Smart Traffic Management System for Anticipating Unexpected Road Incidents in Intelligent Transportation Systems

Abstract

This article describes how anticipating unforeseen road events reveal a serious problem in intelligent transportation systems. Due to the diversity of causes, road incidents do not require regular traffic conditions and accurate prediction of these incidents in real-time becomes a complicated task not defined so far. In this article, a smart traffic management system based cloud-assisted service is proposed to preserve the traffic safety by controlling the road segments and predicts the probability of incoming incidents. The proposed cloud-assisted service includes a predictive model based on logistic regression to predict the occurrence of unforeseen incidents. The sudden slowdown of vehicles speeds is the practical case of the article. The classification task of the predictive model incorporates four explained variables, including vehicle speed, the travel time and estimated delay time. The prediction accuracy is proved by checking the model relevance according to the quality of fit and the statistical significance of each explained variable.