Traffic Regulation Recognition using Crowd-Sensed GPS and Map Data: a Hybrid Approach

Abstract. This article presents a method for traffic control recognition at junctions (traffic lights, stop, priority and right of way rule) using crowd-sensed GPS data (vehicle trajectories), as well as features extracted from OpenStreetMap. Traffic regulators are not mapped in most maps, although the way they regulate traffic at intersections affects the traffic flow and therefore the vehicle idle time at intersections, the fuel consumption, the CO2 emissions, and the arrival time at a destination. Because of the controlled interaction that road users have with each other at intersections, driving safety or assistance applications can be enabled if intersection regulators are mapped. In order to verify the proposed method two sets of trajectories were used, one of which is an open dataset, from two different cities, Hannover and Chicago. Two classification methods were tested, random forest and gradient boosting, using exclusively either dynamic features (trajectories), or static (only data from OSM) or a combination of the dynamic and static features (hybrid model). The results show that the gradient boosting classification with hybrid features can predict traffic regulations with high accuracy (93% in Chicago and 94% in Hannover), outperforming the other detection models (static and dynamic). At the end directions for further research on this topic are proposed.