Analyzing vehicle traces to find and exploit correlated traffic lights for efficient driving

Traffic lights strongly impact vehicle movement and fuel consumption in cities. If drivers were aware of the situation at arrival time, they could adapt their velocity and thus reduce the number of unnecessary stops and fuel consumption. To predict the influence of the traffic light ahead on the velocity of an approaching vehicle, our vision is that drivers share their vehicle traces in a digital cloud, and in return benefit from algorithms evaluating the collected data. With Traffic Light Coordination Analysis (TLCorA), we present one such algorithm analyzing vehicle traces. When a vehicle is approaching a traffic light, TLCorA finds traces of vehicles similar to that of the vehicle at the previous traffic light, and calculates from their approach to the upcoming traffic light whether there is a representative approaching trace. For this purpose, TLCorA classifies the approaching traces with help of a clustering algorithm based on dynamic time warping. We implement TLCorA in simulations of different traffic light signalization algorithms, and study the calculated approach probabilities depending on the respective traffic light correlation level in the scenarios.