Nonlinear Theory and Its Applications, IEICE
Online ISSN : 2185-4106
Special Section on Recent Progress in Nonlinear Theory and Its Applications
Speeding up of the traffic congestion mitigation by stochastic optimization in deep learning
Shinnnosuke NakamuraTakumi UemuraGou KoutakiKeiichi Uchimura
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2018 Volume 9 Issue 1 Pages 49-59

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Abstract

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.

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© 2018 The Institute of Electronics, Information and Communication Engineers
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