An In-Vehicle Behaviour-Based Response Model for Traffic Monitoring and Driving Assistance in the Context of Smart Cities

Abstract

Intelligent transportation systems (ITS) are pivotal to the development of smart cities, as they aim to enhance traffic flow, reduce traffic congestion, improve road safety, and increase social inclusion. Intelligent vehicles can sense, actuate, and process information that has been gathered from the environment to provide reliable services. During communication, congestion is a major issue that affects driving behaviour. This paper proposes a behaviour-based response model for analysing the roadside traffic in a smart city environment. In this model, the vehicles leverage the benefits of connected cloud technology and smart computational capabilities to analyse traffic conditions and provide assisted driving to users. The proposed model employs a regression model for computing and analysing the information that is gathered from the environment. It also generates recommendations for its users and provides traffic congestion-free driving assistance, with a reduced reaction time and improved driving efficiency. Lastly, the model also intends to provide real-time information and actionable insights for drivers so that they can make informed decisions and improve the road safety in smart environments. The performance of the proposed model is validated by using the appropriate experiments, and the results are validated for the varying set of inputs and intervals for the metrics response delay, processing time, and precision errors.