Enhancing Road Safety and Efficiency in Vehicular Ad-Hoc Networks Through Anomaly Detection and Traffic Prediction Using Big Data Analytics

Abstract

Nowadays, the processing of big data has become essential to extract valuable information from vast amounts of data generated by various systems. Traditional approaches to database management and data system supervision are inadequate in efficiently handling large datasets, and they often become outdated. Managing the substantial data generated by Vehicular Ad-Hoc Networks (VANETs) poses significant challenges. In this article, we present a two-step methodology that addresses these challenges by detecting anomalies and accidents, as well as predicting anomalies within road segments. This enables real-time calculation of the total time spent on road segments. Our methodology incorporates a database containing estimated real-time travel times within the network, facilitating optimal route selection for vehicles to minimize travel time and avoid or minimize traffic congestion and accidents along the way. The maintained database serves as input to machine learning algorithms that forecast the time plus location somewhere the likelihood of the accidents or higher traffic jams. Our simulation consequences demonstrate that the proposed methodology achieves improved road safety and effectively mitigates congestion by efficiently distributing traffic load across different roads.