交通燈外文資料及翻譯---代理控制交通燈（英文）

1、Agent controlled traffic lightsDanko A. Roozemond *, Jan L.H. Rogier ** * Delft University of Technology, Civil Engineering and Geosciences Stevinweg 1, 2628 CN, Delft, The Netherlands Phone: +31(0)15 2784976, Fax: +31(0

2、)15 2787700 email: d.roozemond@ct.tudelft.nl ** Netherlands Organisation for Applied Scientific Research TNO-TPD Stieltjesweg 1, 2628 CK, Delft, The Netherlands Phone: +31(0)15 2692000, Fax: +31(0)15 2692111 email: jrogi

3、er@TPD.TNO.NLABSTRACT: Due to several reasons, changing conditions in the environment do not always lead to changes in the traffic control units. The hypothesis of this research is that it may be useful to make use of se

4、lf evaluating and self organising intelligent (urban) traffic control systems. In this research we focus on the applicability of autonomous intelligent agents within urban traffic control (i.e. traffic light systems). We

5、 consider the opportunity of building adaptive traffic control units based on intelligent agent technology that pro-act upon changes (short- and long term) in traffic in real-time. We expect that intelligent agent based

6、systems in urban traffic control can provide a more balanced, co-ordinated and optimal setting of the signal control schema during operation.KEYWORDS: Intelligent agents, urban traffic control, adaptive traffic control,

7、pro active control, traffic light systemsINTRODUCTIONThe 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

8、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 t

9、raffic 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

10、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 tra

11、ffic 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 i

12、s 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 ap

13、plied 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 co

14、ntrol 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

15、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 condition

16、s (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

17、 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 logic

18、al 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 contributionsESIT 2000, 14-15 September 2000, Aachen, Germany 77

19、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

20、 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

21、, changes due to changing traffic volumes over a short period of time, - abrupt, temporal, changes due to prioritised traffic over a short period of timeOne way of handling the balance between performance and complexity

22、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 age

23、nts. 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 r

24、obustness 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 ma

25、de 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,

26、 we only handle cars and we use simple rule bases for knowledge representation.5.2 TYPES OF AGENTS IN URBAN INTERSECTION CONTROLAs we divide the system in several, recognisable, parts we define the following 4 types of a

27、gents: - 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 s

28、pecific 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 pro

29、grams 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 (intersec

30、tion), 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.5.3 DESIGN OF OUR AGENT BASED SYSTEMThe ess

31、ence 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 decis

32、ions 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 a

33、gent(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

34、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 cu

35、rrent 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 (

36、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

37、 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

38、ITSA's, RSA's and all its traffic lights. Here we use the agent technology to implement a distributed planning algorithm. The route agents’ tasks are controlling, co-ordinating and leading the ITSA’s towards a mo