Nested hybrid evolutionary model for traffic signal optimization

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• 作者：Sweta Srivastava ; Sudip Kumar Sahana
• 关键词：Computational techniques ; Problem solving ; GA ; Ant Colony ; Genetic Algorithm ; Petri Net ; Game playing ; Traffic signal ; Optimization techniques
• 刊名：Applied Intelligence
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：46
• 期：1
• 页码：113-123
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence (incl. Robotics); Mechanical Engineering; Manufacturing, Machines, Tools;
• 出版者：Springer US
• ISSN：1573-7497
• 卷排序：46

文摘

A noble Nested Hybrid Evolutionary Model is presented to reduce the wait time of vehicles at traffic signals and improve the mobility within the road network. In effect, it contributes towards achieving green environment and reducing the fuel consumption. The proposed model is based on Bi-level Stackelberg Game in which the upper layer is “traffic signals” which is optimized using evolutionary computational techniques (ACO, GA and a Hybrid of ACO and GA) and the lower layer is “stochastic user equilibrium” for which road network is designed using Petri Net (PN) respectively. A comparative analysis has been carried out and it was found that nested hybrid model outperforms ACO and GA.