[雙語翻譯]交通燈外文翻譯--基于模糊邏輯的智能交通燈模擬器設計及硬件實現(xiàn)

1、4600 英文單詞， 英文單詞，22500 英文字符，中文 英文字符，中文 7150 字文獻出處： 文獻出處：Karakuzu C, Demirci O. Fuzzy logic based smart traffic light simulator design and hardware implementation[J]. Applied Soft Computing, 2010, 10(1): 66-73.Fuzzy logic

2、based smart traffic light simulator design and hardware implementationCihan Karakuzu, Osman DemirciAbstractThe objective of this study is to develop fuzzy logic based traffic junction light simulator system for design an

3、d smart traffic junction light controller purposes and also to observe its performance. Traffic junction simulator hardware is developed to overcome difficulties of working in a real environment and to easily test the pe

4、rformance of the controller. By using the traffic light simulator developed in this study, results of constant duration (conventional) traffic light controller and fuzzy logic based traffic light controller are compared

5、where the vehicle inputs are supplied by the simulator. Statistical experimental results obtained from the implemented simulator show that the fuzzy logic traffic light controller dramatically reduced the waiting time at

6、 red lights since the controller adapts itself according to traffic density. It is obvious that the intelligent light controller is going to provide important advantages in terms of economics and environment.Keywords: T

7、raffic junction; Traffic lights control; Fuzzy control ;Hardware simulator ;Intelligent lights1. IntroductionMost of the traffic junction signal controllers are fixed-cycle type (conventional), i.e., constant green/red p

8、hase for each traffic signal cycle. Although this operation style is simple, its perfor- mance is generally poor for heavy traffic. One reasonable alternative is to design an intelligent controller instead of a fixed-cyc

9、le traffic light control system. Since they have many advantages, smart traffic lights cannot be ignored especially in metropolitan areas. After Zadeh defined the fuzzy sets theory in 1965, this technique has been widely

10、 used in engineering applications. Some studies applying fuzzy set theory to traffic signal control have been done [1–6]. The most popular of these was put forth by Pappis and Mamdani [1]. They built a good model for a t

11、wo way traffic junction where each way has a single lane of traffic flow [1]. Following this Nakatsuyama realized the fuzzy logic based traffic light control of cascaded two way traffic junctions in 1984 [2]. In 1993 Fav

12、illa et al. introduced traffic light controller for a traffic junction with multiple lanes [4]. Recently, Trabia et al. [6] simulated a single junction with four lanes which also took the left- turning traffic flow into

13、account. Current studies [7–14] have considered traffic signal control from the standpoint of different aspects. The objective of these studies is obtaining optimal traffic flow to reach minimal waiting time. These studi

14、es are based on software simulation model. The most important aspects of these studies can be summarized as below.In Ref. [7], Ella Bingham used the same intersection config- uration as in the Pappis and Mamdani simulati

15、on [1]. In her study, the parameters of a Mamdani type fuzzy traffic signal controller were determined by reinforcement learning algorithm based on simulation environment. The objective was to minimize vehicular delay. S

16、he reported that the proposed fuzzy controller exhibited successful performance at constant traffic volumes. Chou and Teng [8] extended the As can be seen from the studies depicted in the previous paragraph traffic light

17、s control has been developed by software simulation models. In this paper traffic simulator system and the fuzzy controller for traffic lights are implemented based on hardware for a four directional junction. From this

18、point of view, this study differs from those depicted above and makes a contribution to the literature. In this study, a four directional single traffic junction is used as shown in Fig. 1. The implemented traffic simula

19、tor system is embedded in the microcontroller hardware. Key points that should be considered for the control of the traffic junction traffic lights are as follows. The average speed of vehicles moving from west to east a

20、t green light is 12 m/s. The time needed to reach the lights on the 200 m length lane where the detectors are placed is calculated using Eq. (1), assuming that there is no vehicle in queue.V= =16.66s （1）200

21、9898;12𝑚/𝑠Using a similar approximation, the vehicle moving from north to south lane with 100 m length, at an average speed of 12 m/s will need 8.33 s. The length of traffic junction is an important param

22、eter in determining the lower limit of the green light duration for the traffic light controller considered in the above calculation. The green and red light durations should be changed with respect to the number of vehi

23、cles in the queue at each cycle in order to keep the number of the vehicles in the queue at a minimum. For example, a 20–120 s phase duration limit may be selected for green light. Then the duration of green light can be

24、 increased step by step up to a maximum level starting from the minimum considering the length of queue at the beginning of each phase. When there is no vehicle in the queue the phase cycle should immediately be terminat

25、ed to let the vehicles pass through in the other direction. Amount of the increment is determined at the end of each phase cycle that can be adjusted depending on the length of queue. However, the total phase duration wi

26、th the extra increments cannot exceed the maximum level.Fig. 2. Block structure of the implemented traffic junction light control systemIn conventional traffic lights, length of vehicles was not taken into account for th

27、e duration of lights. However the real length of vehicles such as 4, 4.2, 4.6 and 8 m has been used for simulation in a recent study in Ref. [8]. In recent studies, sensors are used to determine the number of vehicles at