Traffic Signal Control: a Double Q-learning Approach

Abstract

Currently, the use of information and communication technologies for solving economic, social, transportation, and other problems in the urban environment is usually considered within the "smart city" concept. Optimal traffic management and, in particular, traffic signal control is one of the key components of smart cities. In this paper, we investigate the reinforcement learning approach, namely, the double Q-learning approach, to solve the traffic signal control problem. Both the initial data on the connected vehicles distribution and the aggregated characteristics of traffic flows are used to describe the state of the reinforcement learning agent. Experimental studies of the proposed model were carried out on synthetic and real data using the CityFlow microscopic traffic simulator.