Anticipation and alert system of congestion and accidents in VANET using Big Data analysis for Intelligent Transportation Systems

Abstract

Vehicular Networks (VN) have a huge potential to increase roadway safety and traffic efficiency. Big Data analysis can be instrumental in realizing this potential and enhancing the Intelligent Transportation Systems (ITSs). We study the causes of road accidents using big real-time accidents data obtained from Florida Department of Transportation (FDOT) - District 4. The ultimate goal is to prevent or at least decrease traffic accidents and congestions. Our approach is based on dividing the roadway into segments, based on the infrastructure availability and the secondary accidents factors. We design a real-time Big Data system that receives online streamed data from vehicles on the road in addition to real-time average speed data from vehicles detectors on the road side to (1) Provide accurate Estimated Time of Arrival (ETA) using a Linear Regression (LR) model (2) Predict accidents and congestions before they happen using Naive Bayes (NB) and Distributed Random Forest (DRF) classifiers (3) Update ETA if an accident or a congestion takes place by predicting accurate clearance time. To make this system fast, accurate, and reliable we have implemented Lambda Architecture (LA) in our framework because of its speed, scalability, and fault tolerance. Furthermore, we have optimized the efficiency, the speed, and the accuracy of the designed model by securely selecting the most relevant and significant set of features required for the analysis.