  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.
