GLOSA for adaptive traffic lights: Methods and evaluation

Green Light Optimized Speed Advisory (GLOSA) systems have been shown to be able to reduce both CO₂ emissions and fuel consumption by giving drivers speed recommendations when approaching a traffic light. For the system to reach its maximum potential, is is necessary to properly predict all different types of traffic lights, that is, also adaptive traffic lights where signals may change with lead times as short as 1 s. In previous work we presented an approach to predict these adaptive traffic lights using graph transformation. In this paper we demonstrate how to adequately parametrize such a graph based prediction approach and evaluate the accuracy of the signal prognosis. In a first step, we find feasible values for the proper creation of the prediction graph. This graph is the basis for all predictions and therefore directly influences the quality of the prognosis. We then assess the forecast in terms of correctness and deviation to measure the accuracy of the predictions. We were able to show a prognosis system with an accuracy of 95% and a deviation of less than 2 s. Lastly, we discuss some criteria to compare different approaches of prognosis systems for adaptive traffic lights.