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首页> 外文期刊>Chinese Journal of Electronics >A Hybrid Cellular Swarm Optimization Method for Traffic-Light Scheduling
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A Hybrid Cellular Swarm Optimization Method for Traffic-Light Scheduling

机译：交通灯调度的混合细胞群优化方法

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摘要

With increasing traffic every day, most cities in the world are facing serious traffic problems, such as traffic accidents, congestion and air pollution. Despite the recent improvement of urban infrastructure, reasonable traffic light scheduling still plays an important role in alleviating these traffic problems. It is a great challenge to schedule a huge number of traffic lights efficiently. To solve this problem, we propose a Hybrid cellular swarm optimization method (HCSO) to optimize the scheduling of urban traffic lights. HCSO achieves an efficient and hexible scheduling, which includes the phase timing scheduling and the phase shifting scheduling. To formulate effective solutions for various traffic problems and achieve a globally dynamic scheduling, hexible and concise transition rules based on Cellular automaton (CA) are defined. And the Dynamic cellular particle swarm optimization algorithm (DCPSO) is proposed to find the optimal phase timing scheduling efficiently. Moreover, compared with the differential search algorithm method, the genetic algorithm method, the particle swarm optimization method, the comprehensive learning particle swarm optimization method and the random method in real cases, extensive experiments reveal that HCSO achieves obvious improvements under different traffic conditions.
机译：随着每天流量的增加，世界上大多数城市都面临着严重的交通问题，例如交通事故，交通拥堵和空气污染。尽管最近改善了城市基础设施，但合理的交通信号灯调度在减轻这些交通问题方面仍起着重要作用。有效安排大量交通信号灯是一个巨大的挑战。为了解决这个问题，我们提出了一种混合蜂窝群优化方法（HCSO）来优化城市交通信号灯的调度。 HCSO实现了高效且灵活的调度，其中包括相位时序调度和相移调度。为了制定各种交通问题的有效解决方案并实现全局动态调度，定义了基于蜂窝自动机（CA）的灵活简洁的过渡规则。提出了动态细胞粒子群优化算法（DCPSO），以有效地找到最优的相位时序调度。此外，与实际情况下的差分搜索算法，遗传算法，粒子群优化方法，综合学习粒子群优化方法和随机方法相比，大量实验表明，在不同的交通条件下，HCSO都有明显的改善。

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