SafeRNet: Safe Transportation Routing in the era of Internet of Vehicles and Mobile Crowd Sensing

TL;DR

SafeRNet is a framework that leverages real-time traffic data, historical information, and Bayesian networks to compute and deliver safe transportation routes, aiming to reduce accidents and improve driver safety in modern IoV environments.

Contribution

It introduces a novel safe route computation framework utilizing Bayesian networks and real-time data analysis in the context of Internet of Vehicles and Mobile Crowd Sensing.

Findings

Effective safe route inference demonstrated with real traffic data

Real-time route delivery enhances driver safety

Bayesian network model accurately predicts safe routes

Abstract

World wide road traffic fatality and accident rates are high, and this is true even in technologically advanced countries like the USA. Despite the advances in Intelligent Transportation Systems, safe transportation routing i.e., finding safest routes is largely an overlooked paradigm. In recent years, large amount of traffic data has been produced by people, Internet of Vehicles and Internet of Things (IoT). Also, thanks to advances in cloud computing and proliferation of mobile communication technologies, it is now possible to perform analysis on vast amount of generated data (crowd sourced) and deliver the result back to users in real time. This paper proposes SafeRNet, a safe route computation framework which takes advantage of these technologies to analyze streaming traffic data and historical data to effectively infer safe routes and deliver them back to users in real time. SafeRNet utilizes Bayesian network to formulate safe route model. Furthermore, a case study is presented to demonstrate the effectiveness of our approach using real traffic data. SafeRNet intends to improve drivers safety in a modern technology rich transportation system.