交通灯外文翻译1

Traffic lights

Signal 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 computer 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 techniques 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 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 pursuing 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

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 is 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 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 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 traffic 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 several 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 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 main 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 theory 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 found 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 contain 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 the 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 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 defined 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 on 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 …… 此处隐藏：15806字，全部文档内容请下载后查看。喜欢就下载吧 ……