An innovative approach, IRTPML, aims to enhance both routing efficiency and road safety in vehicular networks. The proposed model integrates geometric particle swarm optimization for routing and convolutional neural networks (CNNs) for traffic accident prediction, leveraging vehicle-to-vehicle communication. By dynamically adapting routing paths based on network conditions and issuing early warnings for potential traffic accidents, the IRTPML model demonstrates significant improvements in energy efficiency, energy consumption, average delay, network throughput, and data delivery ratio compared to existing protocols. Extensive simulations and comparative performance analysis validate the efficacy of the proposed model, suggesting its promising potential for optimizing VANETs and enhancing transportation safety.
Improved Routing Model with Traffic Accident Prediction Using Machine Learning in Vehicular Communication
2025-03-27
817272 byte
Conference paper
Electronic Resource
English
Traffic Accident Risk Prediction Using Machine Learning
IEEE | 2022
|Traffic prediction with deep learning for vehicular communication network
British Library Conference Proceedings | 2022
|Traffic accident analysis using machine learning paradigms
Tema Archive | 2005
|Traffic Accident Detection Using Machine Learning Algorithms
Springer Verlag | 2022
|