Evaluating the Operational Performance of Road Intersections by Mining Trajectory Data Streams

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Traffic congestion describes a situation on transport networks that occurs when demand approaches the**capacity of a road (or of the intersections along the road). It is characterized by slower speeds, longer trip times,**and increased vehicle queues and is associated to significant social, economic and environmental costs. Road**intersections represent one of the most complex configurations encountered when traversing road networks and**a high percentage of accidents occur at these locations. 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, today's vehicles are no longer stand-alone transportation means. 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**information and enabling situational awareness of vehicle drivers.**In this project, we aim to leverage big data mining and machine learning techniques to continuously monitor**the operational performance of road intersections through mining real-time V2I data. A key to the success of**these methods is the quality and timeliness of the analysis provided. The anticipated outcome of the research is**twofold: (i) an increased safety and efficiency of road intersections, and (ii) a data-driven approach to evaluate**road intersection operational performance.**The proposed research collaboration aligns with Canada's Innovation Agenda. Conducting research in the**intersection of big data analytics and machine learning has the potential to attract the brightest students from**around the world, while keeping domestic talent here.