IoT infrastructure for the accident avoidance: an approach of smart transportation

The volcanic growth of the population directly influences road vehicles. The rapid growth of vehicles is the primary cause of traffic congestion, pollution, and life-loss through accidents. In this paper, we propose a cross-point collision avoidance (CCA) model for better predictability about neighbour vehicles and road-crossing points. It consists of two-phase approaches like vehicle to infrastructure and vehicle to vehicle communication model to avoid accidents or collisions during vehicle crossing to each other at turning points on an urban road. To execute this work, we have used sensors and beacons of both static and dynamic nature. For the testing of the applicability and feasibility of proposed algorithms, we have used the RMATLAB17 simulators. The simulation results have been validated against the government safety standards. The proposed CCA model achieves an average safety accuracy of 94.31% under different road shapes. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.