交通信号智能控制系统外文文献及翻译
智能交通系统中英文对照外文翻译文献
智能交通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)原文:Traffic Assignment Forecast Model Research in ITS IntroductionThe intelligent transportation system (ITS) develops rapidly along with the city sustainable development, the digital city construction and the development of transportation. One of the main functions of the ITS is to improve transportation environment and alleviate the transportation jam, the most effective method to gain the aim is to forecast the traffic volume of the local network and the important nodes exactly with GIS function of path analysis and correlation mathematic methods, and this will lead a better planning of the traffic network. Traffic assignment forecast is an important phase of traffic volume forecast. It will assign the forecasted traffic to every way in the traffic sector. If the traffic volume of certain road is too big, which would bring on traffic jam, planners must consider the adoption of new roads or improving existing roads to alleviate the traffic congestion situation. This study attempts to present an improved traffic assignment forecast model, MPCC, based on analyzing the advantages and disadvantages of classic traffic assignment forecast models, and test the validity of the improved model in practice.1 Analysis of classic models1.1 Shortcut traffic assignmentShortcut traffic assignment is a static traffic assignment method. In this method, the traffic load impact in the vehicles’ travel is not considered, and the traffic impedance (travel time) is a constant. The traffic volume of every origination-destination couple will be assigned to the shortcut between the origination and destination, while the traffic volume of other roads in this sector is null. This assignment method has the advantage of simple calculation; however, uneven distribution of the traffic volume is its obvious shortcoming. Using this assignment method, the assignment traffic volume will be concentrated on the shortcut, which isobviously not realistic. However, shortcut traffic assignment is the basis of all theother traffic assignment methods.1.2 Multi-ways probability assignmentIn reality, travelers always want to choose the shortcut to the destination, whichis called the shortcut factor; however, as the complexity of the traffic network, thepath chosen may not necessarily be the shortcut, which is called the random factor.Although every traveler hopes to follow the shortcut, there are some whose choice isnot the shortcut in fact. The shorter the path is, the greater the probability of beingchosen is; the longer the path is, the smaller the probability of being chosen is.Therefore, the multi-ways probability assignment model is guided by the LOGIT model:∑---=n j ii i F F p 1)exp()exp(θθ (1)Where i p is the probability of the path section i; i F is the travel time of thepath section i; θ is the transport decision parameter, which is calculated by the followprinciple: firstly, calculate the i p with different θ (from 0 to 1), then find the θwhich makes i p the most proximate to the actual i p .The shortcut factor and the random factor is considered in multi-ways probabilityassignment, therefore, the assignment result is more reasonable, but the relationshipbetween traffic impedance and traffic load and road capacity is not considered in thismethod, which leads to the assignment result is imprecise in more crowded trafficnetwork. We attempt to improve the accuracy through integrating the several elements above in one model-MPCC.2 Multi-ways probability and capacity constraint model2.1 Rational path aggregateIn order to make the improved model more reasonable in the application, theconcept of rational path aggregate has been proposed. The rational path aggregate,which is the foundation of MPCC model, constrains the calculation scope. Rationalpath aggregate refers to the aggregate of paths between starts and ends of the trafficsector, defined by inner nodes ascertained by the following rules: the distancebetween the next inner node and the start can not be shorter than the distance betweenthe current one and the start; at the same time, the distance between the next innernode and the end can not be longer than the distance between the current one and theend. The multi-ways probability assignment model will be only used in the rationalpath aggregate to assign the forecast traffic volume, and this will greatly enhance theapplicability of this model.2.2 Model assumption1) Traffic impedance is not a constant. It is decided by the vehicle characteristicand the current traffic situation.2) The traffic impedance which travelers estimate is random and imprecise.3) Every traveler chooses the path from respective rational path aggregate.Based on the assumptions above, we can use the MPCC model to assign thetraffic volume in the sector of origination-destination couples.2.3 Calculation of path traffic impedanceActually, travelers have different understanding to path traffic impedance, butgenerally, the travel cost, which is mainly made up of forecast travel time, travellength and forecast travel outlay, is considered the traffic impedance. Eq. (2) displaysthis relationship. a a a a F L T C γβα++= (2)Where a C is the traffic impedance of the path section a; a T is the forecast traveltime of the path section a; a L is the travel length of the path section a; a F is theforecast travel outlay of the path section a; α, β, γ are the weight value of that threeelements which impact the traffic impedance. For a certain path section, there aredifferent α, β and γ value for different vehicles. We can get the weighted average of α,β and γ of each path section from the statistic percent of each type of vehicle in thepath section.2.4 Chosen probability in MPCCActually, travelers always want to follow the best path (broad sense shortcut), butbecause of the impact of random factor, travelers just can choose the path which is ofthe smallest traffic impedance they estimate by themselves. It is the key point ofMPCC. According to the random utility theory of economics, if traffic impedance is considered as the negativeutility, the chosen probability rs p of origination-destinationpoints couple (r, s) should follow LOGIT model:∑---=n j jrs rs bC bC p 1)exp()exp( (3) where rs p is the chosen probability of the pathsection (r, s);rs C is the traffic impedance of the path sect-ion (r, s); j C is the trafficimpedance of each path section in the forecast traffic sector; b reflects the travelers’cognition to the traffic impedance of paths in the traffic sector, which has reverseratio to its deviation. If b → ∞ , the deviation of understanding extent of trafficimpedance approaches to 0. In this case, all the travelers will follow the path whichis of the smallest traffic impedance, which equals to the assignment results withShortcut Traffic Assignment. Contrarily, if b → 0, travelers ’ understanding error approaches infinity. In this case, the paths travelers choose are scattered. There is anobjection that b is of dimension in Eq.(3). Because the deviation of b should beknown before, it is difficult to determine the value of b. Therefore, Eq.(3) is improvedas follows:∑---=n j OD j OD rsrs C bC C bC p 1)exp()exp(，∑-=n j j OD C n C 11（4） Where OD C is the average of the traffic impedance of all the as-signed paths; bwhich is of no dimension, just has relationship to the rational path aggregate, ratherthan the traffic impedance. According to actual observation, the range of b which is anexperience value is generally between 3.00 to 4.00. For the more crowded cityinternal roads, b is normally between 3.00 and 3.50.2.5 Flow of MPCCMPCC model combines the idea of multi-ways probability assignment anditerative capacity constraint traffic assignment.Firstly, we can get the geometric information of the road network and OD trafficvolume from related data. Then we determine the rational path aggregate with themethod which is explained in Section 2.1.Secondly, we can calculate the traffic impedance of each path section with Eq.(2),Fig.1 Flowchart of MPCC which is expatiated in Section 2.3.Thirdly, on the foundation of the traffic impedance of each path section, we cancalculate the respective forecast traffic volume of every path section with improvedLOGIT model (Eq.(4)) in Section 2.4, which is the key point of MPCC.Fourthly, through the calculation processabove, we can get the chosen probability andforecast traffic volume of each path section, but itis not the end. We must recalculate the trafficimpedance again in the new traffic volumesituation. As is shown in Fig.1, because of theconsideration of the relationship between trafficimpedance and traffic load, the traffic impedanceand forecast assignment traffic volume of everypath will be continually amended. Using therelationship model between average speed andtraffic volume, we can calculate the travel timeand the traffic impedance of certain path sect-ionunder different traffic volume situation. For theroads with different technical levels, therelationship models between average speeds totraffic volume are as follows: 1) Highway: 1082.049.179AN V = (5) 2) Level 1 Roads: 11433.084.155AN V = (6) 3) Level 2 Roads: 66.091.057.112AN V = (7) 4) Level 3 Roads: 3.132.01.99AN V = (8) 5) Level 4 Roads: 0988.05.70A N V =(9) Where V is the average speed of the path section; A N is the traffic volume of thepath section.At the end, we can repeat assigning traffic volume of path sections with themethod in previous step, which is the idea of iterative capacity constraint assignment,until the traffic volume of every path section is stable.译文智能交通交通量分配预测模型介绍随着城市的可持续化发展、数字化城市的建设以及交通运输业的发展，智能交通系统（ITS）的发展越来越快。
智能交通信号灯毕业设计外文翻译
智能交通信号灯摘要：信号控制是一种必要的措施以确保的质量和安全,交通循环。
现在的信号控制的进一步发展具有极大的潜力来减少运行时间、车辆、事故成本和整车排放。
检测的发展和计算机技术改变了交通信号控制从定时开环规定自适应反馈控制。
目前的自适应控制方法,像英国、瑞典MOV A SOS)和英国(孤立的信号(area-wide又控制),采用数学优化与仿真技术来调整信号波动的时间观察到的交通流实时的。
优化是通过改变时间和周期长度的绿色的信号。
在area-wide交叉口控制偏移是之间也发生了变化。
已经开发为几种方法确定最优周期长度和最小延迟在十字路口,但基于不确定性和严格的交通信号控制的本质,全局最优是不可能找到的。
1．引文：由于越来越多的公众意识的环境影响道路交通许多当局现在所追求的政策来:，管理供求−拥挤，影响模式和路径选择−;贯彻“三个代表”重要思想,提高公共汽车−有轨电车和其他公共服务车辆;设施提供更好的、更安全,骑自行车和行人的道路使用者等脆弱;降低汽车排放−、噪声和视觉入侵;为所有道路改善安全−用户群。
在自适应交通信号控制的弹性增强的增加的数量在周期层叠的绿色阶段,从而使数学优化非常复杂和困难。
因为这个原因,自适应信号控制在大多数情况下不是建立在精确的优化上,而是建立在绿色的扩展原理。
在实践中,遵循的均匀性是最主要的交通信号控制安全的原因。
这一规定的限制的周期时间和相位的安排。
因此,在实践中是交通信号控制的针对性的解决方案和调整的基础上由交通规划者。
现代可编程信号控制器以大量的可调参数是非常适合这一过程。
对于好的结果,一个经验丰富的策划人和微调领域中是必要的。
模糊控制已经被证明是成功的,在这些问题中,精确的数学建模是困难的或不可能的,但一名有经验的人可以控制的工艺操作。
因此,交通信号控制是一种适合于任务特别为模糊控制。
事实上,最古老的文化之一的潜力的例子是一个模拟的模糊控制在一个inter-section交通信号控制的两个单向的街道。
智能交通信号控制中英文对照外文翻译文献
智能交通信号控制中英文对照外文翻译文献(文档含英文原文和中文翻译)原文：Intelligent Traffic Signal Control Using Wireless SensorNetworksVignesh.Viswanathan and Vigneshwar. SanthanamAbstract:The growing vehicle population in all developing and developed countries calls for a major change in the existing traffic signaling systems. The most widely used automated system uses simple timer based operation which is inefficient for non-uniform traffic. Advanced automated systems in testing use image processingtechniques or advanced communication systems in vehicles to communicate with signals and ask for routing. This might not be implementable in developing countries as they prove to be complex and expensive. The concept proposed in this paper involves use of wireless sensor networks to sense presence of traffic near junctions and hence route the traffic based on traffic density in the desired direction. This system does not require any system in vehicles so can be implemented in any traffic system easily. This system uses wireless sensor networks technology to sense vehicles and a microcontroller based routing algorithm for traffic management.Keywords:Intelligent traffic signals, intelligent routing, smart signals, wireless sensor networks.I. INTRODUCTIONThe traffic density is escalating at an alarming rate in developing countries which calls for the need of intelligent traffic signals to replace the conventional manual and timer based systems. Experimental systems in existence involve image processing based density identification for routing of traffic which might be inefficient in situations like fog, rain or dust. The other conceptual system which is based on interaction of vehicles with traffic signals and each other require hardware modification on each vehicle and cannot be practically implemented in countrieslike India which have almost 100 million vehicles on road [1]. The system proposed here involves localized traffic routing for each intersection based on wireless sensor networks. The proposed system has a central controller at every junction which receives data from tiny wireless sensor nodes placed on the road. The sensor nodes have sensors that can detect the presence of vehicle and the transmitter wirelessly transmits the traffic density to the central controller. The controller makes use of the proposed algorithm to find ways to regulate traffic efficiently.II. THE NEED FOR AN ALTERNATE SYSTEMT he most prevalent traffic signaling system in developing countries is the timer based system. This system involves a predefined time setting for each road at anintersection. While this might prove effective for light traffic, heavy traffic requires an adaptive system that will work based on the density of traffic on each road. The first system proposed for adaptive signaling was based on digital image processing techniques. This system works based on the captured visual input from the roads and processing them to find which road has dense traffic. This system fails during environmental interaction like rain or fog. Also this system in testing does not prove efficient. The advanced system in testing at Pittsburgh [2] involves signals communicating with each other and also with the vehicles. The proposed system does not require a network between signals and vehicles and is a standalone system at each intersection.III. THE PROPOSED SYSTEMThis paper presents the concept of intelligent traffic routing using wireless sensor networks. The primary elements of this system are the sensor nodes or motes consisting of sensors and a transmitter. The sensors interact with the physical environment while the transmitter pages the sensor’s data to the central controller. This system involves the 4 x 2 array of sensor nodes in each road. This signifies 4 levels of traffic and 2 lanes in each road. The sensors are ultrasonic or IR based optical sensors which transmits status based on presence of vehicle near it. The sensor nodes transmit at specified time intervals via ZigBee protocol to the central controller placed at every intersection. The controller receives the signal and computes which road and which lane has to be given green signal based on the density of traffic. The controller makes use of the discussed algorithm to perform the intelligent traffic routing.IV. COMPONENTS INVOLVED IN THE SYSTEMThe proposed system involves wireless sensor networks which are comprised of three basic components: the sensor nodes or motes, power source and a central controller. The motes in turn are comprised of Sensors and transceiver module. The sensors sense the vehicles at intersections and transceiver transmit the sensor’s data tothe central controller through a wireless medium. The Power source provides the power needed for the sensor nodes and is mostly regenerative. The central controller performs all the computations for the sensor networks. The controller receives the input from all sensors and processes simultaneously to make the required decisions.A.SensorsSensors are hardware devices that produce a measurable response to a change in a physical condition like temperature or pressure. Sensors measure physical data of the parameter to be monitored. The continual analog signal produced by the sensors is digitized by an analog-to-digital converter and sent to controllers for further processing. A sensor node should be small in size, consume extremely low energy, operate in high volumetric densities, be autonomous and operate unattended, and be adaptive to the environment. As wireless sensor nodes are typically very small electronic devices, they can only be equipped with a limited power source of less than 0.5-2 ampere-hour and 1.2-3.7 volts. Sensors are classified into three categories: passive Omni-directional sensors; passive narrow-beam sensors; and active sensors [3].The sensors are implemented in this system placed beneath the roads in an intersection or on the lane dividers on each road. The sensors are active obstacle detectors that detect the presence of vehicles in their vicinity. The sensors are set in four levels on each road signifying four levels of traffic from starting from the STOP line. The fourth level indicates high density traffic and signifies higher priority for the road to the controller. The sensors required for obstacle detection can be either ultrasonic or Infrared LASER based sensors for better higher efficiency.B. MotesA mote, also known as a sensor node is a node in a wireless sensor network that is capable of performing some processing, gathering sensory information and communicating with other connected nodes in the network. The main components of a sensor node are a microcontroller, transceiver, external memory, power source andone or more sensors [3].Fig. 1 Block Diagram of a MoteC. Need for MotesThe primary responsibility of a Mote is to collect information from the various distributed sensors in any area and to transmit the collected information to the central controller for processing. Any type of sensors can be incorporated with these Motes based on the requirements. It is a completely new paradigm for distributed sensing and it opens up a fascinating new way to look at sensor networks.D. Advantages of Motes●The core of a mote is a small, low-cost, low-power controller.●The controller monitors one or more sensors. It is easy to interface all sorts ofsensors, including sensors for temperature, light, sound, position, acceleration, vibration, stress, weight, pressure, humidity, etc. with the mote.●The controller connects to the central controller with a radio link. The mostcommon radio links allow a mote to transmit at a distance of about 3 to 61 meters.Power consumption, size and cost are the barriers to longer distances. Since a fundamental concept with motes is tiny size and associated tiny cost, small and low-power radios are normal.●As motes shrink in size and power consumption, it is possible to imagine solarpower or even something exotic like vibration power to keep them running. It ishard to imagine something as small and innocuous as a mote sparking a revolution, but that's exactly what they have done.Motes are also easy to program, either by using serial or Ethernet cable to connect to the programming board or by using Over the Air Programming (OTAP).Fig. 2 Block Diagram of the Proposed SystemE. TransceiversSensor nodes often make use of ISM band, which gives free radio, spectrum allocation and global availability. The possible choices of wireless transmission media are radio frequency (RF), optical communication and infrared. Lasers require less energy, but need line-of-sight for communication and are sensitive to atmospheric conditions. Infrared, like lasers, needs no antenna but it is limited in its broadcasting capacity. Radio frequency-based communication is the most relevant that fits most of the WSN applications. WSNs tend to use license-free communication frequencies: 173, 433, 868, and 915 MHz; and 2.4 GHz. The functionality of bothtransmitter and receiver are combined into a single deviceknown as a transceiver [3].To bring about uniqueness in transmitting and receiving toany particular device various protocols/algorithms are devised. The Motes are often are often provided with powerful transmitters and receivers collectively known as transceivers for better longrange operation and also toachieve better quality of transmission/reception in any environmental conditions.F. Power SourceT he sensor node consumes power for sensing, communicating and data processing. More energy is required for data communication than any other process. Power is stored either in batteries or capacitors. Batteries, both rechargeable and non-rechargeable, are the main source of power supply for sensor nodes. Current sensors are able to renew their energy from solar sources, temperature differences, or vibration. Two power saving policies used are Dynamic Power Management (DPM) and Dynamic V oltage Scaling (DVS). DPM conserves power by shutting down parts of the sensor node which are not currently used or active. A DVS scheme varies the power levels within the sensor node depending on the non-deterministic workload. By varying the voltage along with the frequency, it is possible to obtain quadratic reduction in power consumption.G. Tmote SkyTmote Sky is an ultra low power wireless module for use in sensor networks, monitoring applications, and rapid application prototyping. Tmote Sky leverages industry standards like USB and IEEE802.15.4 to interoperate seamlessly with other devices. By using industry standards, integrating humidity, temperature, and light sensors, and providing flexible interconnection with peripherals, Tmote Sky enables a wide range of mesh network applications [4]. The TMote is one of the most commonly used motes in wireless sensor technology. Any type of sensor can be used in combination with this type of mote.Tmote Sky features the Chipcon CC2420 radio for wireless communications. The CC2420 is an IEEE 802.15.4 compliant radio providing the PHY and some MAC functions [5]. With sensitivity exceeding the IEEE 802.15.4 specification and low power operation, the CC2420 provides reliable wireless communication. The CC2420 is highly configurable for many applications with the default radio settings providing IEEE 802.15.4 compliance. ZigBee specifications can be implemented using the built-in wireless transmitter in the Tmote Sky.Fig. 3 Tmote SkyH. Tmote Key Features•250kbps 2.4GHz IEEE 802.15.4 Chipcon Wireless Transceiver• Interoperability with other IEEE 802.15.4 devices.•8MHz Texas Instruments MSP430 microcontroller (10k RAM, 48k Flash Memory)• Integrated ADC, DAC, Supply V oltage Supervisor, and DMA Controller• Integrated onboard antenna with 50m range indoors / 125m range outdoors• Integrated Humidity, Temperat ure, and Light sensors• Ultra low current consumption• Fast wakeup from sleep (<6μs)• Hardware link-layer encryption and authentication• Programming and data collection via USB• 16-pin expansion support and optional SMA antenna connector• TinyOS support : mesh networking and communication implementation• Complies with FCC Part 15 and Industry Canada regulations• Environmentally friendly – complies with RoHS regulations [4].I. ZigBee Wireless TechnologyZigBee is a specification for a suite of high level communication protocols using small, low-power digital radios based on an IEEE 802.15.4 standard for personal area networks [6] [7]. ZigBee devices are often used in mesh network form to transmit data over longer distances, passing data through intermediate devices to reach more distant ones.This allows ZigBee networks to be formed ad-hoc, with nocentralized control or high-power transmitter/receiver able to reach all of the devices. Any ZigBee device can be tasked with running the network. ZigBee is targeted at applications that require a low data rate, long battery life, and secure networking. ZigBee has a defined rate of 250kbps, best suited for periodic or intermittent data or a single signal transmissionfrom a sensor or input device. Applications include wireless light switches, electrical meters with in-home-displays, traffic management systems, and other consumer and industrial equipment that requires short-range wireless transfer of data at relatively low rates. The technology defined by the ZigBee specification is intended to be simpler and less expensive than other WPANs, such as Bluetooth.J. Types of ZigBee DevicesZigBee devices are of three types:●ZigBee Coordinator (ZC): The most capable device, the Coordinator forms theroot of the network tree and might bridge to other networks. There is exactly one ZigBee Coordinator in each network since it is the device that started the network originally. It stores information about the network, including acting as the Trust Center & repository for security keys. The ZigBee Coordinator the central controller is in this system.●ZigBee Router (ZR): In addition to running an application function, a devicecan act as an intermediate router, passing on data from other devices.●ZigBee End Device (ZED): It contains just enough functionality to talk to theparent node. It cannot relay data from other devices. This relationship allows the node to be asleep a significant amount of the time thereby giving long battery life. A ZED requires the least amount of memory, and therefore can be less expensive to manufacture than a ZR or ZC.K. ZigBee ProtocolsThe protocols build on recent algorithmic research to automatically construct a low-speed ad-hoc network of nodes. In most large network instances, the network will be a cluster of clusters. It can also form a mesh or a single cluster. The current ZigBeeprotocols support beacon and non-beacon enabled networks. In non-beacon-enabled networks, an un-slotted CSMA/CA channel access mechanism is used. In this type of network, ZigBee Routers typically have their receivers continuously active, requiring a more robust power supply. However, this allows for heterogeneous networks in which some devices receive continuously, while others only transmit when an external stimulus is detected. In beacon-enabled networks, the special network nodes called ZigBee Routers transmit periodic beacons to confirm their presence to other network nodes. Nodes may sleep between beacons, thus lowering their duty cycle and extending their battery life. Beacon intervals depend on data rate; they may range from 15.36ms to 251.65824s at 250 kbps. In general, the ZigBee protocols minimize the time the radio is on, so as to reduce power use. In beaconing networks, nodes only need to be active while a beacon is being transmitted. In non-beacon-enabled networks, power consumption is decidedly asymmetrical: some devices are always active, while others spend most of their time sleeping.V. PROPOSED ALGORITHMA. Basic AlgorithmConsider a left side driving system (followed in UK, Australia, India, Malaysia and 72 other countries). This system can be modified for right side driving system (USA, Canada, UAE, Russia etc.) quite easily. Also consider a junction of four roads numbered as node 1, 2, 3 and 4 respectively. Traffic flows from each node to three other nodes with varied densities. Consider road 1 now given green signal in all directions.Fig. 4 Intersection Under Consideration1) Free left turn for all roads (free right for right side driving system).2) Check densities at all other nodes and retrieve data from strip sensors.3) Compare the data and compute the highest density.4) Allow the node with highest density for 60sec.5) Allowed node waits for 1 time slot for its turn again and the process is repeated from step 3.B. Advanced AlgorithmAssume road three is currently given green to all directions. All left turns are always free. No signals/sensors for left lane. Each road is given a time slot of maximum 60 seconds at a time. This time can be varied depending on the situation of implementation. Consider 4 levels of sensors Ax, Bx, Cx, Dx with A having highest priority and x representing roads 1 to 4. Also consider 3 lanes of traffic: Left (L), Middle (M) and Right(R) corresponding to the direction of traffic. Since leftturn is free, Left lanes do not require sensors. So sensors form 4x2 arrays with 4 levels of traffic and 2 lanes and are named MAx, RAx, MBx, RBx and so on and totally 32 sensors are employed.The following flow represents the sequence of operation done by the signal.1) Each sensor transmits the status periodically to the controller.2) Controller receives the signals and computes the following3) The sensors Ax from each road having highest priority are compared.4) If a single road has traffic till Ax, it is given green signal in the next time slot.5) If multiple roads have traffic till Ax, the road waiting for the longest duration is given the green.6) Once a road is given green, its waiting time is reset and its sensor status is neglected for that time slot7) If traffic in middle lane, green is given for straight direction, based on traffic, either right side neighbor is given green for right direction, of opposite road is give green for straight direction.8) If traffic in right lane, green is given for right, and based on traffic, left side neighbor is given green for straight or opposite is given green for right.9) Similar smart decisions are incorporated in the signal based on traffic density and directional traffic can be controlled.C. Implementation and RestrictionsThis system can be implemented by just placing the sensor nodes beneath the road or on lane divider and interfacing the central controller to the existing signal lights and connecting the sensor nodes to the controller via the proposed wireless protocol. The only restriction for implementing the system is taking the pedestrians into consideration. This has to be visualized for junctions with heavy traffic such as highway intersections and amount of pedestrians is very less. Also major intersections have underground or overhead footpaths to avoid interaction of pedestrians with heavy traffic.VI. CONCLUSIONThe above proposed system for automated traffic signal routing using Wireless Sensor Networks is advantageous to many existing systems. The wireless sensors nodes create a standalone system at each intersection making it easy to implement in the intersections having heavy density of vehicles. It is also cost inexpensive and does not require any system in the vehicles making it more practical than existing systems. The use of various systems of sensor nodes can be altered based on the requirement and any type of sensor can be used based on the feasibility of the location.ACKNOWLEDGMENTThe Authors would like to take this opportunity to thank Ms. P. Sasikala, Assistant Professor, ECE department, Sri Venkateswara College of Engineering, Sriperumbudur, who gave the basic insight into the field of Wireless Sensor Networks. We also thank Mrs. G. Padmavathi, Associate Professor, ECE department, Sri Venkateswara College of Engineering, Sriperumbudur, who with her expertise in the field of networks advised and guided on practicality of the concept and provided helpful ideas for future modifications. We also express our gratitude to Dr. S. Ganesh Vaidyanathan, Head of the department of ECE, Sri Venkateswara College of Engineering, Sriperumbudur, who supports us for every innovative project and encourages us “think beyond”for better use of technology. And finally we express our heart filled gratitude to Sri Venkateswara College of Engineering, which has been the knowledge house for our education and introduced us to the field of Engineering and supports us for working on various academic projects.基于无线传感器网络的智能交通信号控制摘要：在所有发展中国家和发达国家，不断增长的汽车数量将促使现有的交通信号系统发生重大变革。
智能交通灯控制系统_英文翻译
英文Because 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 orderly control. 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 AT89C51(1)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 infigure 1 for the AT89C51 pins allotment.Figure 1 the AT89C51 pins allotment(2)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 down(3)external clock driving characteristicsAbout 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) and appropriate 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 tiptop 1.27 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.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 on planar 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 the procedures 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 conductionband 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.。
智能交通中英文对照外文翻译文献
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。
智能城市交通系统外文翻译文献
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
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,。
交通信号智能控制系统外文文献及翻译.doc
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。
毕业设计论文外文文献翻译智能交通信号灯控制中英文对照
英语原文Intelligent Traffic Light Controlby Marco Wiering The topic I picked for our community project was traffic lights. In a community, people need stop signs and traffic lights to slow down drivers from going too fast. If there were no traffic lights or stop signs, people’s lives would be in danger from drivers going too fast.The urban traffic trends towards the saturation, the rate of increase of the road of big city far lags behind rate of increase of the car.The urban passenger traffic has already become the main part of city traffic day by day and it has used about 80% of the area of road of center district. With the increase of population and industry activity, people's traffic is more and more frequent, which is unavoidable. What means of transportation people adopt produces pressure completely different to city traffic. According to calculating, if it is 1 to adopt the area of road that the public transport needs, bike needs 5-7, car needs 15-25, even to walk is 3 times more than to take public transits. So only by building road can't solve the city traffic problem finally yet. Every large city of the world increases the traffic policy to the first place of the question.For example，according to calculating, when the automobile owning amount of Shanghai reaches 800,000 (outside cars count separately ), if it distributes still as now for example: center district accounts for great proportion, even when several loop-lines and arterial highways have been built up , the traffic cannot be improved more than before and the situation might be even worse. So the traffic policy Shanghai must adopt , or called traffic strategy is that have priority to develop public passenger traffic of city, narrow the scope of using of the bicycle progressively , control the scale of growth of the car traffic in the center district, limit the development of the motorcycle strictly.There are more municipals project under construction in big city. the influence on the traffic is greater.Municipal infrastructure construction is originally a good thing of alleviating the traffic, but in the course of constructing, it unavoidably influence the local traffic. Some road sections are blocked, some change into an one-way lane, thus the vehicle can only take a devious route . The construction makes the road very narrow, forming the bottleneck, which seriously influence the car flow.When having stop signs and traffic lights, people have a tendency to drive slower andlook out for people walking in the middle of streets. To put a traffic light or a stop sign in a community, it takes a lot of work and planning from the community and the city to put one in. It is not cheap to do it either. The community first needs to take a petition around to everyone in the community and have them sign so they can take it to the board when the next city council meeting is. A couple residents will present it to the board, and they will decide weather or not to put it in or not. If not put in a lot of residents might be mad and bad things could happened to that part of the city.When the planning of putting traffic lights and stop signs, you should look at the subdivision plan and figure out where all the buildings and schools are for the protection of students walking and riding home from school. In our plan that we have made, we will need traffic lights next to the school, so people will look out for the students going home. We will need a stop sign next to the park incase kids run out in the street. This will help the protection of the kids having fun. Will need a traffic light separating the mall and the store. This will be the busiest part of the town with people going to the mall and the store. And finally there will need to be a stop sign at the end of the streets so people don’t drive too fast and get in a big accident. If this is down everyone will be safe driving, walking, or riding their bikes.In putting in a traffic light, it takes a lot of planning and money to complete it. A traffic light cost around $40,000 to $125,000 and sometimes more depending on the location. If a business goes in and a traffic light needs to go in, the business or businesses will have to pay some money to pay for it to make sure everyone is safe going from and to that business. Also if there is too many accidents in one particular place in a city, a traffic light will go in to safe people from getting a severe accident and ending their life and maybe someone else’s.The reason I picked this part of our community development report was that traffic is a very important part of a city. If not for traffic lights and stop signs, people’s lives would be in danger every time they walked out their doors. People will be driving extremely fast and people will be hit just trying to have fun with their friends. So having traffic lights and stop signs this will prevent all this from happening.Traffic in a city is very much affected by traffic light controllers. When waiting for a traffic light, the driver looses time and the car uses fuel. Hence, reducing waiting times before traffic lights can save our European society billions of Euros annually. To make traffic light controllers more intelligent, we exploit the emergence of novel technologies such as communication networks and sensor networks, as well as the use of more sophisticated algorithms for setting traffic lights. Intelligent traffic light control does not only mean thattraffic lights are set in order to minimize waiting times of road users, but also that road users receive information about how to drive through a city in order to minimize their waiting times. This means that we are coping with a complex multi-agent system, where communication and coordination play essential roles. Our research has led to a novel system in which traffic light controllers and the behaviour of car drivers are optimized using machine-learning methods.Our idea of setting a traffic light is as follows. Suppose there are a number of cars with their destination address standing before a crossing. All cars communicate to the traffic light their specific place in the queue and their destination address. Now the traffic light has to decide which option (ie, which lanes are to be put on green) is optimal to minimize the long-term average waiting time until all cars have arrived at their destination address. The learning traffic light controllers solve this problem by estimating how long it would take for a car to arrive at its destination address (for which the car may need to pass many different traffic lights) when currently the light would be put on green, and how long it would take if the light would be put on red. The difference between the waiting time for red and the waiting time for green is the gain for the car. Now the traffic light controllers set the lights in such a way to maximize the average gain of all cars standing before the crossing. To estimate the waiting times, we use 'reinforcement learning' which keeps track of the waiting times of individual cars and uses a smart way to compute the long term average waiting times using dynamic programming algorithms. One nice feature is that the system is very fair; it never lets one car wait for a very long time, since then its gain of setting its own light to green becomes very large, and the optimal decision of the traffic light will set his light to green. Furthermore, since we estimate waiting times before traffic lights until the destination of the road user has been reached, the road user can use this information to choose to which next traffic light to go, thereby improving its driving behaviour through a city. Note that we solve the traffic light control problem by using a distributed multi-agent system, where cooperation and coordination are done by communication, learning, and voting mechanisms. To allow for green waves during extremely busy situations, we combine our algorithm with a special bucket algorithm which propagates gains from one traffic light to the next one, inducing stronger voting on the next traffic controller option.We have implemented the 'Green Light District', a traffic simulator in Java in which infrastructures can be edited easily by using the mouse, and different levels of road usage can be simulated. A large number of fixed and learning traffic light controllers have already been tested in the simulator and the resulting average waiting times of cars have been plotted and compared. The results indicate that the learning controllers can reduce average waiting timeswith at least 10% in semi-busy traffic situations, and even much more when high congestion of the traffic occurs.We are currently studying the behaviour of the learning traffic light controllers on many different infrastructures in our simulator. We are also planning to cooperate with other institutes and companies in the Netherlands to apply our system to real world traffic situations. For this, modern technologies such as communicating networks can be brought to use on a very large scale, making the necessary communication between road users and traffic lights possible.中文翻译：智能交通信号灯控制马克·威宁我所选择的社区项目主题是交通灯。
交通信号灯中英文对照外文翻译文献
中英文对照外文翻译(文档含英文原文和中文翻译)PLC-based design of traffic lightsAbstract: One kind of traffic light control system using programmable logic controller (PLC), via software control traffic lights run automatically. In the system, the original line is the program instead of the relay, programmable logic controller (PLC) system hardware and software resources to be fair use. Normal operation and emergency transport for a detailed description and from the East and West emergencies can be mutually linked. Traffic signal systems and two seven-segment digital display in the countdown order; also discussed in detail the wiring of the hardware and PLC ladder. Traffic lights at the crossroads of the remote monitoring system design configuration software MCGS, real-time monitoring of traffic lights,1greatly improving the reliability of data transmission. At the same time, we can configure the traffic lights to change the status of photographs.Keywords: switching power,supply protection, circuit system design1 IntroductionWith the social development and progress, traffic flow becomes increasingly important. On the one hand, too many crossroads, more and more vehicles, which are causing serious traffic congestion. On the other hand, in the limited time it is necessary to maintain the vehicle and pedestrian fast and safe. Therefore, one kind of traffic light control system design, can be used to display time countdown, with computer controlled real-time data. In addition, the configuration technology for real-time images that reflect the traffic lights, understand the historical crossroads of work to get traffic lights visualization. The system consists of host computer and a low computer. MCGS configuration is installed in the host computer is lower by the PLC control system.Normal traffic signal timing diagram shown in Figure 1. But there are some urgent matters, for example. There are a number of ambulances to transport patients to the hospital or to deal with a number of fire engines and fire. Fire engines and ambulances rushed to take precedence over other traffic scene. According to urban traffic control system, under normal circumstances, two control methods and urgency traffic control factors into account. This process can show 14 segment encoder. U.S. traffic lights instant record of the monitoring process.Emergency control signals to control traffic emergency switch. If there is no emergency lights all work, but when an emergency open. In this case, the car is urgent priority pass. Once the emergency vehicle passes, emergency switch off immediately. The green light in the same direction of the vehicle quickly flashes three times, followed by the normal operation. If you were from the north-south and east-west two emergency vehicles, traffic control systems can respond quickly came early, and then another.22 Traffic Control System DesignA Hardware designCP1H series PLC as controller, display the procedure should stop when the time series of abnormal system operation, the time will not be displayed. When the emergency procedures are completed time series, countdown display program should be reset. At 220 V AC system is used to control traffic lights, 24 V DC control segment encoder, Figure 2 shows the scheme Eastern time display. CP1H series programmable logic controller (PLC) is a simple controller, which consists of 24 inputs and sixteen outputs. Because the output to twenty In this system, an I / O module must be extended. Circuit is shown in Figure 2.3B Control Program DesignSix timers and two special normal open pulse is used in this system, the green light flashes for all north-south and east-timer and a special pulse; eight kinds of interlocking internal relay is used to implement the urgency and transmit pulse two directions to PLC, shown in the figure. Two SDEC instructions are used to display the countdown display the corresponding light. As an important part of the countdown display program, east and west of the green light reflected in the view 4 in these programs downloaded to the programmable logic controller (PLC), all the traffic lights running accuracy, urgency, and things can be interlocked from north to south strictly, all the lights can be set back to the urgency of passing state. Therefore, these control program is correct, simple.C Monitoring System4Computer system has two main functions: an output signal acquisition and display real-time status of the programmable logic controller (PLC) to control traffic lights, traffic lights. Another notification robot status and history of the state real-time curve by examining the history and alarm window.This monitoring system design and configuration software MCGS configuration is easy. The serial communication is implemented as follows.Data inspection methods: double endedSerial Communications Number: COM0 endedThe minimum sampling period: 200 msProgrammable Logic Controller (PLC) The parameters are defined as follows:5The minimum sampling period of the basic properties: 200 msThree read / write channel: X0, X1, X2Six read-only access (read U.S. traffic lights): Q0-Q5All channels must be connected to a variable defined in a real-time database access visits and other parameters to their default values. After a successful relationship, PLC and computer control system is able to change the color of the analog signal lights in the picture on the PC being collected data through the serial port.. In contrast, by changing the parameters of the host, the corresponding value is written to the PLC internal relay control, intersection traffic lights can be implemented. Experimental results show that the system is usually good enough and animation. Online monitoring system of traffic lights in Figure 5:63 ConclusionExperimental results show that the system is usually configured with enough good photos. This system simplifies the programmable logic controller (PLC) and the communication between the host computer using industrial configuration software development time is greatly reduced. In particular, more suitable for complex control systems. We can control the traffic lights by the PLC and MCGS configuration, replace the original relay control, improve the system's lifetime. At the same time, this method can be applied to control the motor and fluid levels. Remote control and configuration combined with the simulation, can be applied to similar control zone.4 References[1] Whitworth, Duller, Jones D I. Aerial video inspection of overhead power lines [J].PowerEngineering Journal,2001,15:25-32.[2] Jan Axelson, Lakeview Research-Serial port complete [D]. USA:1999:91-135.7基于PLC的交通灯控制系统设计摘要：一种交通灯控制系统采用可编程序控制器(PLC), 通过软件控制交通灯自动运行。
城市道路交叉口交通信号控制外文文献翻译
毕业设计附件外文文献翻译：原文+译文文献出处:Papageorgiou M. The study of urban road intersection traffic signal control [J]. Proceedings of the IEEE, 2016, 21(5): 213-227.原文The study of urban road intersection traffic signal controlPapageorgiou MAbstractUrban traffic is the lifeblood of a city's economic life, is a sign of measuring a city civilization progress, for the development of urban economy and the improvement of people's living standard plays a very important role.With the rapid development of economy and the speeding up of urbanization, the rapid growth of motor vehicle ownership, led to a sharp contradiction between traffic demand and road facilities. The fundamental way to solve the problem of urban traffic has two aspects: one is to strengthen the construction of transport infrastructure, raise the utilization rate of existing road network maximum; On the other hand is to strengthen the intelligent management of urban road network and optimization control.Keywords: road intersection, signal control, optimization method1 IntroductionBritain for the first time in the city of London in 1868 using gas color light signal, is used to manage the urban traffic, this way of signal according to a railway signal consists of a red light and green light, but was unable to promote the use of easy explosion. At this time most of the traffic police directing traffic by using portable lamp. In 1914, the United States in Cleveland Lunching install and use the manual operation of electric lighting lamp, six years later adopted by Japan, adopted by the British after ten years, this signal is set in the middle of the intersection signal tower, four directions has a diameter of 37.5 cm round red, green and yellow cast light. Many countries have adopted and then gradually improved. In 1926, the world's firstautomatic control of street intersection traffic signal success in the UK and start using, it adopts fixed cycle control method, then a more fixed cycle time control mode. In 1928, the United States developed vehicles inductive traffic lights, using the rubber tube pressure detector. A few years later adopted by the United Kingdom, Japan. Toronto, Canada, in 1963 into a computer model used IBM65O centrally coordinated induction control signal system, it marks the urban road traffic control system has entered a new stage. Since then, the United States, Britain, the former federal republic of Germany, Japan, Australia and other countries have built area traffic control system, digital electronic computer the system general traffic monitoring system of traffic control center. To the early eighty s, the world has a traffic control center of the city has more than 300.Traffic signal from manual to automatic, traffic signal by a fixed cycle to cycle, variable system control method by point to wire control and control, from no vehicle detector to a vehicle detector, traffic signal control experience for nearly a century history.2 City traffic signal control approach2.1 Due to intermittent signal controlThis kind of signal cycle length, phase, the green light time and conversion interval is determined beforehand. The signal cycle time in the form of constant running, each cycle of the cycle is long and phase constant. Rely on the equipment provided several scheduled timing scheme are available, and each automatic used interchangeably within the stipulated time in a day.2.2 Half an induction signal controlWill remain green until the signal control ensure the main trunk road in time the monitor on the detected vehicle arrives. The signal through an appropriate transformation after the interval, for once road show a green light, the green light time will last till the time all highway vehicles through the intersection or duration to specified maximum green. In the green wave signal system, the ration of trunk road green time must be limited within a predetermined time. The system cycle length and green time can be adjusted at any time according to need. When the main trunk road no vehicle to maintain a green light, in fact, assigned to the road of green light time,can make full use of all the "more" green time is assigned to the main road.2.3 The induction signals controlAll of the signal phase is controlled by transmission monitor. General provisions of minimum and maximum green time in every phase. In the form of this kind of control cycle length and green time can do a lot of change, according to the certain phase in the cycle can be arbitrarily choose to use, when the detector does not detect traffic, the moment of the phase can be cancelled automatically.2.4 Control modesRefers to the signal road intersection point control way unrelated to run independently. Point control mode is suitable for the distance between adjacent intersections, wire control without much effect or changes in each phase of traffic demand, the intersection of cycle length and split the independent control is more effective than wire control. Single intersection point control is one of the most basic ways of control, because of its simple equipment, investment, convenient maintenance; the province is still widely used. Technically, point control mode is divided into offline points control and on-line control.2.5 Wire control wayLine control method is also called the arterial coordination control, green wave control. In urban road network, if the traffic trunk on a few distance than control signal of the intersection of the unrelated, when setting for a single point of the intersection signal control, from out of the vehicle is likely to be in the downstream and upstream in the red light. The intersection between the fragmented isolated control modes unavoidably makes driving confusing, severe pollution. People developed a kind of based on trunk adjacent intersections of the concept of the green wave coordinate control strategy, i.e. the intersection signal in time to connect coordinated control, makes a convoy in a master with multiple intersection thousand lane, always at the beginning of the green light to reach the intersection, and therefore do not need to stop the intersection, so make sure the roads, to reduce the number of parking and traffic delays on the main line. Drive-by-wire main characteristic is to set common to several signal cycle length and relative phase difference. Wire control issuitable for the intersection spacing is small, the traffic trunk line flux is larger, when traffic not discrete, good control effect.2.6 Surface controlling waySurface controlling method is also called the regional coordination control or online network control, control object of the system is all the intersections in the city or an area traffic signal. Surface controlling method is an extension of the wire control way. Surface controlling area generally divided into several sub areas, the area here is to point to in the same cycle to control area. Control system by the central control machine, the sub-area control machine, intersection control level 3.Central control machine give each child the best cycle is long, each district control machine is responsible for the phase difference and the optimization of calculation, tuning of intersection control machine to do, the overall goal is to make the area of each vehicle when through certain intersection of total loss is minimal. Surface control system applicable to several highways crisscrosses each other around in the city road network structure, at this time if the drive-by-wire cannot achieve the desired effect.3 Intersection traffic signal control optimization method were reviewed3.1 Classical control methodEarly traffic signal controller is in accordance with the fixed percentage of time cycle is long and traffic lights to control the change of the light. The timing control, is study how to according to the intersection of road conditions and intersection of imported to the traffic flow and traffic to determine the timing signal timing scheme. The scheme including determine the phase sequence and number of intersections, and aspect ratio parameters such as cycle and green letter. The main basis of determining the basic parameters of the control signal is to make the intersection traffic efficiency is the largest. Its evaluation index generally have the following: capacity, saturation, travel time, frequency, delay, parking rate and fuel consumption, etc.The greater the cycle length, the greater the capacity, but the vehicle delay and fuel consumption also increase; Cycle length is smaller, the delay of parking is smaller, but the proportion of effective green light time drop, useful signal cycle time reduction, capacity fell sharply. Therefore the signal control of intersection signal timing principle is: undercertain road condition, should match with the appropriate cycle length, slightly higher than that of traffic demand for capacity and minimize delays, parking, and fuel consumption. Both to ensure smooth vehicles and reduce the operation cost. The basic principle of timing signal timing technology is the foundation of other control timing.3.2 The signal control optimization method based on intelligent controlAlthough induction control compared to the timing control can adapt to the random variation of traffic flow, but the induction control algorithms only consider the current phase the extension of time, the passage without considering other phase of vehicle queue. Sometimes, even through phase continuous vehicles arrived, if the other direction of vehicle queue length is very long, also want to change in time phase, in order to obtain the intersection traffic benefit is the biggest. There are two ways to solve the problem, one is the intersection in all directions by using the mathematical model of the vehicle to make accurate predictions, according to the operational research and optimization theory to determine the direction of green light time.2 it is to adopt the method of intelligent control to control the intersection. Because the urban traffic system has the characteristics of randomness, fuzziness, uncertainty, difficult to its mathematical model is set up. Scholars at home and abroad by establishing a mathematical model of the traffic system puts forward all kinds of control algorithm, but because the mathematical model can't reflect the real traffic system very well, even though algorithms are complex, the control effect are not so good, also poor real-time performance. In recent years, with the development of the theory of intelligent control, with the method of the intelligent control of traffic system control got the attention of scholars. Among various kinds of intelligent control methods, fuzzy control and neural network control is dominant.4 The ITS and the research direction is put forwardIn the optimization of traffic signal control of the process, people gradually realize that the traffic signal control couldn't solve the problem of urban traffic congestion. Safety of comprehensive Transportation System, to solve the traffic congestion and traffic accidents, energy and environmental problems, establish efficient, the United States, Japan, Europe and other countries have launched ITS(Intelligent Transportation System) research. ITS concept is put forward in 1994 in Japan, but the research work as early as the late 1960 s, formed the United States, Europe and Japan three ITS research base. ITS refers to the information processing, communication, control, electronics and other advanced technology, made the car, and way more coordinated together, reduce the number of traffic accidents, congestion and pollution, so as to improve the transportation efficiency and productivity of the integrated system. ITS is a new generation of road traffic system, ITS essence is the use of modern high-tech comprehensive solution to the transportation problem. ITS research direction is mainly focused on the traffic control and management, vehicle safety and control, travel information service, the development of traffic flow model, the human element, communications and radio traffic technology, intelligent transport system framework, the evaluation and simulation, est.’s ITS study field, automatic charging system, vehicle control and safety systems, such as bus operation of the system are related to the traffic signal control system, when used in conjunction in significant benefits can be obtained.译文城市道路交叉口交通信号控制研究Papageorgiou M摘要城市交通是城市经济生活的命脉，是衡量一个城市文明进步的标志，对于城市经济的发展和人民生活水平的提高起着十分重要的作用。
有关智能交通相关文献翻译（中文+英文）
智能交通智能交通是一个基于现代电子信息技术面向交通运输的服务系统。
它的突出特点是以信息的收集、处理、发布、交换、分析、利用为主线，为交通参与者提供多样性的服务。
在该系统中，车辆靠自己的智能在道路上自由行驶，公路靠自身的智能将交通流量调整至最佳状态，借助于这个系统，管理人员对道路、车辆的行踪将掌握得一清二楚。
基本信息智能交通系统 (IntelligentTransportationSystem，简称ITS)是未来交通系统的发展方向，它是将先进的信息技术、数据通讯传输技术、电子传感技术、控制技术及计算机技术等有效地集成运用于整个地面交通管理系统而建立的一种在大范围内、全方位发挥作用的，实时、准确、高效的综合交通运输管理系统。
ITS可以有效地利用现有交通设施、减少交通负荷和环境污染、保证交通安全、提高运输效率，因而，日益受到各国的重视。
21世纪将是公路交通智能化的世纪，人们将要采用的智能交通系统，是一种先进的一体化交通综合管理系统。
在该系统中，车辆靠自己的智能在道路上自由行驶，公路靠自身的智能将交通流量调整至最佳状态，借助于这个系统，管理人员对道路、车辆的行踪将掌握得一清二楚。
特点智能交通系统具有以下两个特点：一是着眼于交通信息的广泛应用与服务，二是着眼于提高既有交通设施的运行效率。
与一般技术系统相比。
智能交通系统建设过程中的整体性要求更加严格．这种整体性体现在：（1）跨行业特点。
智能交通系统建设涉及众多行业领域，是社会广泛参与的复杂巨型系统工程，从而造成复杂的行业间协调问题。
（2）技术领域特点。
智能交通系统综合了交通工程、信息工程，通信技术、控制工程、计算机技术等众多科学领域的成果，需要众多领域的技术人员共同协作。
（3）政府、企业、科研单位及高等院校共同参与，恰当的角色定位和任务分担是系统有效展开的重要前提条件。
（4）智能交通系统将主要由移动通信、宽带网、RFID、传感器、云计算等新一代信息技术作支撑，更符合人的应用需求，可信任程度提高并变得“无处不在”。
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)
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。
交通信号智能控制系统外文文献及翻译
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。
交通毕业单一的交通流模式识别研究交叉口智能信号控制毕业论文外文文献翻译及原文
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。
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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。