network Long Short Term Memory (SGCN-LSTM) model for predicting traffic speeds across road networks. Experiments on the PEMS-BAY road network traffic dataset demonstrate the SGCN-LSTM model's effectiveness.
arXiv Detail & Related papers (2024-11-01T00:37:00Z)
GARLIC: GPT-Augmented Reinforcement Learning with Intelligent Control for Vehicle Dispatching [81.82487256783674]
GARLIC: a framework of GPT-Augmented Reinforcement Learning with Intelligent Control for vehicle dispatching.<n>This paper introduces GARLIC: a framework of GPT-Augmented Reinforcement Learning with Intelligent Control for vehicle dispatching.
arXiv Detail & Related papers (2024-08-19T08:23:38Z)
An ASP Framework for Efficient Urban Traffic Optimization [0.0]
This paper presents a framework which allows to efficiently simulate and optimize traffic flow in a large roads' network with hundreds of vehicles. The framework leverages on an Answer Set Programming (ASP) encoding to formally describe the movements of vehicles inside a network. It is then possible to optimize the routes of vehicles inside the network to reduce a range of relevant metrics.
arXiv Detail & Related papers (2022-08-05T10:50:38Z)
Feudal Multi-Agent Reinforcement Learning with Adaptive Network Partition for Traffic Signal Control [44.09601435685123]
Multi-agent reinforcement learning (MARL) has been applied and shown great potential in traffic signal control. Previous work partitions the traffic network into several regions and learns policies for agents in a feudal structure. We propose a novel feudal MARL approach with adaptive network partition.
arXiv Detail & Related papers (2022-05-27T09:02:10Z)
On the Role of Multi-Objective Optimization to the Transit Network Design Problem [0.7734726150561088]
This work shows that single and multi objective stances can be synergistically combined to better answer the transit network design problem (TNDP) As a guiding case study, the solution is applied to the multimodal public transport network in the city of Lisbon, Portugal. The proposed TNDP optimization proved to improve results, with reductions in objective functions of up to 28.3%.
arXiv Detail & Related papers (2022-01-27T16:22:07Z)
Road Network Guided Fine-Grained Urban Traffic Flow Inference [108.64631590347352]
Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem. We propose a novel Road-Aware Traffic Flow Magnifier (RATFM) that exploits the prior knowledge of road networks. Our method can generate high-quality fine-grained traffic flow maps.
arXiv Detail & Related papers (2021-09-29T07:51:49Z)
Integrated Traffic Simulation-Prediction System using Neural Networks with Application to the Los Angeles International Airport Road Network [39.975268616636]
The proposed system includes an optimization-based OD matrix generation method, a Neural Network (NN) model trained to predict OD matrices via the pattern of traffic flow and a microscopic traffic simulator. We test the proposed system on the road network of the central terminal area (CTA) of the Los Angeles International Airport (LAX)
arXiv Detail & Related papers (2020-08-05T01:41:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.