基于蚁群算法的交通信号配时优化
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摘要
随着社会的飞速发展，各大城市机动车数量急剧增加，这导致城市道路交通拥堵问题日趋严重。目前，我国多数城市存在交通拥堵现象，这给城市发展造成很大压力，同时也制约着城市发展，而导致交通拥堵的主要原因是交通信号配时不合理。交通信号配时问题是一个多目标优化问题，存在多个彼此冲突的目标，如何获取问题的最优解，一直以来都是学术界关注的焦点问题。因此，寻找一种适合于求解多目标优化问题的算法显得尤为重要。蚁群算法是90年代初提出的一种新型的全局优化算法,该算法具有信息正反馈、分布式计算、并行性、以及强鲁棒性的优点，这为求解优化问题提供了一种新的思路，也给其带来了新的活力。因此，蚁群算法引起了国内外专家学者的高度关注，并广泛应用于交通信号控制领域中。
本文在交通信号配时优化理论的基础上，针对信号灯配时不合理的问题，从以下几个方面展开了工作。
首先，本文以黄灯变亮时即将到达停车线的车辆为研究对象，在对其分析之后给出了减速停车和加速通过两种决策，并分别讨论了两种决策下黄灯时间最小值的计算公式以及黄灯的合理配时。
其次，本文研究了基于蚁群算法的单点信号配时优化问题，在对单点信号控制中的三个重要参数定义之后，建立了单点信号配时优化模型，然后用Webster算法、遗传算法和蚁群算法来分别求解两相位和四相位交叉口的单点信号配时优化问题，并将三种算法所得的数值结果进行了对比分析，从而证明蚁群算法比Webster算法和遗传算法更有优势。
最后，本文对主干路信号配时优化问题进行了研究，并分别用遗传算法和蚁群算法来求解两相邻交叉口的主干路信号配时优化问题，通过对比两种算法下的数值结果，证明蚁群算法是有效可行的。
With the rapid development of society, traffic congestion has become one ofthe most serious problems in many cities at present, which brings great challengeto the development of cities. However，the primary reason for traffic congestionis the irrational cycle time of traffic lights. Traffic signal timing problem is amulti-objective optimization problem and has more conflicting objectives，whichhas being obtained comprehensive attention from domestic and alien scholars.Therefore, to design an effective algorithm is obviously important for multi-objective optimization. Ant colony algorithm (ACA) is a new simulatedevolutionary optimization algorithm with the characteristics of positive feedback,distributed computing and strong robustness, which provides some new ideas forsolving optimization problems. Nowadays, with the development of artificialintelligence technology, ACA has been applied to signal timing optimizationproblems.
Based on traffic signal timing optimization theory, aiming at theirrationality of traffic light time, the work in this paper is expanded fromfollowing aspects.
Firstly, we study the normally running vehicles when the yellow light is on,and analyze two decisions of drivers such as parking at a reduced speed andpassing the intersection at an accelerated speed, then we can easily obtain theminimum of yellow light time.
Secondly, single signal timing optimization based on ACA is mainly studiedin this paper. After three important parameters in single signal control are defined,its optimization model is established based on three weighting coefficients oftime delay, number of stops and traffic capacity, then it is solved by means ofthree optimization algorithms—Webster algorithm, genetic algorithm (GA) andACA. In a word, we can achieve better performance by ACA, which can well meet the actual traffic demand.
Thirdly, signal timing optimization for the trunk road based on ACA isstudied in this paper. On the basis of three parameters such as time delay, numberof stops and traffic capacity, its optimization model is established and solved bymeans of two optimization algorithms—GA and ACA. The results show thatACA is a simple and feasible method for signal timing optimization problems.
本文在交通信号配时优化理论的基础上，针对信号灯配时不合理的问题，从以下几个方面展开了工作。
首先，本文以黄灯变亮时即将到达停车线的车辆为研究对象，在对其分析之后给出了减速停车和加速通过两种决策，并分别讨论了两种决策下黄灯时间最小值的计算公式以及黄灯的合理配时。
其次，本文研究了基于蚁群算法的单点信号配时优化问题，在对单点信号控制中的三个重要参数定义之后，建立了单点信号配时优化模型，然后用Webster算法、遗传算法和蚁群算法来分别求解两相位和四相位交叉口的单点信号配时优化问题，并将三种算法所得的数值结果进行了对比分析，从而证明蚁群算法比Webster算法和遗传算法更有优势。
最后，本文对主干路信号配时优化问题进行了研究，并分别用遗传算法和蚁群算法来求解两相邻交叉口的主干路信号配时优化问题，通过对比两种算法下的数值结果，证明蚁群算法是有效可行的。
With the rapid development of society, traffic congestion has become one ofthe most serious problems in many cities at present, which brings great challengeto the development of cities. However，the primary reason for traffic congestionis the irrational cycle time of traffic lights. Traffic signal timing problem is amulti-objective optimization problem and has more conflicting objectives，whichhas being obtained comprehensive attention from domestic and alien scholars.Therefore, to design an effective algorithm is obviously important for multi-objective optimization. Ant colony algorithm (ACA) is a new simulatedevolutionary optimization algorithm with the characteristics of positive feedback,distributed computing and strong robustness, which provides some new ideas forsolving optimization problems. Nowadays, with the development of artificialintelligence technology, ACA has been applied to signal timing optimizationproblems.
Based on traffic signal timing optimization theory, aiming at theirrationality of traffic light time, the work in this paper is expanded fromfollowing aspects.
Firstly, we study the normally running vehicles when the yellow light is on,and analyze two decisions of drivers such as parking at a reduced speed andpassing the intersection at an accelerated speed, then we can easily obtain theminimum of yellow light time.
Secondly, single signal timing optimization based on ACA is mainly studiedin this paper. After three important parameters in single signal control are defined,its optimization model is established based on three weighting coefficients oftime delay, number of stops and traffic capacity, then it is solved by means ofthree optimization algorithms—Webster algorithm, genetic algorithm (GA) andACA. In a word, we can achieve better performance by ACA, which can well meet the actual traffic demand.
Thirdly, signal timing optimization for the trunk road based on ACA isstudied in this paper. On the basis of three parameters such as time delay, numberof stops and traffic capacity, its optimization model is established and solved bymeans of two optimization algorithms—GA and ACA. The results show thatACA is a simple and feasible method for signal timing optimization problems.
引文
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[7]李文勇,陈学武.基于TSIS二次开发的交通诱导系统仿真分析[J].武汉理工大学学报（交通科学与工程版),2008,32(5):798-801.
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[9]袁春华,史峰.利用冲突点的车流特点确定信号控制周期[J].华东交通大学学报,2002,19(4):14-16.
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[12]王秋平,谭学龙,张生瑞.城市单点交叉口信号配时优化[J].交通运输工程学报,2006,6(2):60-64.
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[14]Boris Bontoux, Dominique Feillet. Ant colony optimization for the traveling purchaserproblem [J]. Computers and Operations Research,2008(35):628-637.
[15]钟连超,叶彬强,罗劲松等.交通灯控制系统中黄灯时间的确定[J].重庆工学院学报（自然科学),2008,22(4):154-156.
[16]李艳,樊晓平.基于模糊控制的城市交叉路口群信号控制及仿真[J].交通运输工程学报,2003,3(2):117-119.
[17]钱红波,李克平.绿灯间隔时间对交叉口交通安全的影响研究[J].中国安全科学学报,2008,18(6):166-170.
[18]李瑞敏.城市道路交通管理[M].北京:人民交通出版社,2009.
[19]王秀良.交通灯控制系统中黄灯时间的确定[J].交通标准化,2010(224):114-117.
[20]徐学珍,田宏伟.快速路驾驶员反应时间的确定[J].信息技术与信息化,2007(4):105-108.
[21]李克平,杨佩昆,倪颖.城市道路交叉口信号控制中的黄灯问题[J].城市交通,2010,8(4):67-72.
[22]蒲琪,谭永朝,杨超.交叉口信号配时优化模型研究[J].上海铁道大学学报,1999,20(4):31-34.
[23]谷良贤,龚春林.基于遗传算法的组合优化方法在整体式冲压发动机导弹一体化设计中的应用[J].西北工业大学学报,2004,22(3):317-320.
[24]曾囡莉,廖晓昕,王明哲.基于遗传算法的生产调度[J].微型机与应用,2003,5:58-60.
[25]吴勇,郝矿荣,丁永生等.基于遗传算法的机器人动态视觉检测系统[J].电子测量技术,2009,32(4):108-112.
[26]郑华.PCBs故障红外诊断系统中基于遗传算法的图像处理研究[D].成都:电子科技大学,2003.
[27]万月珍,王以治.基于遗传算法的自动控制系统数学模型优化[J].武钢技术,1999,37(3):16-19.
[28]周剑利,马壮,陈贵清.基于遗传算法的人工生命演示系统的研究与实现[J].制造业自动化,2009,31(9):38-40.
[29]Colorni A, Dorigo M, et al. Ant system for job shop scheduling [J]. Belgian Journal ofOperations Research Statistics and Computer Science,1994,34(1):39-53.
[30]何佳佳,侯再恩.PID参数优化算法[J].化工自动化及仪表,2010,37(11):1-4．
[31]Aybars U ur, Do an Aydin. An interactive simulation and analysis software for solvingTSP using Ant Colony Optimization algorithms [J]. Advances in Engineering Software,2009,40:341-349.
[32]Yuvraj Gajpal, Prakash Abad. An ant colony system (ACS) for vehicle routing problemwith simultaneous delivery and pickup [J]. Computers&Operations Research,2009,36:3215--3223.
[33]Do an Aydin, Aybars U ur. Extraction of flower regions in color images using ant colonyoptimization [J]. Procedia Computer Science,2011,3:530-536.
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