A comparison of algorithms used in traffic control systems
Abstract: A challenge in today's society is to handle a large amount of vehicles traversing an intersection. Traffic lights are often used to control the traffic flow in these intersections. However, there are inefficiencies since the algorithms used to control the traffic lights do not perfectly adapt to the traffic situation. The purpose of this paper is to compare three different types of algorithms used in traffic control systems to find out how to minimize vehicle waiting times. A pretimed, a deterministic and a reinforcement learning algorithm were compared with each other. Test were conducted on a four-way intersection with various traffic demands using the program Simulation of Urban MObility (SUMO). The results showed that the deterministic algorithm performed best for all demands tested. The reinforcement learning algorithm performed better than the pretimed for low demands, but worse for varied and higher demands. The reasons behind these results are the deterministic algorithm's knowledge about vehicular movement and the negative effects the curse of dimensionality has on the training of the reinforcement learning algorithm. However, more research must be conducted to ensure that the results obtained are trustworthy in similar and different traffic situations.
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