Title: Efficient traffic management in the internet of vehicles through enhanced routing and deep learning

Authors: Arundhati Sahoo; Asis Kumar Tripathy

Addresses: School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology, Vellore, India ' School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology, Vellore, India

Abstract: In the internet of vehicles (IoV), vehicles are treated as sophisticated smart devices with robust communication systems. IoV utilises cellular technology and internet access for vehicle-to-vehicle communication. However, traditional routing algorithms struggle with rapid vehicle movements and varying road conditions, leading to instability and inefficiency, especially in congested traffic. This study proposes a unique approach called the improved greedy-bi-directional long-short-term memory (I-GBiLSTM) predictor, which integrates an improved greedy perimeter stateless routing algorithm to enhance link stability within 5G networks by incorporating real-time data on vehicle movements and road conditions and traffic patterns. Additionally, a BiLSTM neural network has been enhanced by incorporating a 1-dimensional convolutional autoencoder (1D-CNNAE) and a temporal transformer encoder (TTE) for monitoring and predicting traffic data, enabling unique feature extraction. Experimental results demonstrate that I-GBiLSTM is superior to the other existing protocols, achieving a 99% delivery ratio, a routing overhead value of 100, 180 ms end-to-end delay, and 98.2% prediction accuracy. This research enhances IoV communication efficiency and reliability, offering the potential for optimising traffic flow, reducing congestion, and improving overall safety and user experience in IoV environments.

Keywords: traffic management; internet of vehicles; IoV; routing; deep learning; network traffic prediction.

DOI: 10.1504/IJCSE.2025.148740

International Journal of Computational Science and Engineering, 2025 Vol.28 No.5, pp.543 - 553

Received: 15 Dec 2023
Accepted: 03 Aug 2024
Published online: 22 Sep 2025 *

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