外文翻譯---多智能體系統(tǒng)優(yōu)化城市交通

1、A Multiagent System for Optimizing Urban TrafficJohn France and Ali A. GhorbaniFaculty of Computer Science University of New BrunswickFredericton, NB, E3B 5A3, CanadaAbstractFor the purposes of managing an urban traffic

2、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 per

3、formance of their assignedintersection; 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

4、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 enviro

5、nment, 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 mult

6、iagent 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 pop

7、ulation, 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. Theref

8、ore, 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 changin

9、g 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 expe

10、cted 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

11、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

12、[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 powe

13、rful 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 th

14、is 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 c

15、onclusions of the present study are summarized in Section 5.2 Urban Traffic Congestionthese agents are tailored to meet the requirements of its corresponding intersection.For the purposes of this project, the traffic net

16、work 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 cap

17、abilities 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 divid

18、es 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.O

19、dd 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

20、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

21、 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 abov

22、e 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 secon

23、ds 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 light pa

24、ttern 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 nei

25、ghbors 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.

26、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 traf

27、fic 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

28、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 th

29、e 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 ca

30、lculated as N = T/(α+ε)where α andε represent 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, respecti

31、vely.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 β to represent each of