Intersection Signal Timing Optimization: A Multi-Objective Evolutionary Algorithm

Xinghui Zhang, Xiumei Fan, Shunyuan Yu, Axida Shan, Shujia Fan, Yan Xiao and Fanyu Dang
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Xinghui Zhang: Department of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China
Xiumei Fan: Department of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China
Shunyuan Yu: College of Electronics and Information Engineering, Ankang University, Ankang 725000, China
Axida Shan: Department of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China
Shujia Fan: Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
Yan Xiao: Department of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China
Fanyu Dang: Department of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China

Sustainability, 2022, vol. 14, issue 3, 1-16

Abstract: The rapid motorization of cities has led to the increasingly serious contradiction between supply and demand of road resources, and intersections have become the main bottleneck of traffic congestion. In general, capacity and delay are often used as indicators to improve intersection efficiency, but auxiliary indicators such as vehicle emissions that contribute to sustainable traffic development also need to be considered. It is necessary to evaluate intersection traffic efficiency through multiple measures to reflect different aspects of traffic, and these measures may conflict with each other. Therefore, this paper studies a multi-objective urban traffic signal timing problem, which requires a reasonable signal timing parameter under a given traffic flow condition, to better take into account the traffic capacity, delay and exhaust emission index of the intersection. Firstly, based on the traffic flow as the basic data, combined with the traffic flow description theory and exhaust emission estimation rules, a multi-objective model of signal timing problem is established. Secondly, the target model is solved and tested by the genetic algorithm of non-dominated sorting framework. It is found that the Pareto solution set of traffic indicators obtained by NSGA-III has a larger domain. Finally, the search mechanism of evolutionary algorithm is essentially unconstrained, while the actual traffic signal timing problem is constrained by traffic environment. In order to obtain a better signal timing scheme, this paper introduces the method of combining hybrid constraint strategy and NSGA-III framework, abbreviated as HCNSGA-III. The simulation experiment was carried out based on the same target model. The simulated results were compared with the actual scheme and the timing scheme obtained in recent research. The results show that the indices of traffic capacity, delay and exhaust emission obtained by the proposed method have more obvious advantages.

Keywords: intersection; signal timing; multi-objective; evolutionary algorithm; NSGA-III (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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