交通信号智能控制系统外文文献及翻译doc.docx

1、交通信号智能控制系统外文文献及翻译docAgent controlled traffic lightsAuthor：Danko A. Roozemond，Jan L.H. Rogier Provenance：Delft University of TechnologyIntroductionThe quality of (urban) traffic control systems is determined by the match between the control schema and the actual traffic patterns. If traffic patterns

2、change, what 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 traffic conditions. Adjusting a traffic control unit is a costly and

3、 timely affair if it involves human attention. The hypothesis is that it might offer additional benefit using self-evaluating and self-adjusting traffic 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 adapt

4、ive systems. Real adaptive systems will need pro-active calculated traffic 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 is a theore

5、tical 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 applied to dynamic traffic control. Currently we are designing a layered model of an agent b

6、ased urban traffic control system. We will elaborate on that in the last chapters.Adaptive urban traffic control Adaptive signal control 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

7、based on feed-back algorithms using traffic demand 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 conditions (e.g., UTOPIA-spot,SCOOT). T

8、hese 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 about signal plans under varying conditions; but almost none of the current available tools behave pro-activel

9、y or have meta-rules that may change behaviour of the controller incorporated into the system. The next logical 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 contributions from artificial intel

10、ligence and artificial intelligent agents can be expected, include the capability of dealing with conflicting objectives; the capability of making pro-active decisions on the basis of temporal analysis; the ability of managing, learning, self adjusting and responding to non-recurrent and unexpected

11、events (Ambrosino et al., 1994).What are intelligent agents Agent technology is a new concept within the artificial intelligence (AI). The agent paradigm in AI is based upon the notion of reactive, autonomous, internally-motivated entities that inhabit dynamic, not necessarily fully predictable envi

12、ronments (Weiss, 1999). Autonomy is the ability to function as an independent unit over an extended period of time, performing a variety of actions necessary to achieve pre-designated objectives while responding to stimuli produced by integrally contained sensors (Ziegler, 1990). Multi-Agent Systems

13、 can be characterised by the interaction of many agents trying to solve a variety of problems in a co-operative fashion. Besides AI, intelligent agents should have some additional attributes to solve problems by itself in real-time; understand information; have goals and intentions; draw distinction

14、s between situations; generalise; synthesise new concepts and / or ideas; model the world they operate in and plan and predict consequences of actions and evaluate alternatives. The problem solving component of an intelligent agent can be a rule-based system but can also be a neural network or a fuz

15、zy expert system. It may be obvious that finding a feasible solution is a necessity for an agent. Often local optima in decentralised systems, are not the global optimum. This problem is not easily solved. The solution has to be found by tailoring the interaction mechanism or to have a supervising a

16、gent co-ordinating the optimisation process of the other agents.Intelligent agents in UTC,a helpful paradigmAgent technology is applicable in different fields within UTC. The ones most important mentioning are: information agents, agents for traffic simulation and traffic control. Currently, most ap

17、plications of intelligent agents are information agents. They collect information via a network. With special designed agents user specific information can be provided. In urban traffic these intelligent agents are useable in delivering information about weather, traffic jams, public transport, rout

18、e closures, best routes, etc. to the user via a Personal Travel Assistant. Agent technology can also be used for aggregating data for further distribution. Agents and multi agent systems are capable of simulating complex systems for traffic simulation. These systems often use one agent for every tra

19、ffic participant (in a similar way as object oriented programs often use objects). The application of agents in (Urban) Traffic Control is the one that has our prime interest. Here we ultimately want to use agents for pro-active traffic light control with on-line optimisation. Signal plans then will

20、 be determined based on predicted and measured detector data and will be tuned with adjoining agents. The most promising aspects of agent technology, the flexibility and pro-active behaviour, give UTC the possibility of better anticipation of traffic. Current UTC is not that flexible, it is unable t

21、o adjust itself if situations change and cant handle un-programmed situations. Agent technology can also be implemented on several different control layers. This gives the advantage of being close to current UTC while leaving considerable freedom at the lower (intersection) level.Designing agent bas

22、ed urban traffic control systemsThe ideal system that we strive for is a traffic control system that is based on actuated traffic controllers and is able to pro actively handle traffic situations and handling the different, sometimes conflicting, aims of traffic controllers. The proposed use of the

23、concept of agents in this research is experimental.Assumptions and considerations on agent based urban traffic controlThere are three aspects where agent based traffic control and -management can improve current state of the art UTC systems:- Adaptability. Intelligent agents are able to adapt its be

24、haviour and can learn from earlier situations.- Communication. Communication makes it possible for agents to co-operate and tune signal plans.- Pro-active behaviour. Due to the pro active behaviour traffic control systems are able to plan ahead.To be acceptable as replacement unit for current traffi

25、c control units, the system should perform the same or better than current systems. The agent based UTC will require on-line and pro-active reaction on changing traffic patterns. An agent based UTC should be demand responsive as well as adaptive during all stages and times. New methods for traffic c

26、ontrol 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 handle in a different way (Rogier, 1999):- gradual changes due to changing traff

27、ic volumes over a longer period of time,- abrupt changes due to changing traffic volumes over a longer period of time,- abrupt, temporal, 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 h

28、andling the balance between performance and complexity 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 agents. These agents have th

29、e task of solving conflicts between lower level agents that they cant solve. This represents current traffic control implementations and ideas. One final aspect to be mentioned is the robustness of agent based systems (if all communication fails the agent runs on, if the agent fails a fixed program

30、can be executed.To be able to keep our first urban traffic control model as simple as possible we have made the following assumptions: we limit ourselves to inner city traffic control (road segments, intersections, corridors), we handle only controlled intersections with detectors (intensity and spe

31、ed) at all road segments, we only handle cars and we use simple rule bases for knowledge representation.Types of agents in urban intersection control As we divide the system in several, recognisable, parts we define the following 4 types of agents:- Roads are represented by special road segment agen

32、ts (RSA),- Controlled intersections are represented by intersection agents (ITSA),- For specific, defined, areas there is an area agent (higher level),- For specific routes there can be route agents, that spans several adjoining road segments (higher level).We have not chosen for one agent per signa

33、l. This may result in a more simple solution but available traffic control programs 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 (intersection), but with local and glob