Big data vehicle density management in vehicular ad-hoc network

Abstract

Smart city project is today a domain of interest to community research which play well-known role in road traffic management. Data exchange became complicated in terms of capacity in the intelligent transport system (ITS), and without the raise of big data, the treatment is very difficult to manage. vehicular ad-hoc network (VANETs) faces many challenges mainly the voluminous data generated by different actors of VANET environment.
We propose a real time anomalies detection system in an instantaneous way with parallel data treatment. The system method intends to compute precisely vehicle density at each section on each road, which help to handle the traffic and forward to vehicles information about the road and the best safe path to reach their destination. Also, we build anomalies prediction system based on machine learning framework, it is a good solution for avoiding traffic congestion and limiting the risk of accidents. The simulation results demonstrate that the proposed system method reduces congestion greatly by taking into account the load balancing and therefore avoids saturation and reduces accidents. It should also be noted that the results obtained show that the system is characterized by low latency and high accuracy.