Machine Learning Approach for Intelligent Transport System in IOV-Based Vehicular Network Traffic for Smart Cities - Kokane, Chandrakant D.; Mohadikar, Gopal; Khapekar, Sonu; Jadhao, Bharti; Waykole, Tushar; Deotare, Vilas V. (International Journal of Intelligent Systems and Applications in Engineering, 2023)
The transportation industry will face many significant issues, including traffic congestion, pollution, and ineffective traffic management, as a result of the increasing urbanisation and demographic growth. It appears that one potential solution to these issues may be provided by intelligent transport systems (ITS) that harness the power of vehicular networks on the Internet of Vehicles (IoV). By integrating communication between vehicles and infrastructure (V2V) and vehicles and the cloud (V2C), the proposed ITS architecture seeks to create a comfortable and effective transportation ecosystem. The V2V connection helps with the transmission of data on route conditions, collision avoidance, and speed, position, and other factors. Vehicle-to-infrastructure (V2I) communication enables automobiles to connect with infrastructure components like traffic lights, road signs, and parking systems in order to optimise traffic signal timings and provide drivers with real-time information. Innovative applications like customised recommendations, dynamic navigation, and predictive maintenance are made possible via V2C communication, which makes it possible for cars to connect to the cloud. The recommended method makes use of tree-based machine learning models including Decision Tree (DT), XGBoost (XGB), and Random Forest (RF) to increase traffic detection accuracy and computational efficiency.