Automatic Detection of Mobile Interactions in a Signalized Intersection1

This work deals with the consequences of traffic light control strategies in signalized intersections on the behavior of users, particularly in terms of risk. Traffic light control consists in adapting traffic lights to the variable demand of traffic in order to improve the fluidity and the safety. An adaptive real-time control strategy developed at INRETS has been tested and gave good results according to fluidity criteria. Traffic in a complex real intersection was recorded over a period of eight months, during which time four different control strategies were applied. The resulting database allows us to make a comparative analysis of the effects of the different strategies. It is based on an experimental automatic observation device supplied by video sensors, which gives us dynamic measurements of the spatial occupation of the intersection. We examined traffic events relevant to safety. As no accident occurred during the experiment, our interest turned to traffic conflicts and their observation methods. Following the Swedish traffic conflict technique and Åse Svensson's work (Å. Svensson, 1998), we study traffic events relevant to safety. We apply her representation of the number of events for each degree of severity, which allows more complete and subtle diagnosis. Within this framework, we propose a categorization of traffic events in a signalized intersection, with respect to the features of the occupation measurements. By using pattern recognition methods to deal with these measurements, this approach treats very large databases in a systematic way. We define three categories of interactions, according to various speeds and positions of conflict protagonists. We explain the detection methods, and how we plan to evaluate the severity. 1 The research reported in this paper is part of a PhD thesis, supported by a region Ile de France grant.