RoadCrowd: An approach to road traffic forecasting at junctions using crowd-sourcing and Bayesian model

Real time road traffic update is highly desirable for city dwellers, commuters, tourists and travelers. In this paper, we propose a novel methodology for estimating traffic conditions at road intersections. Our methodology involves crowd sourcing approach for traffic estimation as well as utilization of conditional probability distribution of traffic states at adjacent junctions. The later approach is inspired by Bayesian inference. Bayesian inference is used to estimate traffic condition at one junction based on the traffic condition at an adjacent junction. We have formulated two algorithms which are used to update traffic conditions and to retrieve traffic conditions respectively based on the crowd sourced data and the probabilistic methodology. A web based prototype has been developed which incorporates the described methodology. The initial evaluation shows the feasibility of our proposed methodology.