Road traffic prediction using Bayesian networks

Having prior road condition knowledge for planned or unplanned journeys will be beneficial in terms of not only time but potentially cost. Being able to obtain real-time information will further enhance these benefits. Current systems rely on huge infrastructure investments by governments to install cameras, road sensors and billboards to keep motorists informed. These efforts can only be, at best, available at pre-identified hotspots. Radio broadcast is an alternative, where they rely on reports by other motorists. However, such reports are often delayed and not tailored to individual motorist. Seeing the limitations of existing approaches to obtain real-time road conditions, this research work leverages on mobile devices that provide context sensitive information to propose a predictive analytics framework based on a Bayesian Network for road condition prediction. This paper aims to contribute to (i) defining a set of evidences (variables) that could potentially be utilized for road condition prediction and (ii) construction of a Bayesian Network model to predict road conditions. In conclusion, we presented a novel approach to provide potentially unlimited coverage of road traffic conditions with substantially reduced infrastructure investments.