智能交通信号控制中英文对照外文翻译文献

单片机交通灯中英文资料对照外文翻译文献原文题目：DESIGN OF TRAFFIC LIGHT BASED ON MCUBecause of the rapid development of our economy resulting in the car number of large and medium-sized cities surged and the urban traffic, is facing serious test, leading to the traffic problem increasingly serious, its basically are behaved as follows: traffic accident frequency, to the human life safety enormous threat, Traffic congestion, resulting in serious travel time increases, energy consumption increase; Air pollution and noise pollution degree of deepening, etc. Daily traffic jams become people commonplace and had to endure. In this context, in combination with the actual situation of urban road traffic, develop truly suitable for our own characteristics of intelligent signal control system has become the main task.PrefaceIn practical application at home and abroad, according to the actual traffic signal control application inspection, planar independent intersection signal control basic using set cycle, much time set cycle, half induction, whole sensor etc in several ways. The former two control mode is completely based on planar intersection always traffic flow data of statistical investigation, due to traffic flow the existence of variable sex and randomicity, the two methods have traffic efficiency is low, the scheme, the defects of aging and half inductive and all the inductive the two methods are in the former two ways based on increased vehicle detector and according to the information provided to adjust cycle is long and green letter of vehicle, it than random arrived adaptability bigger, can make vehicles in the parking cord before as few parking, achieve traffic flowing effectIn modern industrial production,current,voltage,temperature, pressure, and flow rate, velocity, and switch quantity are common mainly controlled parameter. For example: in metallurgical industry, chemical production, power engineering, the papermaking industry, machinery and food processing and so on many domains, people need to transport the orderlycontrol. By single chip microcomputer to control of traffic, not only has the convenient control, configuration simple and flexible wait for an advantage, but also can greatly improve the technical index by control quantity, thus greatly improve product quality and quantity. Therefore, the monolithic integrated circuit to the traffic light control problem is an industrial production we often encounter problems.In the course of industrial production, there are many industries have lots of traffic equipment, in the current system, most of the traffic control signal is accomplished by relays, but relays response time is long, sensitivity low, long-term after use, fault opportunity increases greatly, and adopts single-chip microcomputer control, the accuracy of far greater than relays, short response time, software reliability, not because working time reduced its performance sake, compared with, this solution has the high feasibility.About AT89C511.function characteristics description:AT89C51 is a low power consumption, high performance CMOS8 bit micro-controller, has the 8K in system programmable Flash memory. Use high-density Atmel company the beltpassword nonvolatile storage technology and manufacturing, and industrial 80S51 product instructions and pin fully compatible. Chip Flash allow program memory in system programmable, also suitable for conventional programmer. In a single chip, have dexterous 8 bits CPU and in system programmable Flash, make AT89C51 for many embedded control application system provides the high flexible, super efficient solution. AT89C51 has the following standard function: 8k bytes Flash, 256 bytes RAM, 32-bit I/O mouth line, the watchdog timer, two data pointer, three 16 timer/counter, a 6 vector level 2 interrupt structure, full-duplex serial port, piece inside crystals timely clock circuit. In addition, AT89C51 can drop to 0Hz static logic operation, support two software can choose power saving mode. Idle mode, the CPU to stop working, allowing the RAM, timer/counter, serial ports, interruption continue to work. Power lost protection mode, RAM content being saved, has been frozen, microcontroller all work stop, until the next interruption or hardware reset so far. As shown in figure 1 for the AT89C51 pins allotment.Figure 1 the AT89C51 pins allotment2.interrupt introductionAT89C51 has six interrupt sources: two external interruption, (and), three timer interrupt (timer 0, 1, 2) and a serial interrupts. Each interrupt source can be passed buy bits or remove IE the relevant special register interrupt allow control bit respectively make effective or invalid interrupt source. IE also includes an interrupt allow total control bit EA, it can be a ban all interrupts. IE. Six is not available. For AT89C51, IE. 5 bits are also not be used. User software should not give these bits write 1. They AT89 series for new product reserved. Timer 2 can be TF2 and the T2CON registers EXF2 or logical triggered. Program into an interrupt service, the sign bit can be improved by hardware qing 0. In fact, the interrupt service routine must determine whether TF2 or EXF2 activation disruption, the sign bit must also by software qing 0. Timer 0 and 1 mark a timer TF0 and TF1 has been presented in the cycle count overflow S5P2 074 bits. Their value until the next cycle was circuit capture down. However, the timer 2 marks a TF2 in count overflow of the cycle of S2P2 074 bits, in the same cycle was circuit capture down3.external clock driving characteristicsTable 14.leisure and power lost pattern external pins stateTable 2About 8255 chip1.8255 features:(1)A parallel input/output LSI chips, efficacy of I/O devices, but as CPU bus and peripheral interface.(2)It has 24 programmable Settings of I/O mouth, even three groups of 8 bits I/O mouth to mouth, PB mouth and PA PC mouth. They are divided into two groups 12 I/O mouth, A group including port A and C mouth (high four, PC4 ~ PC7), including group B and C port B mouth (low four, PC0 ~ PC3). A group can be set to give basic I/O mouth, flash control (STROBE) I/O flash controlled, two-way I/O3 modes, Group B can only set to basic I/O or flash controlled the I/O, and these two modes of operation mode entirely by controlling registers control word decision.2. 8255 pins efficacy:(1). RESET: RESET input lines, when the input outside at high levels, all internal registers (including control registers) were removed, all I/O ports are denoting input methods.(2). CS: chip choose a standard lamp line 1, when the input pins for low levels, namely/CS = 0, said chip is selected, allow 8255 and CPU for communications, / CS = 1, 8255 cannot with CPU do data transmission.(3). RD: read a standard lamp line 1, when the input pins for low levels, namely/RD = 0 and/CS = 0, allow 8255 through the data bus to the CPU to send data or state information, namely the CPU 8255 read from the information or data.(4). The WR: write a standard lights, when the input pins for low levels, namely/WR = 0 and/CS = 0, allows the CPU will data or control word write 8255.(5). D7: three states D0 ~ two-way data bus, 8255 and CPU data transmission channel, when the CPU execution input/output instruction, through its realization 8 bits of data read/write operation, control characters and status information transmitted through the data bus.(6). PA0 ~ PA7: port A input and output lines, A 8 bits of data output latches/buffers, an 8 bits of data input latches.(7). PB0 ~ PB7: port B input and output lines, a 8 bits of I/O latches, an 8 bits of input and output buffer.(8). PC0 ~ PC7: port C input and output lines, a 8 bits of data output latches/buffers, an 8 bits of data input buffer. Port C can through the way of working setting into two four ports, every 4 digit port contains A 4 digit latches, respectively with the port A and port B cooperate to use, can be used as control standard lights output or state standard lights input ports.(9). A0, A1: address selection line, used to select the PA 8255 mouth, PB mouth, PC mouth and controlling registers.When A0=0, A1= 0, PA mouth be chosen;When A0=0, A1 = 1, PB mouth be chosen;When A0=0, A1 = 1, PC mouth be chosen;When A0=1, A1= 1, control register is selected.Concerning seven section LED display introductionThrough light emitting diode chip appropriate link (including series and parallel) andappropriate optical structure. May constitute a luminous display light-emitting segments or shine points. By these luminous segments or shine point can be composed digital tube, symbols tube, m word pipe, tube, multilevel matrix display tube etc. Usually the digital tube, symbols tube, m word tube were called stroke display, but the stroke displays and matrix tube collectively referred to as character displays.1. The LED display classification(1) by word high marks: stroke monitors word high least 1mm (monolithic integrated type more digital tube word high in commonly 2 ~ 3mm). Other types of stroke display tiptop1.27 mm (0.5 inch) even up to hundreds of mm.(2) color-coded score red, orange, yellow, green and several kinds.(3) according to the structure points, reflecting cover type, a single point-elastic and monolithic integrated type.(4) from the luminous section electrode connection mode of points of anode and cathode two kinds.2. LED display parametersDue to the LED display is LED based, so its light, and the electrical characteristics and ultimate meaning of the parameters with most of the same light emitting diode. But because the LED monitor containing multiple light emitting diode, it must has the following specific parameters:(1) the luminous intensity ratioDue to the digital tube paragraphs in the same driving voltage, each are not identical, so positive current each different. The luminous intensity All segments of the luminous intensity values the ratio of the maximum and minimum values for the luminous intensity ratio. The ratio between 2.3 in 1.5 ~, the maximum cannot exceed 2.5.(2) pulse positive currentIF each segment of typical strokes displays for positive dc working current IF, then the pulse, positive current can be far outweigh.someotherwordpeopledontthinkoffirst. Pulse 390v smaller, pulse positive current can be bigger.Traffic signal control typeThe purpose of the traffic signal control are three: first,in time and space space intersection traffic in different directions,control traffic operation order; Second, make onplanar cross the road network on the people and objects of transport at the highest efficiency, Third, as the road users to provide necessary information, and help them to effectively use the traffic facilities. Road traffic signal control of basic types have many points method.According to the control geometry characteristic is divided into: single intersection control - point control, the traffic trunk lines of coordinated control - wire, traffic network coordination control surface controlling; -- According to the control principle differentiates: timing control, induced control and adaptive control.About watch-dog circuitBy single-chip computers.the micro computer system, because of single chip work often can be affected by external electromagnetic interference, causing program run fly while into dead circulation, the program's normal operation be interrupted by single chip microcomputer control system was unable to work, can cause the whole system of come to a standstill, happen unpredictable consequences, so out of microcontroller running status real-time.according consideration, they generate a specially used for monitoring microcontroller program running state of the chip, commonly known as "watchdog" (watchdog).MAX692 was slightly system monitoring circuit chip, have back-up battery switching, power lost discriminant functions monitoring, the watchdog. The encapsulation and pin instructions as figure2shows.Figure 2 MAX692 encapsulation and pinsWatch-dog circuit application, make SCM can in no condition to achieve continuous work, its working principle is: the watchdog chip and MCU an I/O pins are linked together, the I/O pins through program control it regularly to the watchdog of the pins on into high level (or the low level), this program statement is scattered on SCM other control statements, once among single-chip due to the interference makes application run into a fly after theprocedures section into dead circulation state, write the watchdog pins program cannot be executed, this time, the watch-dog circuit will be without microcontroller sent signals, then at it and MCU reset pin connected pin reset signal give out a a, make SCM reposition occurs, namely the program from program memory splittext started, so we realized the MCU automatic reset.Infrared detection circuitThe infrared radiation photon in semiconductor materials stimutes the non-equilibrium carriers (electronic or holes), cause electrical properties change. Because carrier does not escape in vitro, so called within the photoelectric effect. Quantum photoelectric effect high sensitivity, response speed heat detectors much faster, is optional detectors. In order to achieve the best performance, generally need worked in low temperature. Photoelectric detector can be divided into:(1) optical type: also called photoconductive resistance. The incident photon stimulate the valence band uniform semiconductor electronic across forbidden band into the conduction band and left in valence band, cause cavitation increases, for electric conductance eigen light conductivity. From the band gaps of impurity level also can stimulate light into the conduction band or born carriers valence band, and for impurities light conductivity. The cutoff wavelength by impurity ionization energy (ie) decision. Quantum efficiencies below eigen optical and require lower working temperature.(2) photovoltaic type: mainly p - n knot of light born volts effect. Energy more than the width of infrared photonic band gaps in "area and its nearby of electrons cavitation. Existing "electric field make hole into p area, electronic into n area, two parts appear potentials. Deoxidization device have voltage or current signal. Compared with optical detectors, pv detector detect rate more than forty percent of figure limit, Don't require additional bias electric field and load resistance, no power consumption, having a high impedance. These characteristics of preparation and use of the focal plane array bring great benefits.(3) light emitting - Schottky potential barrier detector: metal and semiconductor contact, typically include PtSi/Si structure and form was Schott potential barrier, infrared photon through Si layer for PtSi absorption, electronic Fermi level, obtain energy leap over left cavitation potential barrier into the Si substrate, PtSi layer of electronic was collected, complete infrared detection. Make full use of Si integration technology, facilitate production,with lower cost and good uniformity wait for an advantage, but make it mass (1024 x 1024 even greater) focal plane array to make up for the defect of quantum low efficiency. Have strict low temperature requirements. With this kind of detector, both at home and abroad has already produced as qualitative good thermography. Pt Si/Si structure made of FPA is the earliest IRFPA.Timing counting and traffic calculationUsing MCS - 51 internal timer/counter for timing, cooperate software delay realizes the timer. This method hardware cost saving, cut allows the reader in timer/counter use, disruptions and programming get exercise and improve. Computation formula is as follows: TC = M - CType in, M for counter touch value, the value and the counter working way concerned.For a traffic intersection, it can in the shortest possible time to achieve maximum traffic, even reached the best performance, we call in unit of time to achieve the maximum flow multi-energy for cars.Use the equation: (traffic = traffic/time) to represent.译文题目：基于单片机的交通灯设计我国经济快速发展，汽车数量猛增，大中型城市的城市交通正面临着严峻的考验，交通问题日益严重，其主要表现如下：交通事故频发，对人类生命安全造成极大威胁；交通拥堵严重，导致出行时间增加，能源消耗加大；空气污染和噪声污染程度日益加深等。

智能交通灯控制研究1绪论研究交通的目的是为了优化运输，人流以及货流。

由于道路使用者的不断增加，现有资源和基础设施有限，智能交通控制将成为一个非常重要的课题。

但是，智能交通控制的应用还存在局限性。

例如避免交通拥堵被认为是对环境和经济都有利的，但改善交通流也可能导致需求增加。

交通仿真有几个不同的模型。

在研究中，我们着重于微观模型，该模型能模仿单独车辆的行为，从而模仿动态的车辆组。

由于低效率的交通控制，汽车在城市交通中都经历过长时间的行进。

采用先进的传感器和智能优化算法来优化交通灯控制系统，将会是非常有益的。

优化交通灯开关，增加道路容量和流量，可以防止交通堵塞，交通信号灯控制是一个复杂的优化问题和几种智能算法的融合，如模糊逻辑，进化算法，和聚类算法已经在使用，试图解决这一问题，本文提出一种基于多代理聚类算法控制交通信号灯。

在我们的方法中，聚类算法与道路使用者的价值函数是用来确定每个交通灯的最优决策的，这项决定是基于所有道路使用者站在交通路口累积投票，通过估计每辆车的好处(或收益)来确定绿灯时间增益值与总时间是有差异的，它希望在它往返的时候等待，如果灯是红色，或者灯是绿色。

等待，直到车辆到达目的地，通过有聚类算法的基础设施，最后经过监测车的监测。

我们对自己的聚类算法模型和其它使用绿灯模拟器的系统做了比较。

绿灯模拟器是一个交通模拟器，监控交通流量统计，如平均等待时间，并测试不同的交通灯控制器。

结果表明，在拥挤的交通条件下，聚类控制器性能优于其它所有测试的非自适应控制器，我们也测试理论上的平均等待时间，用以选择车辆通过市区的道路，并表明，道路使用者采用合作学习的方法可避免交通瓶颈。

本文安排如下：第2部分叙述如何建立交通模型，预测交通情况和控制交通。

第3部分是就相关问题得出结论。

第4部分说明了现在正在进一步研究的事实，并介绍了我们的新思想。

2交通控制模型在这一节中，我们注重信息技术在交通运输方面的应用在这个领域可以获得很多利益，几个国家政府和商业公司从智能运输系统( ITS )中获得了利益。

智能交通信号控制技术的研究与应用（英文中文双语版优质文档）Intelligent traffic signal control technology is a research based on artificial intelligence technology. It is mainly to solve the problem of road traffic congestion in modern cities, optimize the traffic signal system, reduce the incidence of traffic accidents, improve traffic efficiency, reduce energy consumption and environmental pollution .1. Research on Intelligent Traffic Signal Control TechnologyIntelligent traffic signal control technology mainly studies the optimal control of traffic flow and the control strategy of traffic lights. The traditional traffic signal control method adopts the timing control method. The main problem of this method is that it cannot adapt to the traffic flow changes in different time periods, and cannot realize the real sense of traffic optimization control. The intelligent traffic signal control technology uses artificial intelligence technology to realize the adaptive control of the traffic signal system through the analysis and processing of real-time traffic data, so as to realize the optimal control of traffic.The main research content of intelligent traffic signal control technology includes: real-time traffic flow monitoring based on intelligent transportation system, traffic flow prediction based on traffic simulation technology, signal control optimization algorithm based on traffic flow prediction, etc. Among them, real-time traffic flow monitoring based on intelligent transportation system is the basis of intelligent traffic signal control technology. Through collecting traffic data, traffic condition monitoring and data analysis, real-time monitoring and forecasting of traffic flow is realized. The traffic flow prediction based on traffic simulation technology is to predict the traffic flow by establishing a traffic simulation model, and provide traffic flow prediction data for the signal control system. The signal control optimization algorithm based on traffic flow prediction realizes the optimization of traffic signal control by optimizing the signal control strategy according to the forecast data.2. Application of intelligent traffic signal control technologyIntelligent traffic signal control technology has been widely used in many cities. For example, in Shenzhen, Shanghai, Guangzhou and other cities in China, intelligent traffic signal control technology has been widely used and achieved remarkable results. Among them, Shenzhen took the lead in promoting intelligent traffic signal control technology in 2009. Through real-time monitoring and prediction of traffic flow data, it realized adaptive control of traffic signals and effectively solved the problem of urban traffic congestion.In addition, there are many other application scenarios for intelligent traffic signal control technology. For example, in highways, airports, ports and other transportation hubs, through the monitoring and control of traffic flow, the smooth and efficient operation of traffic flow can be achieved, traffic efficiency can be improved, and the incidence of traffic accidents can be reduced. In the public transportation system, intelligent traffic signal control technology can also be applied to the control of bus priority and public bicycle lanes to provide more convenient services for public transportation.also be combined with other technologies, such as GPS positioning technology, vehicle identification technology, road monitoring technology, etc., to achieve more accurate traffic monitoring and signal control, and provide a more comprehensive solution for urban traffic management .3. Advantages and challenges of intelligent traffic signal control technologyadvantages over traditional traffic signal control technology. First of all, it can realize adaptive control of traffic signals, intelligently adjust according to real-time traffic data, and realize real traffic optimization control. Secondly, it can reduce the probability of traffic congestion and accidents, improve the efficiency of traffic operation, and reduce energy consumption and environmental pollution. In addition, intelligent traffic signal control technology can also be used in combination with other traffic management technologies to achieve more comprehensive and precise traffic management.However, intelligent traffic signal control technology also faces some challenges. First of all, it requires a large amount of data support and algorithm optimization, the establishment of a complete data collection and processing system, and the optimization and upgrading of intelligent algorithms. Secondly, its promotion and application need to fully consider the characteristics and actual conditions of urban traffic, and need to fully coordinate and cooperate with urban planning and traffic management departments. Finally, the safety and stability of intelligent traffic signal control technology also needs to be fully guaranteed to avoid traffic accidents and other problems caused by technical problems.4. Future OutlookWith the continuous development and application of artificial intelligence technology, intelligent traffic signal control technology will also be more widely used in the future. In the future, intelligent traffic signal control technology will pay more attention to the realization of traffic intelligence and automation, and use artificial intelligence technology and automatic driving technology to achieve more intelligent, efficient and safe urban traffic management. Specifically, the future intelligent traffic signal control technology will cover the development of the following aspects:1. The intelligence and adaptability of traffic signal control will be more perfect. With the continuous advancement of intelligent algorithms and data processing technologies, traffic signal control systems will become more intelligent, adaptive and flexible, able to more accurately predict and respond to changes in traffic flow, and achieve more efficient and precise traffic signal control.2. Intelligent traffic signal control technology will be integrated with other traffic management technologies to achieve more comprehensive and precise traffic management. In the future, intelligent traffic signal control technology will be used in combination with GPS positioning technology, vehicle identification technology, road monitoring technology and other traffic management technologies to achieve more accurate, comprehensive and efficient urban traffic management.3. Intelligent traffic signal control technology will be widely used in autonomous driving technology and intelligent transportation systems. With the development of autonomous driving technology, intelligent traffic signal control technology will be used in combination with autonomous driving technology to achieve more intelligent and automated urban traffic management. At the same time, intelligent traffic signal control technology will also be widely used in intelligent transportation systems to provide more comprehensive and efficient solutions for urban traffic management.In short, intelligent traffic signal control technology is an important technology in the field of urban traffic management, and has broad application prospects and development space. In the future, we have reason to believe that with the continuous innovation and progress of technology, intelligent traffic signal control technology will bring more extensive and far-reaching influence on urban traffic management and social development.智能交通信号控制技术是一项基于人工智能技术的研究，它主要是为了解决现代城市道路交通拥堵的问题，优化交通信号系统，减少交通事故的发生率，提高交通效率，降低能源消耗和环境污染。

智能交通系统的设计与实现（英文中文双语版优质文档）Intelligent Transportation System (Intelligent Transportation System, ITS) is a traffic management system that integrates information technology, intelligent control technology, sensor technology, communication technology and other technologies. It can improve transportation efficiency, reduce traffic congestion, reduce the incidence of traffic accidents, and at the same time improve the driving experience and provide a better service experience. This paper will discuss the design and implementation of intelligent transportation system from two aspects.1. Design of Intelligent Transportation System1. Functional module designThe functional modules of the intelligent transportation system include data acquisition module, data processing module, decision-making control module and information release module. Among them, the data acquisition module is used to collect traffic information, vehicle information and driving route information; the data processing module is used to process and analyze the collected data, and generate traffic status reports and predictive analysis reports; the decision-making control module is used to process data based on The report generated by the module formulates the optimal route planning and traffic control strategy; the information release module is used to release traffic information and route planning information to drivers and passengers.2. System architecture designThe system architecture of intelligent transportation system includes data acquisition layer, data processing layer, decision-making control layer and information release layer. Among them, the data acquisition layer is mainly composed of sensors and cameras for collecting traffic information and vehicle information; the data processing layer is mainly composed of servers and data processing software for processing and analyzing the collected data; the decision-making control layer is mainly composed of The control center and control software are used to formulate optimal route planning and traffic control strategies; the information release layer is mainly composed of display screens and voice broadcast systems, which are used to release traffic information and route planning information to drivers and passengers.3. Technology selection designThe technologies required by intelligent transportation systems include information technology, intelligent control technology, sensor technology, communication technology, etc. When selecting technology, it is necessary to select the appropriate technology according to the system requirements and technology development status. For example, in terms of sensor technology, you can choose acoustic sensors, image sensors, etc.; in terms of communication technology, you can choose 4G, 5G, etc.; in terms of information technology, you can choose artificial intelligence, big data, etc.2. Realization of Intelligent Transportation System1. Data collection and processingThe data collection of the intelligent transportation system is mainly carried out through sensors and cameras. Sensors can collect data such as traffic flow, vehicle speed, and lane occupancy, and cameras can collect vehicle information and driving route information. The collected data needs to be analyzed and processed by the data processing module to generate traffic status reports and predictive analysis reports. In the data processing module, it is necessary to use data analysis software to clean, process, model and analyze the collected data, such as using machine learning algorithms to predict and analyze the data, and generate traffic congestion prediction reports to provide decision-making information for the decision-making control module. support.2. Route planning and traffic controlThe decision-making control module is one of the most critical modules in the intelligent transportation system. In this module, it is necessary to combine the reports generated by the data processing module and the predictive analysis reports to formulate optimal route planning and traffic control strategies. For example, when traffic is congested, route optimization measures can be taken to avoid congested sections, or traffic signal control measures can be taken to control vehicle speed to reduce congestion. In terms of route planning and traffic control, a decision support system based on artificial intelligence algorithms can be used to achieve optimal route planning and traffic control through data analysis and decision-making models.3. Information release and service experienceThe information release module of the intelligent transportation system is used to release traffic information and route planning information to drivers and passengers. For example, when there is a traffic jam, information about the congestion situation and avoidance routes can be released to drivers and passengers to provide a better driving experience. At the same time, intelligent transportation systems can also provide other service experiences, such as vehicle remote control, vehicle diagnosis and maintenance, etc.Summarize:Intelligent transportation system is a traffic management system that integrates information technology, intelligent control technology, sensor technology, communication technology and other technologies. It can improve transportation efficiency, reduce traffic congestion, reduce the incidence of traffic accidents, and at the same time improve the driving experience and provide a better service experience. The design of intelligent transportation system includes functional module design, system architecture design and technology selection design; the realization of intelligent transportation system includes data collection and processing, route planning and traffic control, information release and service experience, etc. The application of intelligent transportation system will be an important part of the construction of smart cities in the future, and will contribute to the improvement of urban traffic management and public transportation services.智能交通系统（Intelligent Transportation System, ITS）是一种集信息技术、智能控制技术、传感器技术、通讯技术等多种技术于一体的交通管理系统。

中英文对照外文翻译文献(文档含英文原文和中文翻译)智能交通的的设计由于我国经济的快速发展，导致大中型城市汽车数量激增，城市交通面临严峻的考验，导致交通问题增加，其主要表现为：交通事故频发，给人类生命安全造成巨大的威胁，造成严重的交通拥堵，出行时间增加，能源消费的增加；空气污染和噪声污染程度加深等，日常交通拥堵成为人们司空见惯而又不得不忍受。

在此背景下，结合实际情况城市道路交通，发展真正适合我们自己的特点的智能信号控制系统已成为主要任务。

前言在国内外实际应用中，根据实际交通信号控制的应用检验，平面独立的交叉口信号控制基本采用了定周期，多时间的设置周期，半感应，全传感器等几种方式。

前两者的控制模式是完全基于平面交叉口的交通流量数据的统计调查，由于交通流量的现在变性和随机性的存在，这两种方法具有交通效率低的缺陷，该方案，老化和半感应和感应两方法在前两种方式的基础上增加了车辆检测器，根据提供的信息来调整周期和车辆的绿色通道，它比随机到达的适应性大，可以使车辆在交通拥挤前先停车，实现对交通流量的影响。

在现代工业生产中，电流、电压、温度、压力、流量、速度、开关量等都是常用的主要被控参数。

例如：在冶金工业、化工药品的生产、电力工程、造纸行业、机械制造和食品加工等诸多领域，人们需要交通的有序控制。

通过单片机控制交通运输，不仅具有方便的控制、配置简单、灵活等优点，而且还可以通过控制量大幅度提高技术指标，从而大大提高了产品的质量和数量。

因此，单片集成电路的交通灯控制问题是一个工业生产中，我们经常遇到的问题。

在工业生产过程中，有很多行业有大量的交通设备，在目前的系统中，大部分的交通控制信号是通过继电器，而继电器的响应时间长、灵敏度低、长期使用后，故障的机会大大增加，相对于单片机控制，远大于继电器的精度、响应时间短，软件可靠性，不会因为工作时间的缘故而降低其性能，相比，该方案具有较高的可行性。

关于AT89C51（1）功能特点说明：AT89C51是一个低功耗，高性能CMOS8位微控制器，具有8K可编程Flash存储器。

Agent controlled traffic lightsAuthor：Danko A. Roozemond，Jan L.H. RogierProvenance：Delft University of Technology IntroductionThe quality of (urban) traffic control systems is determined by the match between the control schema and the actual traffic patterns. If traffic patterns change, what they usually do, the effectiveness is determined by the way in which the system adapts to these changes. When this ability to adapt becomes an integral part of the traffic control unit it can react better to changes in traffic conditions. Adjusting a traffic control unit is a costly and timely affair if it involves human attention. The hypothesis is that it might offer additional benefit using self-evaluating and self-adjusting traffic control systems. There is already a market for an urban traffic control system that is able to react if the environment changes;the so called adaptive systems. "Real" adaptive systems will need pro-active calculated traffic information and cycle plans- based on these calculated traffic conditions- to be updated frequently.Our research of the usability of agent technology within traffic control can be split into two parts. First there is a theoretical part integrating agent technology and traffic control. The final stage of this research focuses on practical issues like implementation and performance. Here we present the concepts of agent technology applied to dynamic traffic control. Currently we are designing a layered model of an agent based urban traffic control system. We will elaborate on that in the last chapters.Adaptive urban traffic controlAdaptive signal control systems must have a capability to optimise the traffic flow by adjusting the traffic signals based on current traffic. All used traffic signal control methods are based on feed-back algorithms using traffic demand data -varying from years to a couple of minutes - in the past. Current adaptive systems often operate on the basis of adaptive green phases and flexible co-ordination in (sub)networks based on measured traffic conditions (e.g., UTOPIA-spot,SCOOT). These methods are still not optimal where traffic demand changes rapidly within a short time interval. The basic premise is that existing signal plan generation tools make rational decisions about signal plans under varying conditions; but almost none of the current available tools behave pro-actively or have meta-rules that may change behaviour of the controller incorporated into the system. The next logical step for traffic control is the inclusion of these meta-rules and pro active and goal-oriented behaviour. The key aspects of improved control, for which contributions from artificial intelligence and artificial intelligent agents can be expected, include the capability of dealing with conflicting objectives; the capability of making pro-active decisions on the basis of temporal analysis; the ability of managing, learning, self adjusting and responding to non-recurrent and unexpected events (Ambrosino et al.., 1994).What are intelligent agentsAgent technology is a new concept within the artificial intelligence (AI). The agent paradigm in AI is based upon the notion of reactive, autonomous, internally-motivated entities that inhabit dynamic, not necessarily fully predictable environments (Weiss, 1999). Autonomy is the ability to function as an independent unit over an extended period of time, performing a variety of actions necessary to achieve pre-designated objectives while responding to stimuli produced by integrally contained sensors (Ziegler, 1990). Multi-Agent Systems can be characterised by the interaction of many agents trying to solve a variety of problems in a co-operative fashion. Besides AI, intelligent agents should have some additional attributes to solve problems by itself in real-time; understand information; have goals and intentions; draw distinctions between situations; generalise; synthesise new concepts and / or ideas; model the world they operate in and plan and predict consequences of actions and evaluate alternatives. The problem solving component of an intelligent agent can be a rule-based system but can also be a neural network or a fuzzy expert system. It may be obvious that finding a feasible solution is a necessity for an agent. Often local optima in decentralised systems, are not the global optimum. This problem is not easily solved. The solution has to be found by tailoring the interaction mechanism or to have a supervising agent co-ordinating the optimisation process of the other agents. Intelligent agents in UTC,a helpful paradigmAgent technology is applicable in different fields within UTC. The ones most important mentioning are: information agents, agents for traffic simulation and traffic control. Currently, most applications of intelligent agents are information agents. They collect information via a network. With special designed agents user specific information can be provided. In urban traffic these intelligent agents are useable in delivering information about weather, traffic jams, public transport, route closures, best routes, etc. to the user via a Personal Travel Assistant. Agent technology can also be used for aggregating data for further distribution. Agents and multi agent systems are capable of simulating complex systems for traffic simulation. These systems often use one agent for every traffic participant (in a similar way as object oriented programs often use objects). The application of agents in (Urban) Traffic Control is the one that has our prime interest. Here we ultimately want to use agents for pro-active traffic light control with on-line optimisation. Signal plans then will be determined based on predicted and measured detector data and will be tuned with adjoining agents. The most promising aspects of agent technology, the flexibility and pro-active behaviour, give UTC the possibility of better anticipation of traffic. Current UTC is not that flexible, it is unable to adjust itself if situations change and can't handle un-programmed situations. Agent technology can also be implemented on several different control layers. This gives the advantage of being close to current UTC while leaving considerable freedom at the lower (intersection) level. Designing agent based urban traffic control systemsThe ideal system that we strive for is a traffic control system that is based on actuated traffic controllers and is able to pro actively handle traffic situations and handling the different, sometimes conflicting, aims of traffic controllers. The proposed use of the concept of agents in this research is experimental.Assumptions and considerations on agent based urban traffic controlThere are three aspects where agent based traffic control and -management can improve current state of the art UTC systems:- Adaptability. Intelligent agents are able to adapt its behaviour and can learn from earlier situations.- Communication. Communication makes it possible for agents to co-operate and tune signal plans.- Pro-active behaviour. Due to the pro active behaviour traffic control systems are able to plan ahead.To be acceptable as replacement unit for current traffic control units, the system should perform the same or better than current systems. The agent based UTC will require on-line and pro-active reaction on changing traffic patterns. An agent based UTC should be demand responsive as well as adaptive during all stages and times. New methods for traffic control and traffic prediction should be developed as current ones do not suffice and cannot be used in agent technology. The adaptability can also be divided in several different time scales where the system may need to handle in a different way (Rogier, 1999):- gradual changes due to changing traffic volumes over a longer period of time,- abrupt changes due to changing traffic volumes over a longer period of time,- abrupt, temporal, changes due to changing traffic volumes over a short period of time,- abrupt, temporal, changes due to prioritised traffic over a short period of time One way of handling the balance between performance and complexity is the use of a hierarchical system layout. We propose a hierarchy of agents where every agent is responsible for its own optimal solution, but may not only be influenced by adjoining agents but also via higher level agents. These agents have the task of solving conflicts between lower level agents that they can't solve. This represents current traffic control implementations and idea's. One final aspect to be mentioned is the robustness of agent based systems (if all communication fails the agent runs on, if the agent fails a fixed program can be executed.To be able to keep our first urban traffic control model as simple as possible we have made the following assumptions: we limit ourselves to inner city traffic control (road segments, intersections, corridors), we handle only controlled intersections with detectors (intensity and speed) at all road segments, we only handle cars and we use simple rule bases for knowledge representation.Types of agents in urban intersection controlAs we divide the system in several, recognisable, parts we define the following 4 types of agents:- Roads are represented by special road segment agents (RSA),- Controlled intersections are represented by intersection agents (ITSA),- For specific, defined, areas there is an area agent (higher level),- For specific routes there can be route agents, that spans several adjoining road segments (higher level).We have not chosen for one agent per signal. This may result in a more simple solution but available traffic control programs do not fit in that kind of agent. We deliberately choose a more complex agent to be able to use standard traffic control design algorithms and programs. The idea still is the optimisation on a local level (intersection), but with local and global control. Therefor we use area agents and route agents. All communication takes place between neighbouring agents and upper and lower level ones.Design of our agent based systemThe essence of a, demand responsive and pro-active agent based UTC consists of several ITSA's (InTerSection Agent).,some authority agents (area and route agents) and optional Road Segment Agents (RSA). The ITSA makes decisions on how to control its intersection based on its goals, capability, knowledge, perception and data. When necessary an agent can request for additional information or receive other goals or orders from its authority agent(s).For a specific ITSA, implemented to serve as an urban traffic control agent, the following actions are incorporated (Roozemond, 1998):- data collection / distribution (via RSA - information on the current state of traffic; from / to other ITSA's - on other adjoining signalised intersections);- analysis (with an accurate model of the surrounds and knowing the traffic and traffic control rules define current trend; detect current traffic problems);- calculation (calculate the next, optimal, cycle mathematically correct);- decision making (with other agent deciding what to use for next cycle; handle current traffic problems);- control (operate the signals according to cycle plan).In figure 1 a more specific example of a simplified, agent based, UTC system is given. Here we have a route agent controlling several intersection agents, which in turn manage their intersection controls helped by RSA's. The ITSA is the agent that controls and operates one specific intersection of which it is completely informed. All ITSA's have direct communication with neighbouring ITSA's, RSA's and all its traffic lights. Here we use the agent technology to implement a distributed planning algorit hm. The route agents’ tasks are controlling, co-ordinating and leading the ITSA’s towards a more global optimum. Using all available information the ITSA (re)calculates the next, most optimal, states and control strategy and operates the traffic signals accordingly. The ITSA can directly influence the control strategy of their intersection(s) and is able to get insight into on-coming trafficThe internals of the ITSA modelTraffic dependent intersection control normally works in a fast loop. The detectordata is fed into the control algorithm. Based upon predetermined rules a control strategy is chosen and the signals are operated accordingly. In this research we suggest the introduction of an extra, slow, loop where rules and parameters of a prediction- model can be changed by a higher order meta-model.ITSA modelThe internals of an ITSA consists of several agents. For a better overview of the internal ITSA model-agents and agent based functions see figure 2. Data collection is partly placed at the RSA's and partly placed in the ITSA's. The needed data is collected from different sources, but mainly via detectors. The data is stored locally and may be transmitted to other agents. The actual operation of the traffic signals is left to an ITSA-controller agent. The central part of the ITSA, acts as a control strategy agent. That agent can operate several control strategies, such as anti-blocking and public transport priority strategies. The control strategy agent uses the estimates of the prediction model agent which estimates the states in the near future. The ITSA-prediction model agent estimates the states in the near future. The prediction model agent gets its data related to intersection and road segments - as an agent that ‘knows’ the forecasting equations, actual traffic conditions and constraints - and future traffic situations can be calculated by way of an inference engine and it’s knowledge and data base. On-line optimisation only works if there is sufficient quality in traffic predictions, a good choice is made regarding the performance indicators and an effective way is found to handle one-time occurrences (Rogier, 1999).Prediction modelWe hope to include pro-activeness via specific prediction model agents with a task of predicting future traffic conditions. The prediction models are extremely important for the development of pro active traffic control. The proposed ITSA-prediction model agent estimates the states of the traffic in the near future via its own prediction model. The prediction meta-model compares the accuracy of the predictions with current traffic and will adjust the prediction parameters if the predictions were insufficient or not accurate. The prediction model agent is fed by several inputs: vehicle detection system, relevant road conditions, control strategies, important data on this intersection and its traffic condition, communication with ITSA’s of nearby intersections and higher level agents. The agent itself has a rule-base, forecasting equations, knows constraints regarding specific intersections and gets insight into current (traffic) conditions. With these data future traffic situations should be calculated by its internal traffic forecasting model. The predicted forecast is valid for a limited time. Research has shown that models using historic, up-stream and current link traffic give the best results (Hobeika & Kim, 1994).Control strategy modelThe prediction of the prediction model is used in the control strategy planning phase. We have also included a performance indicating agent, necessary to update thecontrol parameters in the slower loop. The control strategy agent uses the estimates of the prediction model agent to calculate the most optimal control strategy to pro-act on the forecasts of the prediction model agent, checks with other adjoining agents its proposed traffic control schema and then plans the signal control strategy The communication schema is based on direct agent to agent communication via a network link. The needed negotiation finds place via a direct link and should take the global perspective into consideration. Specific negotiation rules still have to be developed. Some traffic regulation rules and data has to be fed into the system initially. Data on average flow on the links is gained by the system during run-time. In the near future computer based programs will be able to do, parts of, these kind of calculus automatically. For real-time control the same basic computer programs, with some artificial knowledge, will be used. Detectors are needed to give information about queues and number of vehicles. The arrival times can also be given by the RSA so that green on demand is automatically covered.Conclusions and future workAdaptive signal control systems that are able to optimise and adjust the signal settings are able to improve the vehicular throughput and minimise delay through appropriate response to changes in the measured demand patterns. With the introduction of two un-coupled feed back loops, whether agent technology is used or not, a pro-active theory of traffic control can be met. There are several aspects still unresearched. The first thing we are going to do is to build a prototype system of a single intersection to see if the given claims of adaptability and pro activeness can be realised. A working prototype of such system should give appropriate evidence on the usability of agent based control systems. There are three other major subjects to be researched in depth; namely self adjustable control schema's, on-line optimisation of complex systems and getting good prediction models. For urban traffic control we need to develop self adjustable control schemes that can deal with dynamic and actuated data. For the optimisation we need mathematical programming methodologies capable of real-time on-line operation. In arterial and agent based systems this subject becomes complex due to several different, continuously changing, weights and different goals of the different ITSA's and due to the need for co-ordination and synchronisation. The research towards realising real-time on-line prediction models needs to be developed in compliance with agent based technology. The pro-active and re-active nature of agents and the double loop control schema seems to be a helpful paradigm in intelligent traffic management and control. Further research and simulated tests on a control strategy, based on intelligent autonomous agents, is necessary to provide appropriate evidence on the usability of agent-based control systems.代理控制交通灯作者：Danko A. Roozemond，Jan L.H. Rogier出处：Delft University of Technology前言（城市）交通控制系统的好坏决定于系统控制模式和实际交通流量模式是否相符。

毕业设计（论文）外文文献翻译文献、资料中文题目：单一的交通流模式识别研究交叉口智能信号控制文献、资料英文题目：文献、资料来源：文献、资料发表（出版）日期：院（部）：专业：班级：姓名：学号：指导教师：翻译日期： 2017.02.14Study on Traffic Flow Patterns Identification of Single Intersection Intelligent Signal Control单一的交通流模式识别研究交叉口智能信号控制AbstractTo improve the level of intersection traffic management, we research traffic flow patterns of intersections. Based on Fuzzy C-Means clustering algorithm, this paper finished quality evaluating on cluster center value and optimizing the clustering processthrough combining with comprehensive evaluation method of fuzzy clustering quality. Based on above contents we proposed aprocess to recognize intersection traffic flow pattern based on fuzzy c-means clustering. After analyzing example like intersectionof West Beijing Road and Xi Kang Road in Nanjing, we obtained this intersection traffic flow can be divided into high peak,evening peak, flat peak in morning, afternoon, and noon peak 5 modes from field data. Then we simulated and analyzed the delayto verify the effectiveness and practicality of the method.摘要为了提高交叉口交通管理水平，我们研究了交叉口的交通流模式。

智能城市交通系统外文翻译文献(文档含中英文对照即英文原文和中文翻译)A Multiagent System for Optimizing Urban TrafficJohn France and Ali A. GhorbaniFaculty of Computer ScienceUniversity of New BrunswickFredericton, NB, E3B 5A3, CanadaAbstractFor the purposes of managing an urban traffic system, a hierarchical multiagent system that consists of several locally operating agents each representing an intersection of a traffic system is proposed. Local Traffic Agents (LTAs) are concerned with the optimal performance of their assigned intersection; however, the resulting traffic light patterns may result in the failure of the system when examined at a global level. Therefore, supervision is required and achieved with the use of a Coordinator Traffic Agent (CTA).A CTA provides a means by which the optimal local light pattern can be compared against the global concerns. The pattern can then be slightly modified to accommodate the global environment, while maintaining the local concerns of the intersection.Functionality of the proposed system is examined using two traffic scenarios: traffic accident and morning rush hour. For both scenarios, the proposed multiagent system efficiently managed the gradual congestion of the traffic.1 IntroductionThe 20th century witnessed the worldwide adoption of the automobile as a primary mode of transportation. Coupled with an expanding population, present-day traffic networks are unable to efficiently handle the daily movements of traffic through urban areas. Improvements to road networks are often confined by the boundaries of existing structures. Therefore, the primary focus should be to improve traffic flow without changing the layout or structure of the existing roadways. Any solution to traffic problem must handle three basic criteria, including: dynamically changing traffic patterns, occurrence of unpredictable events, and a non-finite based traffic environment [2]. Multiagent systems provide possible solutions to this problem, while meeting all necessary criteria. Agents are expected to work within a real-time, non-terminating environment. As well, agents can handle dynamically occurring events and may posses several processes to recognize and handle a variety of traffic patterns [3, 5].Although several approaches to developing a multiagent traffic system have been studied, each stresses the importance of finding a balance between the desires of the local optimum against a maintained average at the global level [4]. Unfortunately, systems developed to only examine and optimize local events do not guarantee a global balance[6]. However, local agents are fully capable of determining their own local optimum. Therefore, a more powerful approach involves the creation of a hierarchical structure in which a higher-level agent monitors the local agents, and is able to modify the local optimum to better suit the global concerns [7].The remainder of this paper is organized as follows. Section 2 examines the problems of urban traffic. The design of a hierarchical multiagent model is given in Section 3. The experimental results are presented in Section 4. Finally, the conclusions of the present study are summarized in Section 5.2 Urban Traffic CongestionImprovements to urban traffic congestion must focus on reducing internal bottlenecks to the network, rather than replacing the network itself. Of primary concern is the optimization of the traffic lights, which regulate the movement of traffic through the various intersections within the environment. At present, traffic lights may possess sensors to provide basic information relating to their immediate environment. This includes road and clock sensors, measuring the presence and density of traffic and providing the time of day to the traffic light.A solution to the urban traffic problem using agents is to simply replace all decision-making objects within the system by a corresponding agent. Even the most basic system will consist of several agents, leading to the creation of a multiagent environment. In this case, the traffic environment is broken down into its fundamental components, with one agent for each of the traffic lights within the system. To maintain organization and cooperation between the Local Traffic Agents (LTA), a Coordinator Traffic Agent (CTA) exists to monitor global concerns and maintain order.3 Hierarchical Multiagent Model for Urban TrafficTo achieve a balance between the local and global aspects of an urban traffic system, a multiagent system based on a hierarchical architecture is proposed. LTAs and CTAs make up the fundamental levels of the hierarchy, in which the LTAs meet the needs of the specific intersection, and the CTAs determine if the chosen patterns of a LTA are suited to meet any global concerns. A solitary Global Traffic Agent (GTA) may exist for networks of sufficient size, and an Information Traffic Agent (ITA) provides a central location for the storage of all shared information within the system. For each agent, the variables necessary to organize and maintain the hierarchy are listed.The development of this system, in which several LTAs work under the guidance of a single CTA, represents the backbone to a hierarchical structure of agents within the system. The CTA provides the bonds between itself and the LTAs of the system, requiring that the CTA store a list of the neighboring intersections for each of the LTAs. However, the computational capabilities of a single CTA are limited, and a road network of sufficient size may require the use of multiple CTAs to handle all of the LTAs within the system. In this circumstance, the network will be subdivided into regions controlled by a single CTA, with a top-level Global Traffic Agent (GTA) linking the CTAs together. The GTA is an optional agent, existing only if the network is sufficiently large that it is required.A LTA interacts at a global level by sending a message containing the calculated optimal local light pattern to its supervising CTA. The CTA will find the appropriate neighboring intersections, and then determine what the global optimum for the handled LTA will be. To calculate the global optimum, the CTA will require all information relating to each of the neighboring intersection. The CTA will request the information from the ITA by providing a list of the intersections the CTA is concerned with. Once this information is retrieved, a CTA calculates the global optimum and determines if a variance exists between the local and global traffic light patterns. If a significant difference is found, a balance between the local and global optimums must be negotiated, and then returned to the LTA.4 ImplementationThe proposed urban traffic multiagent system has been implemented using the JACK Development Environment, utilizing JACK Intelligent Agents TM.JACK uses the Belief Desire Intention (BDI) model. Under this framework,“the agent pursues its given goals (desires), adopting appropriate plans (intentions) according to its current set of data (beliefs) about the state of the world.”[1]. Agents created under the JACK environment are event-driven, and can respond to internal or external events occurring within the systemThe first phase of implementing the multiagent system involves the creation of LTAs. Each ofthese agents are tailored to meet the requirements of its corresponding intersection.For the purposes of this project, the traffic network consists of six intersections. Each intersection consists of two roads crossing over one another. Each approaching road posses two lanes, a left-turning lane, and a straight/rightturning lane.The decision-making capabilities of the LTAs is developed in the second phase. The first round of decisions by a LTA are concerned with finding the local optimum, with no consideration for neighboring intersections. A basic expert system divides the sensor inputs into a corresponding light pattern. The resulting light pattern consists of an eight-element array, which can be broken down into two elements for each of the North, East, South and West directions.Odd elements of the array (zero is the first index) specify the duration of the advanced green state for each of the appropriate directions, while even elements indicate the time of the straight/right-turning lanes. This light pattern is always in the same format, and once calculated, stored by the LTA. The values contained within the array consist of strings, indicating the duration of the traffic light. The values of the strings are as follows:Red: Red light, lanes remain in a stopped state.Short: Green light, most frequently occurring, 30-seconds in duration for straight directions, 15 seconds for leftturning lanes.Medium: Green light, often for above average traffic densities,45-seconds in duration for straight directions, 25 seconds for left-turning lanesShort: Green light, indicating a high traffic density, 60-seconds in duration for straight directions, 35 seconds for left-turning lanes.Once the optimal local traffic light pattern is calculated,the LTA sends a message event to the CTA. The traffic light pattern is passed to the CTA, allowing the CTA to adjust the LTA’s ligh t pattern to better meet any global concerns. Stored within the CTA is a vector of neighbors for each LTA within the system. When a CTA receives a message event from a LTA, the CTA gathers all information relating to the neighbors of the currently handled LTA from the ITA. The CTA will use this information within its own expert system, comparing the local optimum light pattern against the current densities of the neighboring intersections. If a significant difference is found between the local optimum and the essence of the global optimum, the traffic light pattern to be implemented is altered to reduce the difference between the two optimums. The new traffic light pattern is returned to the LTA for implementation within the traffic light.4.1 ExperimentsThis sections presents some of the experiments carried out for two fixed state scenarios. In each experiment, a list of variables is provided to initialize the current state of the environment. Once the state of the environment is established, each LTA goes through the process of changing the state of their traffic light to accommodate the other direction. The resulting traffic light pattern for each intersection is recorded, and the number of vehicles passing through the intersection, N, in the available time indicated by the traffic light pattern is calculated as N = T/(α+ε)where αandεrepresent the ideal amount of time required for a vehicle to pass through a traffic intersection and the latency increase to the ideal length of time due to unexpected events, respectively.An advanced form of this calculation would allow the latency value of _ to increase by a constant factor for each additional segment of the waiting vehicles. This can be demonstrated by using βto represent each of the latency groups, imposing a maximum number of vehicles that exist within each latency group. Let the number of vehicles found in latency group k is calculated as,where tβi denotes the amount of time used by the latency group βi The total number of vehicles that could then pass through the intersection would be calculated as N = β1 +β2 + ···+ βm, where m represent the number of latency groups that can make it through the traffic light.In this simulation we set α= 2 and ε= 1. A limit of three was imposed on the value of β0, while no limit was imposed on β1. These values were chosen for simplicity, and the precision in which the three possible values of T could be divided.To display the traffic density of the network, a grayscale image representing the density values within the environment is used (see Figure 1). Each lane of the traffic network is covered with an appropriate grayscale image.Figure 1. Initial densities prior to accident.Figure 2. Densities after six cycles.4.1.1 Traffic Accident ScenarioThe traffic accident scenario involves the occurrence of a traffic accident in the upper-right intersection of the network(see Figure 1).The occurrence of the accident results in the intersection at the upper-right to force all traffic tostop. This is done by implementing an all red traffic light pattern at the intersection faced with the traffic accident. The traffic light patterns of the adjacent intersections (2 and 6), remove their green states for the east and north directions, respectively. Although traffic can still move in all other available directions, those vehicles planning to head towards the stopped directions are forced to wait at the intersection. This results in a gradual increase to the traffic density at the intersections adjacent to the accident. Figure 2 shows the densities after 6 cycles.As the level of congestion increases at intersections 2 and 6, eventually their density values reach a point that leads to the CTA reducing the length of time that the other intersections (1 and 5) allow traffic to proceed. This results in a decrease to the overall congestion at intersections 2 and 6. Although slowed down, the density values will eventually reach their maximum level, at which time the totally congested event occurs. This forces intersections 1 and 5 to stop allowing traffic to move towards intersections 2 and 6. By the 8th cycle, the traffic accident is cleared up. Figure 3Figure 3. Densities five cycles after the accident is cleared up.shows the traffic densities 5 cycles after the accident is completely cleared up.4.1.2 Morning Rush Hour ScenarioTo initialize the morning rush hour scenario, the traffic densities of the network are set to low values. Over the next several cycles, a constant movement of incoming traffic is seen from the unknown directions, and from the suburbs located between intersections 1 and 2. With the addition of traffic from the suburbs, by the end of the second cycle, the east-bound lane of intersection 2 is heavily used.When both east-bound directions for intersections 2 and 5 are fully congested (see Figure 4), traffic heading in those directions will be forced to wait. This will allow the eastbound directions of intersection 2 and 5 to reduce their traffic densities, which will allow traffic to approach these lanes during the next cycle. Until one of the east-bound directions is de-congested, traffic will not be diverted in a north/south direction to travel around the problem.As rush hour passes and the inbound traffic density is reduced, the network is able to clear out the congested intersections. This is done from east to west, as the rush hour traffic is proceeding in an eastward direction.5 ConclusionsThe development of a hierarchical multiagent structure to manage an urban traffic system ispresented in this paper. To test the functionality of the proposed urban traffic multiagent system, two traffic scenarios are considered. For both scenarios (traffic accident and morning rush hour), the multiagent system efficiently managed the gradual congestion of the network. As one roadway becomes more congested, the duration of the traffic lights of neighboring intersections leading towards the congested area are reducedFigure 4. Densities after ten cycles.by the CTA. This redirection proves successful and results in the achievement of a global balance between the roadways of the network. However, when the traffic density continues to build, all roadways heading in a similar direction will eventually become equally congested. The urban traffic multiagent system handles this situation by halting all traffic heading in those directions. This allows the congested roadways to decrease their density values. Although this slows the network down, the congested traffic is handled in a more organized and controlled manner.6 AcknowledgmentsThis work was partially funded through grant RGPIN 227441-00 from the Natural Science and Engineering Research Council of Canada (NSERC) to Dr. Ali Ghorbani.References[1] Jack intelligent agents: User guide. 2002.[2] T. P. M. Baglietto and R. Zoppoli. Distributed-information neural control: The case of dynamic routing intraffic networks.IEEE Transactions on Neural Networks, 3(12), 2001.[3] P. Brumeister, A. Haddadi, and G. Matylis. Application of multiagent systems in traffic and transportation. IEEE Proc.-Soft. Eng., (144), 1997.[4] J. R. Campos and N. R. Jennings. Towards a social level characterization of socially responsible agents. IEEEProc.-Soft.Eng., (144), 1997.[5] K. R. Erol, R. Levy, and J. Wentworth. Application of agent technology to traffic simulation./advance/agent.html, Last access June 2002.[6] C. Ledoux. An urban traffic flow model integrating neural networks. Transportation Research, 5, 1997.[7] D. A. Roozemond. Using intelligent agents for pro-active, real-time urban intersection control. EuropeanJournal of Operational Research, 2001.多智能体系统优化城市交通约翰·法国和阿里 A.Ghorbani计算机科学学院新不伦瑞克大学弗雷德里克顿E3B 5A3 加拿大摘要管理城市交通系统而言，建议由的几个本地经营代理组成，每个代表交叉口的交通系统的分层多智能体系统。

交通信号外文翻译文献(文档含中英文对照即英文原文和中文翻译)Traffic signalsIn the United States alone ,some 250,000 intersections have traffic signals , which are defined as all power-operated traffic-control devices except flashers，signs，and markings for directing or warning motorists, cyclists，or pedestrians.Signals for vehicular，bicycle，and pedestrian control are ‘pretimed’where specific times intervals are allocated to the various traffic movements and as 'traffic actuated' where time intervals are controlled in whole or in part by traffic demand.Pretimed Traffic Signals'Pretimed' traffic signals are set to repeat regularly a given sequence of signal indications for stipulated time intervals through the 24-hr day. They have the advantages of having controllors of lower first cost and that they can be interconnected and coordinated to vehicles to move through a series of intersections with a minimum of stops and other delays.Also, their operation is unaffected by conditions brought on by unusual vehicle behavior such as forced stops，which，with some traffic-actuated signal installations may bring a traffic jam. Their disadvantage is that they cannot adjust to short-time variations in traffic flow and often hold vehicles from one direction when there is no traffic in the other. This results in inconvenience, and sometimes a decrease in capacity.‘Cycle length’the time required for a complete sequence of indications, ordinarily falls between 30 and 120s. Short cycle lengths are to be preferred, as the delay to standing vehicles is reduced. With short cycles, however a relatively high percentage of the total time is consumed in clearing the intersection and starting each succeeding movement. As cycle length increases, the percentage of time lost from these causes decreases. With high volumes of traffic, it may be necessary to increase the cycle length to gain added capacity.Each traffic lane of a normal signalized intersection can pass roughly one vehicle each 2.1s of green light. The yellow (caution) interval following each green period is usually between 3 and 6s，depending on street width，the needs of pedestrians, and vehicle approach speed. To determine an approximate cycle division, it is common practice to make short traffic counts during the peak period. Simple computations give the number of vehicles to be accommodated during each signal indication and the minimum green time required to pass them. With modern control equipment, it is possible to change the cycle length and division several times a day, or go to flashing indications to fit the traffic pattern better.At many intersections，signals must be timed to accommodate pedestrian movements. The Manual recommends that the minimum total time allowed be an initial interval of 4 to 7s for pedestrians to start plus walking time computed at 4 ft/s (1. 2m/s). With separate pedestrian indicators，the WALK indication(lunar white) covers the first of these intervals, and flashing DON'T WALK (Portland orange ) the remainder. The WALK signal flashes when there are possible conflicts with vehicles and is steady when there are none. Steady DON'T WALK tells the pedestrian not to proceed.If pedestrian control is solely by the vehicle signals，problems develop if the intersection is wide, since the yellow clearance interval will have to be considerably longer than the 3 to 5s needed by vehicles. This will reduce intersection capacity and may call for a longer cycle time. On wide streets having a median at least 6 ft (1. 8m)wide，pedestrians may be stopped there. A separate pedestrian signal activator must be placed on this median if pedestrian pushbuttons are incorporated into the overall control system.Coordinated MovementFixed-time traffic signals along a street or within an area usually are coordinated to permit compact groups of vehicles called `platoons’to move along together without stopping. Under normal traffic volumes，properly coordinated signals at intervals variously estimated from 2500 ft (0. 76km)to more than a mile (1. 6km) are very effective in producing a smooth flow of traffic. On the other hand，when a street is loaded to capacity，coordination of signals is generally ineffective in producing smooth traffic flow.Four systems of coordination-simultaneous, alternate，limited progressive, and flexible progressive-have developed over time. The simultaneous system made all color indications on a given street alike at the same time .It produced high vehicle speeds between stops but low overall speed. Because of this and other faults，it is seldom used today.The alternate system has all signals change their indication at the same time，but adjacent signals or adjacent groups of signals on a given street show opposite colors. The alternate system works fairly well on a single street that has approximately equal block spacing. It also has been effective for controlling traffic in business districts several blocks on a said, but only when block lengths are approximately equal in both directions. With an areawide alternate system，green and red indications must be of approximately equal length. This cycle division is satisfactory where two major streets intersect but gives too much green time to minor streets crossing major arteries. Other criticisms are that at heavy traffic volumes the later section of the platoon of vehicles is forced to make additional stops，and that adjustments to changing traffic conditions are difficult.The simple progressive system retains a common cycle length but provides 'go' indications separately at each intersection to match traffic progression. This permits continuous or nearly continuous flow of vehicle groups at a planned speed in at least one direction and discourages speeding between signals. Flashing lights may be substituted for normal signal indications when traffic becomes light.The flexible progressive system has a master controller mechanism that directs the controllers for the individual signals. This arrangement not only gives positive coordination between signals，but also makes predetermined changes in cycle length，cycle split，andoffsets at intervals during the day. For example，the cycle length of the entire system can be lengthened at peak hours to increase capacity and shortened at other times to decrease delays.Flashing indications can be substituted when normal signal control is not needed. Also the offsets in the timing of successive signals can be adjusted to favor heavy traffic movements, such as inbound in the morning and outbound in the evening. Again，changes in cycle division at particular intersections can be made. The traffic responsive system is an advanced flexible progressive system with the capacity to adjust signal settings to measured traffic volumes.Where traffic on heavy-volume or high-speed arteries must be interrupted for relatively light cross traffic，semi-traffic-actuated signals are sometimes used. For them，detectors are placed only on the minor street. The signal indication normally is green on the main road and red on the cross street. On actuation, the indications are reversed for an appropriate interval after which they return to the original colors.交通信号仅在美国，约250000十字路口有交通信号,这被定义为所有除了闪光灯、标志、和标记的用于指导或警告驾驶员、骑自行车的人和行人的电动交通控制装置,。

Agent controlled traffic lightsAuthor：Danko A. Roozemond，Jan L.H. RogierProvenance：Delft University of Technology IntroductionThe quality of (urban) traffic control systems is determined by the match between the control schema and the actual traffic patterns. If traffic patterns change, what they usually do, the effectiveness is determined by the way in which the system adapts to these changes. When this ability to adapt becomes an integral part of the traffic control unit it can react better to changes in traffic conditions. Adjusting a traffic control unit is a costly and timely affair if it involves human attention. The hypothesis is that it might offer additional benefit using self-evaluating and self-adjusting traffic control systems. There is already a market for an urban traffic control system that is able to react if the environment changes;the so called adaptive systems. "Real" adaptive systems will need pro-active calculated traffic information and cycle plans- based on these calculated traffic conditions- to be updated frequently.Our research of the usability of agent technology within traffic control can be split into two parts. First there is a theoretical part integrating agent technology and traffic control. The final stage of this research focuses on practical issues like implementation and performance. Here we present the concepts of agent technology applied to dynamic traffic control. Currently we are designing a layered model of an agent based urban traffic control system. We will elaborate on that in the last chapters.Adaptive urban traffic controlAdaptive signal control systems must have a capability to optimise the traffic flow by adjusting the traffic signals based on current traffic. All used traffic signal control methods are based on feed-back algorithms using traffic demand data -varying from years to a couple of minutes - in the past. Current adaptive systems often operate on the basis of adaptive green phases and flexible co-ordination in (sub)networks based on measured traffic conditions (e.g., UTOPIA-spot,SCOOT). These methods are still not optimal where traffic demand changes rapidly within a short time interval. The basic premise is that existing signal plan generation tools make rational decisions about signal plans under varying conditions; but almost none of the current available tools behave pro-actively or have meta-rules that may change behaviour of the controller incorporated into the system. The next logical step for traffic control is the inclusion of these meta-rules and pro active and goal-oriented behaviour. The key aspects of improved control, for which contributions from artificial intelligence and artificial intelligent agents can be expected, include the capability of dealing with conflicting objectives; the capability of making pro-active decisions on the basis of temporal analysis; the ability of managing, learning, self adjusting and responding to non-recurrent and unexpected events (Ambrosino et al.., 1994).What are intelligent agentsAgent technology is a new concept within the artificial intelligence (AI). The agent paradigm in AI is based upon the notion of reactive, autonomous, internally-motivated entities that inhabit dynamic, not necessarily fully predictable environments (Weiss, 1999). Autonomy is the ability to function as an independent unit over an extended period of time, performing a variety of actions necessary to achieve pre-designated objectives while responding to stimuli produced by integrally contained sensors (Ziegler, 1990). Multi-Agent Systems can be characterised by the interaction of many agents trying to solve a variety of problems in a co-operative fashion. Besides AI, intelligent agents should have some additional attributes to solve problems by itself in real-time; understand information; have goals and intentions; draw distinctions between situations; generalise; synthesise new concepts and / or ideas; model the world they operate in and plan and predict consequences of actions and evaluate alternatives. The problem solving component of an intelligent agent can be a rule-based system but can also be a neural network or a fuzzy expert system. It may be obvious that finding a feasible solution is a necessity for an agent. Often local optima in decentralised systems, are not the global optimum. This problem is not easily solved. The solution has to be found by tailoring the interaction mechanism or to have a supervising agent co-ordinating the optimisation process of the other agents. Intelligent agents in UTC,a helpful paradigmAgent technology is applicable in different fields within UTC. The ones most important mentioning are: information agents, agents for traffic simulation and traffic control. Currently, most applications of intelligent agents are information agents. They collect information via a network. With special designed agents user specific information can be provided. In urban traffic these intelligent agents are useable in delivering information about weather, traffic jams, public transport, route closures, best routes, etc. to the user via a Personal Travel Assistant. Agent technology can also be used for aggregating data for further distribution. Agents and multi agent systems are capable of simulating complex systems for traffic simulation. These systems often use one agent for every traffic participant (in a similar way as object oriented programs often use objects). The application of agents in (Urban) Traffic Control is the one that has our prime interest. Here we ultimately want to use agents for pro-active traffic light control with on-line optimisation. Signal plans then will be determined based on predicted and measured detector data and will be tuned with adjoining agents. The most promising aspects of agent technology, the flexibility and pro-active behaviour, give UTC the possibility of better anticipation of traffic. Current UTC is not that flexible, it is unable to adjust itself if situations change and can't handle un-programmed situations. Agent technology can also be implemented on several different control layers. This gives the advantage of being close to current UTC while leaving considerable freedom at the lower (intersection) level. Designing agent based urban traffic control systemsThe ideal system that we strive for is a traffic control system that is based on actuated traffic controllers and is able to pro actively handle traffic situations and handling the different, sometimes conflicting, aims of traffic controllers. The proposed use of the concept of agents in this research is experimental.Assumptions and considerations on agent based urban traffic controlThere are three aspects where agent based traffic control and -management can improve current state of the art UTC systems:- Adaptability. Intelligent agents are able to adapt its behaviour and can learn from earlier situations.- Communication. Communication makes it possible for agents to co-operate and tune signal plans.- Pro-active behaviour. Due to the pro active behaviour traffic control systems are able to plan ahead.To be acceptable as replacement unit for current traffic control units, the system should perform the same or better than current systems. The agent based UTC will require on-line and pro-active reaction on changing traffic patterns. An agent based UTC should be demand responsive as well as adaptive during all stages and times. New methods for traffic control and traffic prediction should be developed as current ones do not suffice and cannot be used in agent technology. The adaptability can also be divided in several different time scales where the system may need to handle in a different way (Rogier, 1999):- gradual changes due to changing traffic volumes over a longer period of time,- abrupt changes due to changing traffic volumes over a longer period of time,- abrupt, temporal, changes due to changing traffic volumes over a short period of time,- abrupt, temporal, changes due to prioritised traffic over a short period of time One way of handling the balance between performance and complexity is the use of a hierarchical system layout. We propose a hierarchy of agents where every agent is responsible for its own optimal solution, but may not only be influenced by adjoining agents but also via higher level agents. These agents have the task of solving conflicts between lower level agents that they can't solve. This represents current traffic control implementations and idea's. One final aspect to be mentioned is the robustness of agent based systems (if all communication fails the agent runs on, if the agent fails a fixed program can be executed.To be able to keep our first urban traffic control model as simple as possible we have made the following assumptions: we limit ourselves to inner city traffic control (road segments, intersections, corridors), we handle only controlled intersections with detectors (intensity and speed) at all road segments, we only handle cars and we use simple rule bases for knowledge representation.Types of agents in urban intersection controlAs we divide the system in several, recognisable, parts we define the following 4 types of agents:- Roads are represented by special road segment agents (RSA),- Controlled intersections are represented by intersection agents (ITSA),- For specific, defined, areas there is an area agent (higher level),- For specific routes there can be route agents, that spans several adjoining road segments (higher level).We have not chosen for one agent per signal. This may result in a more simple solution but available traffic control programs do not fit in that kind of agent. We deliberately choose a more complex agent to be able to use standard traffic control design algorithms and programs. The idea still is the optimisation on a local level (intersection), but with local and global control. Therefor we use area agents and route agents. All communication takes place between neighbouring agents and upper and lower level ones.Design of our agent based systemThe essence of a, demand responsive and pro-active agent based UTC consists of several ITSA's (InTerSection Agent).,some authority agents (area and route agents) and optional Road Segment Agents (RSA). The ITSA makes decisions on how to control its intersection based on its goals, capability, knowledge, perception and data. When necessary an agent can request for additional information or receive other goals or orders from its authority agent(s).For a specific ITSA, implemented to serve as an urban traffic control agent, the following actions are incorporated (Roozemond, 1998):- data collection / distribution (via RSA - information on the current state of traffic; from / to other ITSA's - on other adjoining signalised intersections);- analysis (with an accurate model of the surrounds and knowing the traffic and traffic control rules define current trend; detect current traffic problems);- calculation (calculate the next, optimal, cycle mathematically correct);- decision making (with other agent deciding what to use for next cycle; handle current traffic problems);- control (operate the signals according to cycle plan).In figure 1 a more specific example of a simplified, agent based, UTC system is given. Here we have a route agent controlling several intersection agents, which in turn manage their intersection controls helped by RSA's. The ITSA is the agent that controls and operates one specific intersection of which it is completely informed. All ITSA's have direct communication with neighbouring ITSA's, RSA's and all its traffic lights. Here we use the agent technology to implement a distributed planning algori thm. The route agents’ tasks are controlling, co-ordinating and leading the ITSA’s towards a more global optimum. Using all available information the ITSA (re)calculates the next, most optimal, states and control strategy and operates the traffic signals accordingly. The ITSA can directly influence the control strategy of their intersection(s) and is able to get insight into on-coming trafficThe internals of the ITSA modelTraffic dependent intersection control normally works in a fast loop. The detectordata is fed into the control algorithm. Based upon predetermined rules a control strategy is chosen and the signals are operated accordingly. In this research we suggest the introduction of an extra, slow, loop where rules and parameters of a prediction- model can be changed by a higher order meta-model.ITSA modelThe internals of an ITSA consists of several agents. For a better overview of the internal ITSA model-agents and agent based functions see figure 2. Data collection is partly placed at the RSA's and partly placed in the ITSA's. The needed data is collected from different sources, but mainly via detectors. The data is stored locally and may be transmitted to other agents. The actual operation of the traffic signals is left to an ITSA-controller agent. The central part of the ITSA, acts as a control strategy agent. That agent can operate several control strategies, such as anti-blocking and public transport priority strategies. The control strategy agent uses the estimates of the prediction model agent which estimates the states in the near future. The ITSA-prediction model agent estimates the states in the near future. The prediction model agent gets its data related to intersection and road segments - as an agent that ‘knows’ the forecasting equation s, actual traffic conditions and constraints - and future traffic situations can be calculated by way of an inference engine and it’s knowledge and data base. On-line optimisation only works if there is sufficient quality in traffic predictions, a good choice is made regarding the performance indicators and an effective way is found to handle one-time occurrences (Rogier, 1999).Prediction modelWe hope to include pro-activeness via specific prediction model agents with a task of predicting future traffic conditions. The prediction models are extremely important for the development of pro active traffic control. The proposed ITSA-prediction model agent estimates the states of the traffic in the near future via its own prediction model. The prediction meta-model compares the accuracy of the predictions with current traffic and will adjust the prediction parameters if the predictions were insufficient or not accurate. The prediction model agent is fed by several inputs: vehicle detection system, relevant road conditions, control strategies, important data on this intersection and its traffic condition, communication with ITSA’s of nearby intersections and higher level agents. The agent itself has a rule-base, forecasting equations, knows constraints regarding specific intersections and gets insight into current (traffic) conditions. With these data future traffic situations should be calculated by its internal traffic forecasting model. The predicted forecast is valid for a limited time. Research has shown that models using historic, up-stream and current link traffic give the best results (Hobeika & Kim, 1994).Control strategy modelThe prediction of the prediction model is used in the control strategy planning phase. We have also included a performance indicating agent, necessary to update thecontrol parameters in the slower loop. The control strategy agent uses the estimates of the prediction model agent to calculate the most optimal control strategy to pro-act on the forecasts of the prediction model agent, checks with other adjoining agents its proposed traffic control schema and then plans the signal control strategy The communication schema is based on direct agent to agent communication via a network link. The needed negotiation finds place via a direct link and should take the global perspective into consideration. Specific negotiation rules still have to be developed. Some traffic regulation rules and data has to be fed into the system initially. Data on average flow on the links is gained by the system during run-time. In the near future computer based programs will be able to do, parts of, these kind of calculus automatically. For real-time control the same basic computer programs, with some artificial knowledge, will be used. Detectors are needed to give information about queues and number of vehicles. The arrival times can also be given by the RSA so that green on demand is automatically covered.Conclusions and future workAdaptive signal control systems that are able to optimise and adjust the signal settings are able to improve the vehicular throughput and minimise delay through appropriate response to changes in the measured demand patterns. With the introduction of two un-coupled feed back loops, whether agent technology is used or not, a pro-active theory of traffic control can be met. There are several aspects still unresearched. The first thing we are going to do is to build a prototype system of a single intersection to see if the given claims of adaptability and pro activeness can be realised. A working prototype of such system should give appropriate evidence on the usability of agent based control systems. There are three other major subjects to be researched in depth; namely self adjustable control schema's, on-line optimisation of complex systems and getting good prediction models. For urban traffic control we need to develop self adjustable control schemes that can deal with dynamic and actuated data. For the optimisation we need mathematical programming methodologies capable of real-time on-line operation. In arterial and agent based systems this subject becomes complex due to several different, continuously changing, weights and different goals of the different ITSA's and due to the need for co-ordination and synchronisation. The research towards realising real-time on-line prediction models needs to be developed in compliance with agent based technology. The pro-active and re-active nature of agents and the double loop control schema seems to be a helpful paradigm in intelligent traffic management and control. Further research and simulated tests on a control strategy, based on intelligent autonomous agents, is necessary to provide appropriate evidence on the usability of agent-based control systems.代理控制交通灯作者：Danko A. Roozemond，Jan L.H. Rogier出处：Delft University of Technology前言（城市）交通控制系统的好坏决定于系统控制模式和实际交通流量模式是否相符。