Reinforcement Learning of Driver Receiving Traffic Signal Information for Passing through Signalized Intersection at Arterial Road

摘要

Real-time traffic signal information has recently become available thanks to further developments in Intelligent Transportation System (ITS) technology. Drivers may change their driving behavior by receiving information on the status of upcoming traffic signals. The behavior of a driver who reacts to such information with the purpose of smoothly passing through an upcoming signalized intersection is described as reinforcement learning in this study. The influence of reactive drivers on traffic flows is analyzed by a multi-agent traffic flow simulation on an arterial road composed of four signalized intersections. Furthermore, the relationship between the ratio of reactive drivers and traffic flows is clarified, under the assumption that there is a mixture of reactive and nonreactive drivers on the road. The results of the multi-agent traffic flow simulation showed that the average stop time decreased as the ratio of reactive drivers increased. Moreover, the threshold of the ratio of reactive drivers to begin to influence largely vehicle stop situations was around 50%. The average travel time was almost constant regardless of the ratio of reactive drivers. It was also demonstrated that reacting to the traffic signal information and decelerating accordingly did not cause time delays. This suggests that the provision of the traffic signal information and appropriate reaction to the provided information may help reduce the amount of CO2emissions from a vehicle approaching a signalized intersection and probably contribute to alleviating progress of global warming.