Speeding up of the traffic congestion mitigation by stochastic optimization in deep learning

抄録

In recent years, many researchers have become interested in methods for mitigating traffic congestion by optimizing traffic signal parameters. To mitigate traffic congestion over a widespread area, a method using an advanced genetic algorithm and a traffic simulator has been proposed (Nishihara, T., et al., “The Verification with Real-World Road Network on Optimization of Traffic Signal Parameters using Multi-Element Genetic Algorithms”, ITS World Congress, 2012). However, this method consumes considerable time when simulating traffic flow. This paper proposes a method that reduces the processing time of the simulator by using a neural network.