城市道路交叉路口交通信号模糊控制系统设计与实现
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摘要
随着我国经济的快速发展以及城市化进程的加快,近年各大城市机动车数量急剧增加,交通问题已成为亟待解决的民生问题之一。城市交通系统的道路车辆流量具有较大的时变性和随机性,传统交叉口信号灯控制方法已难以满足交叉路口的通行量、疏导时间等需求。
针对这一问题,引入模糊逻辑算法,设计了一种实时的智能交通信号控制系统。该系统利用车辆计数传感器获得各路口的车流量,根据交叉路口现状设计了一个单变量二维模糊逻辑控制器。该控制器以等待车辆数量和交叉方向等待车辆数量之差作为输入,绿灯延时作为输出。其中,模糊控制规则表在离线状态下转化为查询表,并存储于计算机中以供系统在线查询。实际应用时,系统通过查表根据路口等待车辆数量变化自动调节绿灯时间。
本文提出了该智能交通信号控制系统的软硬件设计方案。系统以AT89S52单片机为核心,利用Intel8279芯片为键盘及显示器接口电路,采用C语言开发了具有交通灯控制、功率驱动、绿切等功能的交通灯模糊逻辑控制器。
采用路口交通实例,对两种不同信号控制方式进行仿真实验和现场测量比较。试验结果表明感应控制方式优于多时段定周期控制,在交叉口流量较小的情况下表现得更突出。虽然实时交通数据和状况的不断地更新,但实时模糊控制系统仍能保证最优的交叉路口利用率,缩短了车辆平均等待延误时间。
With the rapid economic development and the speeding up urbanization process, the number of motor vehicles has been increasing sharply resent years, which leads the traffic problem to one of the urgent livelihood problems in China. Due to the time variation and randomness of the intersection vehicle flow in the modern urban transport system, the conventional control method of intersection traffic light has become difficult to meet the demands of traffic intersection such as the traffic capacity, traffic dispersion and so on.
According to the problems, a real time intelligent traffic signal control system is projected based on fuzzy logic algorithm. The traffic flow of each intersection is acquired through the vehicle counting sensors in the system. And a single variable fuzzy logic controller with two dimensions inputs is designed aimed, the inputs of which are the number of waiting vehicles under the green light phase and the difference between and the number under the red light phase, while the output of which is the delay time of the green light. The fuzzy control rules table is translated into a query table off-time, and then the query table is stored in the memory for querying online. In practice, the green light time is adjust automatically based on the waiting cars in the cross road.
The software and hardware of the intelligent traffic signal control system are proposed in the paper. The intelligent traffic signal control system based on fuzzy logic algorithm could control the traffic light, drive power and so on using C language. It adopts AT89S52 microcontroller as the core chip, and Intel8279 as input/output device.
Two different ways of signal control are compared with the intersection traffic data. The results show that the vehicle actuated control is better than the multiple interval fixed-time control, and the advantage of the former is distinguished when there is not many vehicles in the cross road. Although the real data and status of the intersection traffic are update continuously, the real fuzzy logic control system can guarantee the optimal adaptability of the intersection, and reduce the vehicle's average waiting delay time.
针对这一问题,引入模糊逻辑算法,设计了一种实时的智能交通信号控制系统。该系统利用车辆计数传感器获得各路口的车流量,根据交叉路口现状设计了一个单变量二维模糊逻辑控制器。该控制器以等待车辆数量和交叉方向等待车辆数量之差作为输入,绿灯延时作为输出。其中,模糊控制规则表在离线状态下转化为查询表,并存储于计算机中以供系统在线查询。实际应用时,系统通过查表根据路口等待车辆数量变化自动调节绿灯时间。
本文提出了该智能交通信号控制系统的软硬件设计方案。系统以AT89S52单片机为核心,利用Intel8279芯片为键盘及显示器接口电路,采用C语言开发了具有交通灯控制、功率驱动、绿切等功能的交通灯模糊逻辑控制器。
采用路口交通实例,对两种不同信号控制方式进行仿真实验和现场测量比较。试验结果表明感应控制方式优于多时段定周期控制,在交叉口流量较小的情况下表现得更突出。虽然实时交通数据和状况的不断地更新,但实时模糊控制系统仍能保证最优的交叉路口利用率,缩短了车辆平均等待延误时间。
With the rapid economic development and the speeding up urbanization process, the number of motor vehicles has been increasing sharply resent years, which leads the traffic problem to one of the urgent livelihood problems in China. Due to the time variation and randomness of the intersection vehicle flow in the modern urban transport system, the conventional control method of intersection traffic light has become difficult to meet the demands of traffic intersection such as the traffic capacity, traffic dispersion and so on.
According to the problems, a real time intelligent traffic signal control system is projected based on fuzzy logic algorithm. The traffic flow of each intersection is acquired through the vehicle counting sensors in the system. And a single variable fuzzy logic controller with two dimensions inputs is designed aimed, the inputs of which are the number of waiting vehicles under the green light phase and the difference between and the number under the red light phase, while the output of which is the delay time of the green light. The fuzzy control rules table is translated into a query table off-time, and then the query table is stored in the memory for querying online. In practice, the green light time is adjust automatically based on the waiting cars in the cross road.
The software and hardware of the intelligent traffic signal control system are proposed in the paper. The intelligent traffic signal control system based on fuzzy logic algorithm could control the traffic light, drive power and so on using C language. It adopts AT89S52 microcontroller as the core chip, and Intel8279 as input/output device.
Two different ways of signal control are compared with the intersection traffic data. The results show that the vehicle actuated control is better than the multiple interval fixed-time control, and the advantage of the former is distinguished when there is not many vehicles in the cross road. Although the real data and status of the intersection traffic are update continuously, the real fuzzy logic control system can guarantee the optimal adaptability of the intersection, and reduce the vehicle's average waiting delay time.
引文
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[3]J.R.Wootton, A G Ortiz, S M Amin. Ingelligent transportation system:a global market presperctive[J]. Mathmatical Comput. Modelling.1995,22:295～268
[4]刘智勇,梁渭清.城市交通信号控制的进展[J].公路交通科技,2003,20(6)：121～125
[5]金稳.多相位智能交通控制器的研究[D].浙江大学,2007.
[6]杜娟.智能交通系统中交叉口的模糊控制及算法[D],辽宁工学院.2007.
[7]卢喜光.发达国家的ITS发展[J].高科技与产业化,2008,6～55.
[8]史其信,陆化普.中国ITS发展战略构想[J].公路交通科技,1999,2：13～16.
[9]Chang Tang Hsien, Lin Jen Ting. Optimal Signal Timing for an Oversaturated Intersection, Transportation Research Part B:Methodological.2000,34(6): 471～491.
[10]Ahmad, Azrulnor. Development of traffic light control system using programmable logic controller[D]. Malaysia Pahang University.2007.
[11]Tillotson H T. The automatic control of urban road traffic[J]. Physics in Technology.1984,15(3):136～142.
[12]史忠科.交通控制系统导论[M].北京：科学出版社.2003.
[13]刘智勇.智能交通控制理论及其应用[M].北京：科学出版社,2003.
[14]雷常青.交通控制与诱导递阶协调方法的研究[D].天津：天津大学,2004.
[15]赵建玉.智能计算方法在城市交通中的应用与交通流建模研究[D].山东：山东大学,2006.
[16]赵杰,胡子祥,刘丽亚.国外发展快速公交的经验及对我国城市交通发展的启示[J].城市交通,2004,2(3)：56～59.
[17]高雨.多相位智能交通控制器研究[D].浙江大学,2006.
[18]温惠英,尹宏斌,徐建闽.一种信号交叉口模糊交通控制方法[J].华南理工大学学报(自然科学版),2001,12(29)：24～27.
[19]赵凯.城市智能交通信号控制系统[D].西北工业大学.2001.
[20]李红梅.基于MATLAB的短时交通流预测分析及实现[D].山东科技大 学,2007.
[21]杨兆升.城市智能公共交通系统理论与方法[M].北京：中国铁道出版社,2002.
[22]徐冬玲,方建安,邵世煌.交通系统的模糊控制及其神经网络实现[J].信息与控制,1992,21(2)：74～79.
[23]沈国江,王智,刘翔等.城市区域交通智能控制研究[J].信息与控制,2004,33(1)：2～5.
[24]陈洪,陈森发单路口交通实时模糊控制的一种方法[J].信息与控制.1997,26(3)：227～233.
[25]赵晨,胡福乔,施鹏飞.基于模糊逻辑的路口交通信号控制[J].计算机工程.2003,29(10)：50～52.
[26]张宗华,张伟,赵霖.利用遗传算法实现交通控制系统优化方法[J].计算机工程,2003,29(12)：53～54.
[27]宫晓燕,陈德望,刘小明.基于有序样本聚类的交叉口交通信号控制算法研究[J].公路交通科技.2003,20(1)：101～104.
[28]蒋忠远,宋文,杨博.基于Petri网的交通信号控制系统[J].系统仿真学报.2007,19(8)：221～226.
[29]龙建成,高自友,任华玲.城市网络交通动态信号控制方法[J].中国公路学报,2009,22(4)：108～114.
[30]马万经,李晓丹,杨晓光.基于路径的信号控制交叉口关联计算模型[J].统计大学学报(自然科学版).2009,37(11)：1462～1466.
[31]马莹莹,杨晓光,曾滢.信号控制交叉口周期时长多目标优化模型及求解[J].同济大学学报(自然科学版).2009,37(6)：761～765.
[32]吴明晖,黄辉先,谭志辉等.一种交叉路口信号优化控制模型的研究与仿真[J],系统仿真学报,2006,18(7)：1866～1869.
[33]曾光奇,胡均安.模糊控制理论与工程应用[M].武汉：华中科技大学出版社,2006.
[34]Pappis P, Mamdani H. A fuzzy logic controller for a traffic junction[J]. IEEE Transactions on System, Man and Cybernetics,1997,7(10):707～717.
[35]Nakatsuyama M, NagahashiH, Nishizuka N. Fuzzy logic phase controller for traffic junctions in the one way arterial road [J] In Proceeding of IFAC 9th Triennial World Cong., Budapest, Hungary,1984:865～2870.
[36]Skowronski W, Shaw L. Self-learning fuzzy traffic controller for a traffic junction[J]. European Congress on Intelligent Techniques and Soft Computing EUFIT 93, Aachen, Germany,1993:751～761.
[37]Porche I. Lafortune S. Adaptive Look-ahead Optimization of Traffic Signals[J]. ITS Journal,4(3):209-254.
[38]Chou C H, Teng J C. A Fuzzy Logic Controller for Traffic Junction Signals[J]. Proceedings of the 6th Joint Conference on Information Sciences,2002,859～870..
[39]Y Sazi Murat, Ergun Gedizlioglu, A fuzzy logic multi～phased signal control model for isolated junctions[J]. Transportation Research Part C,2005,3:19～36.
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