Development of internet of vehicles and recurrent neural network enabled intelligent transportation system for smart cities - Surve, Jyoti; Bangare, Manoj L.; Bangare, Sunil L.; Pol, Urmila R.; Mali, Manisha; Meenakshi, Meenakshi; Alsalmani, Abdullah; Morsi, Sami A. (International Journal of Reconfigurable and Embedded Systems (IJRES), 2025)
The number of deaths has increased as a direct result of the increased frequency of traffic accidents, congestion, and other risk factors. Developing countries have prioritised the development of intelligent transport systems in order to reduce pollution, traffic congestion, and wasted time. This article describes an intelligent transport system that leverages the internet of vehicles (IoV) and deep learning to forecast traffic congestion. Data is acquired using a car’s global positioning system (GPS), road and vehicle sensors, traffic cameras, and traffic speed, density, and flow. All acquired data is stored in one location on a cloud server. The cloud server also stores historical traffic, road, and vehicle data. Using particle swarm optimisation, features are improved. The optimised dataset is used to train and test recurrent neural networks (RNNs), support vector machines (SVMs), and multi layer perceptrons (MLPs). A deep learning algorithm can predict traffic congestion and make recommendations to drivers on how fast to travel and which route to take. The experimental effort employs the performance measurement system (PeMS) traffic dataset. RNN has achieved accuracy of 95.1%.