Evaluating and Forecasting the Operational Performance of Road Intersections

Road intersections represent one of the most complex configurations encountered when traversing road networks. It is therefore of vital importance to improve their operational performance, as that can significantly contribute towards the efficiency of the whole transport network. Traditional approaches to improve the efficiency of intersections are based on analysis of static data or expert opinions. However, due to the advancements on Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication technologies, it is possible to enhance safety and improve road intersection efficiency by continuously monitoring traffic conditions and enabling situational awareness of vehicle drivers. Towards this end, we design, develop and evaluate a system for evaluating and forecasting the operational performance of road intersections by mining streams of V2I data. Our system makes use of graph mining and trajectory data mining methods to continuously evaluate a set of well-defined measures of effectiveness (MOEs) for traffic operations at different levels of road network abstraction. In addition, the system enables interactive analysis and exploration of the various MOEs. The system architecture and methods are general and can be used in various settings requiring continuous monitoring and/or forecasting of the road network state.