基于智能交通系统的绿色交通双层优化模型

Kun Liu
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引用次数: 1

摘要

本文提出了一个包含三种算法的双层优化模型(BLOM)。BLOM旨在实现智能交通系统上下级模型的燃油节约和二氧化碳减排。在上层模型中,交通信号方案的优化是为了使单位时间内通过路口的总油耗最小。同时,交通信号信息数据被发送到下层模型,在下层模型中,车辆的运动状态被优化为绿色交通。为了比较和改进模型的性能,实现了遗传算法和粒子群混合优化的上层模型和遗传算法与粒子群混合优化的下层模型(GA-PSO/GA-PSO)、上层模型和下层模型的遗传算法(GA/PSO)和两层模型的遗传算法(GA/GA)三种算法。仿真结果表明,GA-PSO/GA-PSO混合算法比其他GA/PSO和GA/GA算法收敛速度更快,且具有最佳的分辨率和最少的计算时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bi-level optimisation model for greener transportation with intelligent transport system
In this paper, we propose a bi-level optimisation model (BLOM) with three algorithms. BLOM is intended for fuel saving and carbon dioxide emission reduction in both upper-level and lower-level model with intelligent transport system. Traffic signal schemes are optimised for minimising total fuel consumption passing through a road intersection in unit time in the upper-level model. At the same time, traffic signal information data are sent to the lowerlevel model in which vehicle motion states are optimised for greener transportation. Three algorithms include hybrid genetic algorithm and particle swarm optimisation in upper-level model with hybrid genetic algorithm and particle swarm optimisation in lower-level model (GA-PSO/GA-PSO), GA in upper-level model with PSO in lower-level model (GA/PSO) and GA in both level model (GA/GA) are realised to compare and improve the performance of the model. The simulation results derive GA-PSO/GA-PSO hybrid algorithm converges faster with the best resolution and least calculation time than other GA/PSO and GA/GA algorithms.
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