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.
Highlights
With the quick development of science techniques and industrial society in China, the city population scale and city ground extend continuously
(2) Aiming at the problem that the multi-objective optimization algorithm is easy to fall into the local optimum or the diversity becomes worse, and the convergence becomes worse when solving the actual traffic signal timing problem, by combining the hybrid constraint strategy, the HCNSGA-III algorithm is proposed to avoid falling into the local optimum or improve the diversity to ensure the convergence
In Ref. [34], a multi-objective optimization method is proposed by using Pareto optimization combined with Particle swarm optimization (PSO) algorithm to optimize per capita delay, vehicle emission and intersection capacity
Summary
With the quick development of science techniques and industrial society in China, the city population scale and city ground extend continuously. It is urgent to study intersection signal timing, improve intersection efficiency, make traffic infrastructure more convenient for users and make the environment sustainable. In order to optimize multiple objectives in ITSTP, it is usually necessary to establish a mathematical model of intersection traffic efficiency index to connect the optimization objectives with decision variables. NSGA-III and NSGA-II for solving multi-objective optimization problems in traffic signal timing is compared. (2) Aiming at the problem that the multi-objective optimization algorithm is easy to fall into the local optimum or the diversity becomes worse, and the convergence becomes worse when solving the actual traffic signal timing problem, by combining the hybrid constraint strategy, the HCNSGA-III algorithm is proposed to avoid falling into the local optimum or improve the diversity to ensure the convergence.
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