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1、精选优质文档-倾情为你奉上Traffic lightsSignal control is a necessary measure to maintain the quality and safety of traffic circulation. Further development of present signal control has great potential to reduce travel times, vehicle and accident costs, and vehicle emissions. The development of detection and co
2、mputer technology has changed traffic signal control from fixed-time open-loop regulation to adaptive feedback control. Present adaptive control methods, like the British MOVA, Swedish SOS (isolated signals) and British SCOOT (area-wide control), use mathematical optimization and simulation techniqu
3、es to adjust the signal timing to the observed fluctuations of traffic flow in real time. The optimization is done by changing the green time and cycle lengths of the signals. In area-wide control the offsets between intersections are also changed. Several methods have been developed for determining
4、 the optimal cycle length and the minimum delay at an intersection but, based on uncertainty and rigid nature of traffic signal control, the global optimum is not possible to find out.As a result of growing public awareness of the environmental impact of road traffic many authorities are now pursuin
5、g policies to: manage demand and congestion; influence mode and route choice; improve priority for buses, trams and other public service vehicles; provide better and safer facilities for pedestrians, cyclists and other vulnerable road users; reduce vehicle emissions, noise and visual intrusion; and
6、improve safety for all road user groups.In adaptive traffic signal control the increase in flexibility increases the number of overlapping green phases in the cycle, thus making the mathematical optimization very complicated and difficult. For that reason, the adaptive signal control in most cases i
7、s not based on precise optimization but on the green extension principle. In practice, uniformity is the principle followed in signal control for traffic safety reasons. This sets limitations to the cycle time and phase arrangements. Hence, traffic signal control in practice are based on tailor-made
8、 solutions and adjustments made by the traffic planners. The modern programmable signal controllers with a great number of adjustable parameters are well suited to this process. For good results, an experienced planner and fine-tuning in the field is needed. Fuzzy control has proven to be successful
9、 in problems where exact mathematical modelling is hard or impossible but an experienced human can control the process operator. Thus, traffic signal control in particular is a suitable task for fuzzy control. Indeed, one of the oldest examples of the potentials of fuzzy control is a simulation of t
10、raffic signal control in an inter-section of two one-way streets. Even in this very simple case the fuzzy control was at least as good as the traditional adaptive control. In general, fuzzy control is found to be superior in complex problems with multiobjective decisions. In traffic signal control s
11、everal traffic flows compete from the same time and space, and different priorities are often set to different traffic flows or vehicle groups. In addition, the optimization includes several simultaneous criteria, like the average and maximum vehicle and pedestrian delays, maximum queue lengths and
12、percentage of stopped vehicles. So, it is very likely that fuzzy control is very competitive in complicated real intersections where the use of traditional optimization methods is problematic.Fuzzy logic has been introduced and successfully applied to a wide range of automatic control tasks. The mai
13、n benefit of fuzzy logic is the opportunity to model the ambiguity and the uncertainty of decision-making. Moreover, fuzzy logic has the ability to comprehend linguistic instructions and to generate control strategies based on priori communication. The point in utilizing fuzzy logic in control theor
14、y is to model control based on human expert knowledge, rather than to model the process itself. Indeed, fuzzy control has proven to be successful in problems where exact mathematical modelling is hard or impossible but an experienced human operator can control process. In general, fuzzy control is f
15、ound to be superior in complex problems with multi-objective decisions.At present, there is a multitude of inference systems based on fuzzy technique. Most of them, however, suffer ill-defined foundations; even if they are mostly performing better that classical mathematical method, they still conta
16、in black boxes, e.g. de fuzzification, which are very difficult to justify mathematically or logically. For example, fuzzy IF - THEN rules, which are in the core of fuzzy inference systems, are often reported to be generalizations of classical Modus Ponens rule of inference, but literally this not t
17、he case; the relation between these rules and any known many-valued logic is complicated and artificial. Moreover, the performance of an expert system should be equivalent to that of human expert: it should give the same results that the expert gives, but warn when the control situation is so vague
18、that an expert is not sure about the right action. The existing fuzzy expert systems very seldom fulfil this latter condition.Many researches observe, however, that fuzzy inference is based on similarity. Kosko, for example, writes Fuzzy membership.represents similarities of objects to imprecisely d
19、efined properties. Taking this remark seriously, we study systematically many-valued equivalence, i.e. fuzzy similarity. It turns out that, starting from the Lukasiewicz well-defined many-valued logic, we are able to construct a method performing fuzzy reasoning such that the inference relies only o
20、n experts knowledge and on well-defined logical concepts. Therefore we do not need any artificial defuzzification method (like Center of Gravity) to determine the final output of the inference. Our basic observation is that any fuzzy set generates a fuzzy similarity, and that these similarities can
21、be combined to a fuzzy relation which turns out to a fuzzy similarity, too. We call this induced fuzzy relation total fuzzy similarity. Fuzzy IF - THEN inference systems are, in fact, problems of choice: compare each IF-part of the rule base with an actual input value, find the most similar case and
22、 fire the corresponding THEN-part; if it is not unique, use a criteria given by an expert to proceed. Based on the Lukasiewicz welldefined many valued logic, we show how this method can be carried out formally.Hypothesis and Principles of Fuzzy Traffic Signal Control Traffic signal control is used t
23、o maximize the efficiency of the existing traffic systems 6. However, the efficiency of traffic system can even be fuzzy. By providing temporal separation of rights of way to approaching flows, traffic signals exert a profound influence on the efficiency of traffic flow. They can operate to the adva
24、ntage or disadvantage of the vehicles or pedestrians; depend on how the rights of ways are allocated. Consequently, the proper application, design, installation, operation, and maintenance of traffic signals is critical to the orderly safe and efficient movement of traffic at intersections.In traffi
25、c signal control, we can find some kind of uncertainties in many levels. The inputs of traffic signal control are inaccurate, and that means that we cannot handle the traffic of approaches exactly. The control possibilities are complicated, and handling these possibilities are an extremely complex t
26、ask. Maximizing safety, minimizing environmental aspects and minimizing delays are some of the objectives of control, but it is difficult to handle them together in the traditional traffic signal control. The causeconsequence- relationship is also not possible to explain in traffic signal control. T
27、hese are typical features of fuzzy control.Fuzzy logic based controllers are designed to capture the key factors for controlling a process without requiring many detailed mathematical formulas. Due to this fact, they have many advantages in real time applications. The controllers have a simple compu
28、tational structure, since they do not require many numerical calculations. The IFTHEN logic of their inference rules does not require much computational time. Also, the controllers can operate on a large range of inputs, since different sets of control rules can be applied to them. If the system rel
29、ated knowledge is represented by simple fuzzy IFTHEN- rules, a fuzzy-based controller can control the system with efficiency and ease. The main goal of traffic signal control is to ensure safety at signalized intersections by keeping conflict traffic flows apart. The optimal performance of the signa
30、lized intersections is the combination of time value, environmental effects and traffic safety. Our goal is the optimal system, but we need to decide what attributes and weights will be used to judge optimality.The entire knowledge of the system designer about the process, traffic signal control in
31、this case, to be controlled is stored as rules in the knowledge base. Thus the rules have a basic influence on the closed-loop behaviour of the system and should therefore be acquired thoroughly. The development of rules is time consuming, and designers often have to translate process knowledge into
32、 appropriate rules. Sugeno and Nishida mentioned four ways to derive fuzzy control rules:1. operators experience2. control engineers knowledge3. fuzzy modelling of the operators control actions4. fuzzy modelling of the process5. crisp modeling of the process6. heuristic design rules7. on-line adapta
33、tion of the rules.Usually a combination of some of these methods is necessary to obtain good results. As in conventional control, increased experience in the design of fuzzy controllers leads to decreasing development times.The main goals of FUSICO-research project are theoretical analysis of fuzzy
34、traffic signal control, generalized fuzzy rules for traffic signal control using linguistic variables, validation of fuzzy control principles and calibration of membership functions, and development of a fuzzy adaptive signal controller. The vehicle-actuated control strategies, like SOS, MOVA and LH
35、OVRA, are the control algorithms of the first generation. The fuzzy control algorithm can be one of the algorithms of the second generation, the generation of artificial intelligence (AI). The fuzzy control is capable of handling multi-objective, multi-dimensional and complicated traffic situations,
36、 like traffic signalling. The typical advantages of fuzzy control are simple process, effective control and better quality.FUSICO-project modelled the experience of policeman. The rule base development was made during the fall 1996. Mr. Kari J. Sane, experienced traffic signal planner, was working a
37、t the Helsinki University of Technology at this time. Everyday discussions and working groups helped us to model his experience to our rules.In particular pathological traffic jams or situations where there are very few vehicles in circulation; there first-in-first-out is the only reasonable control
38、 strategy. The Algorithm is looking for the most similar IF-part to the actual input value, and the corresponding THEN-part is then fired. Three realistic traffic signal control systems were constructed by means of the Algorithm and a simulation model tested their performance. Similar simulations we
39、re made to a non-fuzzy and classical Mamdani style fuzzy inference systems, too. The results with respect to average vehicle and pedestrian delay or average vehicle delay were in most cases better on fuzzy similarity based control than on the other control systems. Comparisons between fuzzy similari
40、ty based control and Mamdani style fuzzy control also strength an assumption that, in approximate reasoning, a fundamental concept is many-valued similarity between objects rather than a generalization of classical Modus Ponens rule of inference.The results of this project have indicated that fuzzy
41、signal control is the potential control method for isolated intersections. The comparison results of Pappis-Mamdani control, fuzzy isolated pedestrian crossing and fuzzy two-phase control are good. The results of isolated pedestrian crossing indicate that the fuzzy control provides the effective com
42、promise between the two opposing objectives, minimum pedestrian delay and minimum vehicle delay. The results of two-phase control and Pappis-Mamdani control indicate that the application area of fuzzy control is very wide. The maximum delay improvement was more than 20 %, which means that the effici
43、ency of fuzzy control can be better than the efficiency of traditional vehicle-actuated control.According to these results, we can say that the fuzzy signal control can be multiobjective and more efficient than conventional adaptive signal control nowadays. The biggest benefits can, probably, be ach
44、ieved in more complicated intersections and environments. The FUSICO-project continues. The aim is to move step by step to more complicated traffic signals and to continue the theoretical work of fuzzy control. The first example will be the public transport priorities. 交 通 灯信号控制是一种必要的措施以确保的质量和安全,交通循
45、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MOVA 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。 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
46、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
47、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。即使在这个非常简单的情况下,模糊控制是至少在作为一个良好的传统的自适应控制。一般而言,模糊控制是发现在复杂问题都优于用多目标决策。在交通信号控制多种交通流竞争来自同一时间和空间,而且不同的优先选择往往不同交通流或车辆组。此外,优化
48、标准,包括几个同时喜欢平均和最大车辆和行人延误、最大队列长度和百分比停止的车辆。所以,它很可能是很有竞争力的模糊控制在复杂真实的十字路口的地方传统的优化方法的使用是有问题的。介绍了模糊逻辑,并成功地应用于大范围的自动控制任务。最大的好处模糊逻辑是有机会模型与不确定的模糊决策。此外,模糊逻辑有能力理解语言指令和控制策略的基础上产生的先验的沟通。这一点在利用模糊逻辑来控制理论的基础上,是模仿人类专家控制的知识,而不是为了构建过程本身。的确,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,但一名有经验的操作员可以控制的过程。一般而言,模糊控制是发现在复杂问题都优于多目标决
49、策。目前,有大量的基于模糊推论系统技术。不过它们当中的主要部分,受含糊不清的根基;即使它们大都是古典数学方法表现更好,他们还带有黑色的盒子,如德模糊化,这是很难证明数学或逻辑的。例如,如果-然后模糊规则,它们在核心的模糊推理系统,经常报道的工作方式,是Ponens概括规则推理机制的经典,但随便起来就不是这样的,这之间的关系,这些规则和多值逻辑是任何已知的复杂和人工。此外,专家系统的性能应相当于人类专家:它应该得到同样的结果,专家给,但提醒当控制问题是如此模糊,专家是不确定适当的行为。现有的模糊专家系统很少满足这第二种情况。然而,很多研究观察,模糊推理的方法是基于相似。Kosko,举个例子,写的模糊隶属代表的相似性定义对象特性的imprecisely。以这句话严重,我们学习系统的多值等价,即模糊相似度。原来,从Lukasiewicz多值逻辑的定义,我们能构建出一个模糊推理方法的表演,依赖于专家知识推理和只在定义的逻辑概念。所以,我们不需要任何人造的解模糊化方法确定(如重心)决定最后输出的推断。我们基本的观察是,任何的模糊集的生成一个模糊相似度,这