Using Intelligent Agents

8/8/2019 Using Intelligent Agents

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U S I N G I N T E L L I G E N T A G E N T S F O R D Y N A M I C U R B A N T R A F F I CC O N T R O L S Y S T EM S

Danko A. RoozemondDelft University of Technology, faculty of Civil Engineering and Ge osciences,Civil Eng ineerin g Irfformatics

A B S T R A C T

In this research we investigate the applicabili ty of autonomous intell igent agents inUrban Traffic Control (UTC ), an d w hy the se Artificial Intell igent strategies are useful.Designing, implemen ting, optimising and adjusting UTC systems involves quite som eeffort and knowledg e. We prop ose a system that autonomously can adapt to ch angingenvironme nts. The m ain advantages from the artif icial Intell igent Agents (IA) are:- S elf adjustabilty is an integral part o f art if icial IA based traffic control unit (bothlong (road wo rks) and short term (accidents) changes are covered);- The, mo re flexible, traffic control unit can be optimised while the unit is operating.For self-evaluating, -optimising and -adjusting UTC we need a fully pro-active, real-t ime traffic control system. Such an UTC system requires monitoring system oftraffic, a rule- or model-base for evaluation and adjustment, a mode l o f the surroundsand a n efficient diagnostic routine for both traffic l ight operations as well as rule- a ndparameter adjustments. The UTC model system is based on several IntelIigentintersection Traffic Signalling Agents (ITSA) and some authority agents.1 . I N T R O D U C T I O N

A large percentage of peak-h our travel is conducted under congeste d conditions. Theexisting road netw ork is not always able to cop e with i ts demand. Un der pressure fromthese problems, traffic management professionals and policy makers turn to newtraff ic manage ment and -control sys tems. Our approach is jus t one o f the many waysto less congestion and m ore op timal use o f urban intersections. Signal control systemsthat have the capabili ty of optimising and adjusting the traffic l ight sett ings are able toimprove the v ehicular throughp ut and minimise delay through appropriate response tochanges in demand patterns. Adjusting a traffic control unit is a costly and timelyaffair . Due to several reasons, changing environme nts do not always lead to changesin the traffic control units . The hypo thesis is that it may be useful to m ake use o f selfevaluating and self adjusting traffic control systems and that in urban traffic controlthere is a market fo r a system that is ab le to adjust i tself if the env ironmen t changes.The general objective of this research is to obtain knowledge regarding theapplicability of autonom ous intell igent agents in the dom ain o f Intelligent TransportSystems (ITS) and specifically in urban traffic control (UTC). W e prop ose a systemthat autono mously can adapt i tse lf to ch anging environmen ts. For self-evaluating, -optimising and -adjusting UTC we need a fully pro-active, real-t ime traffic controlsystem. Such a system must have the capabili ty of optimising the traffic flow byadjusting the traffic l ights and traffic l ight parameters in order to m aximise th e perso n

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and vehicular throughput and m inimise delay through appropriate response to changesin demand patterns. The above considerations suggest a need to complement existingsystems for traffic control with strategic knowledge applied to gain understanding o fthe specific control processes, and corresponding actions. In such a system the agentshave artificial intelligence and are capable of calculating and optimising, haveadequate knowledge about the intersection(s) and may directly influence the controlstrategy of their intersection(s). These agents are able to get insight in oncomingtraffic ahd are able to co-ordinate the traffic signalling operations. In such way we,hope to, get a real-time system that makes bet ter use of the capacity of the intersectionand still m aintai n its integrity and stability within the overall transportation system.2 . I N T E G R A T I N G D Y N A M I C T R A F F I C C O N T R O L2 .1 Typ o logy o f an I D TM I SOne of the difficulties of traffic control is that traffic is not fully predictable. Insystems where huma ns are in control a total deterministic system is beyond reach. Thebasis of this research forms a theoretical framework o f an Integrated Dynamic TrafficManag ement a nd Information System 0DTM IS); a framework incorporating all trafficmanagement and -control systems, thus creating one multi-user, multi-disciplinarytraffic mana gemen t and control system. The focus will be mainly on concepts and theextra opportunities of an IDTMIS; not on the technological (read implementation)sides. For a more elaborate description of IDTMIS see Roozemond (1996-a). Theimpacts of such an integrated system can be significant: improved operationalefficiency, improved safety, reducing energy and environmental impacts, improvedcomfort and more co-operation between system users (de Romph, 1994). Theframework is not propounded as one single system. Instead it is based on a scalable,open, distributed and recursive architecture where several smaller systems interact ondifferent levels. The individual and modular parts are designed to keep informationlocal, working closely together, acting on the rules implemented in the system and onactual information gathered by the system. Sub-systems are designed to performautonomously, co-ordinating their own actions, interacting with other sub-systemstrough standardised interfaces when necessary. To put IDTMIS in perspective,integration of dynamic traffic management and control systems alone, is no cure forcurrent traffic problems, although it may streamline and reduce the traffic load andgive better information and advises.2.2 Urban Traff ic ControlDesigning, implementing, optimising and adjusting urban traffic control systemsinvolves quite some effort and requires specific knowledge. Due to several reasons,changing environments not always lead to changes in the traffic control units.Adjusting a traffic control unit is a costly and timely affair. Often a traffic control unitis not adjusted for short term road works, nor for changing traffic patterns. Theeffectiveness of (urban) traffic control systems greatly depends on its ability to reactupon changes in traffic patterns. W hen this ability to react becomes an integral part ofthe traffic control unit, the better it can react to changes in traffic conditions.Intelligent signal control systems must have the capability to handle unforeseenchanges in traffic flow, such as accidents and optimise the traffic flow by adjusting thetraffic lights and co-ordinate operation between each signal in order to maximisethroughput and minim ise delay. All current traffic signal control methods are based on

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traffic demand data in the past; the t ime span between data collection, datat ransmission, calculat ion o f parameter set t ings varies f rom years to a minim um of 5minutes. T hese method s are not good in ci rcumstances where t raf f ic demand changesrapidly within a t ime interval of 1 - 10 minutes as in the morning or evening rushhours. F or intell igent urban traffic control we ideally need a fully pro-active, real-t imetraffic control system; anticipating what will happen within the n ext coup le of minutes(Rooze_mond, 1997). For su ch a sys tem the c ontrol plans use d are based o n actuatedtraffic conditions and are updated frequently and possibly even during a cycle. Apossible disadvantage of such a short term signal t iming sett ing is that i t mayintroduce cycle to cycle fluctuations. Possible advantage is that such a system couldcope with major traffic disruptions. A right balance should be found: being able tochan ge signal sett ings, but not that differentiation each t im e (for stabili ty reasons).In the domain of Urban Traff ic Control (UTC) more and more research is di rectedtowards operation and optimisation of individual and linked signal-controlled roadjunctions based on actual traffic (Kronborg & Davidson, 1993), (Salto et al., 1997),(Vincent, 1993)). The basic premis e is that existing signal-plan generation tools (e.g. ,UTOPIA-SPOT or SCOO T) mak e rational decisions about signal plans unde r varyingconditions. True as this may be, these tools can ' t yet be used in a real-t ime sett ing formor e than traffic control as no real meta-rules are incorporated into the system. Othersystems capable o f self -optimising are MOV A (UK) and LH OVR A (Sweden). Bothare designed for optimising isolated intersections and no explicit integration takesplace between intersect ions. Both are mainly used at intersect ions in high-speedenvironments, not in urban areas. MOV A uses several detector types and some sort ofoptimisation algorithm based on stage control and mathematical optimisation asLHOVRA uses signal group control and heurist ic traffic engineering functions.Research by K ronborg & Davidson (1993) sh owed that both opt imisat ion algori thmsare good, but signal group control can be o f benefit for cri tical traffic mov eme nts evenduring c onge sted situations.

An UTC exhibit ing intell igent behaviour must, minimally, have the followingcapabili t ies: handle operations in real t ime and show adaptive and goal-orientedbehaviour. O ne o f the poss ible IT tools to achieve these goals is the u se o f Intell igentAgents. The aforement ioned considerat ions suggest a nee d to complem ent exist ingsystems for traffic control with strategic knowledge applied to understand thesituation, traffic behaviour, the specific processes of congestion development, andthen corresponding actions could be taken by intel l igent agents. Agen t based systemsshould also be able to handle unexpected or wrong input and learn f rom experiences.3 . I N T E L L I G E N T A G E N T SIntell igent Age nts can be des cribed as autonomous comp utational systems that inhabitdynamic, not ne cessarily fully predictable environments. An agent is "autonomous" tothe degre e that i t decides for i tself how to relate data to com man ds in i ts efforts toachieve goals, satisfy motivations, etc. Autonomy is the abili ty to function as anindependent uni t or element over an extended period of t ime, performing a variety o factions necessary to achieve pre-designated objectives while responding to st imuliproduced by integrally contained sensors (Zeigler, 1990). Besides some ArtificialIntell igence, intell igent agents should have some additional at tributes. Intell igentagents, as an entity, should be able to solve problem s by i tse lf in real-time; understandinformation; have intentions; draw distinctions between situations; generalise;

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synthesise new concepts and / or ideas; model the wor ld they operate in and plan andpredict conse quenc es of actions and evaluate alternatives. Multi Ag ent Systems can becharacterised by the interaction of man y agents trying to solve a variety of problems ina co-operative fashion (Hogg & Huberm an, 1990). The problem solving com pon ent ofan intell igent agent can be a simple rule based system but can also be a neuralnetwork, an exper t sys tem or jus t some fuzzy rules .While considerable effort is being dev oted to understanding the detailed interactionsamon g a few agents and designing operational Multi Agent Sy stems that can deal withsimple problems, relatively li t t le is know n about the global behaviour o f these systemsas they are scaled up to deal with more realist ic problems. There are a number ofshortcomings and problems that are related to the distr ibution of autonomous agentsand tasks. One of the difficulties in both the design and the understanding ofdistr ibuted artificial intelligence systems com es from the lack of ce ntral controls, andthe ensuing conflicting, uncertain, incomplete and delayed knowledge on the part ofthe agents. In this respect, c oncurrency is important issue, because several processe scan be p erform ed simultaneously. Messag es arrive with delays and in arbitrary order.Therefore plans an d actions o f agents should be design ed to hand le those situations. I tis not possible to reconstruct, during run-time, the correct picture of the succession ofevents. When computational agents in such systems make choices in the presence ofdelayed and incom plete knowled ge about the state o f the.i t system; their dynam ics canbecome extremely complex. Par t o f the complexi ty in behaviour is due to the nature o fthe system, i .e. the value of an agent 's decision depends on the choices m ade by the allthe others. Co-ordination is an important issue for several reasons, for instance toprom ote beneficial interactions, to avo id harmful interactions and beca use individualdecisions may have global impacts. Thus a major question is how agents shouldchoose among their problem-solving strategies and, in particular, with which otheragents they should collaborate. We may come across local minima in traffic controlsolvers. It ma y b e obvio us that a provision to find a more global optimu m or findin g afeasible solution is a necessity for an agent. This complicated dynam ics can result inmany agents making poor decis ions when seen f rom a global perspect ive (even thoughthey appear reaso nable locally). This ma y lead to significantly lowered performanceand great difficulty in programmlug the individual agents. Thus, i t is important todevise effective decentralised control methods that simplify the global dynamics. I tmay be nec essary to get a hierarchy of agents. Such higher-level agent investigates theperformanc e of a grou p and h as the abili ty to change the goals or actions o f agents inthat group, The design o f the c ontrol laves go,mining the behaviou~ of individualagents is crucial for the design of co-operative agent teams. These laws util ise acombinat ion of local and/or global knowledge to achieve the des i red emergent groupbehaviour. In figure 1 the interaction and communication links of an agent systembased on several agents and agent groups is shown. In figure 2 an example of aspecific UTC agent based system is given.We propose a syste m that autonomo usly can adapt to changing environm ents. In thatsys tem we get an UT C sys tem based on agent technology that i s able to adapt andrespo nd to traffic conditions in real-t ime and stil l maintain i ts integrity and stabili tywithin the overall transportation system and in the meantime get a system that makesbetter use of the capacity of intersections. The key aspects of improved control, forwh ich c ontributions from artificial intell igence and artificial intelligent agents can beexpected, include (Amb rosino et al . , 1994):

the capab ili ty of dealing with m ultiple problems and c onflicting objectives;

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the capabi l i ty of making decisions on the basis of temporal analysis anddevelopments;the abili ty of managing, learning, and respon ding to non-recu rrent andunexpected events;sel f adjustabilty is an integral part of IA b ased units;the, mo re flexible, control unit can, pro-active, optimise while operating.

4. Agent Based Urban Traffic ControlThe basic functions of an (automated) Urban Traffic Control (UTC) system arereasonably wel l known. The part icular techniques proposed are experimental and no tmainstream , espec ially wh en propo sed for such a large, on-line, application. Thereforewe have to start with a study of what is necessary to build. After that we need todetermine how we are going to bui ld what kind of a system. Then we needprototyping to test the given claims and feasibil i ty. The most useful agent in UTCwould be a traffic signal control agent. As stated before, the t ime-span from datacollection to signal control parameter sett ings can be shortene d to imp rove the c ontrolstrategy. Saito et al . (1997) have found that the use of quick response demandprediction models in saturated situations (degree of saturation > 0.5) could improvedelay's per ve hicle on a single approach intersection by 5% to 15 %. In saturatedsituations such a n im prove ment is huge and is achievable by intell igent signal control.Such an UTC system requires: moni toring system of t raf fic , a rule- or model-base forevaluation and adjustment, a model of the surrounds and an efficient diagnosticroutine for b oth traffic l ight operations as well as rule- and param eter adjustments.The essence of an, demand responsive, pro-act ive, UTC system can be based onseveral, coupled, intersection control ITSA's (Intell igent Traffic Signalling Agent) .The design of a multi-agent system requires flexible autonomy. Meaning that agentswill be required to w ork autonomou sly, but will often be influenced by others.

5. Concep t and requirements of agent based UT CAn agent based system is essentially discrete as all agents have a finite number ofstates. The agents' state evolution is driven by occurrence o f events. Events (output)are generated by agents due to events triggering an agent ( input) or as a passage oft ime. I t may be clear that the t ime-step of every agent should not necessari ly be thesame. The w hole process o f operat ing a global clock and deal ing wi th the messagepassin g and data transfer system is a network affair. There for we on ly give som ebasic details on the requirements of dynamic urban signal control systems. Thesesystem s need regular, fast and reliable transmission betw een the different agents andsub-sys tems of small but frequent messag es, with a high integrity. The comm unica tiontype should be multi-cast ( to a designated group of agents) or point to point ( to onesingle agent) .Urban Intell igent Transport Systems requires intell igent agents o f many types. In thispaper we only address specific ones dealing with urban intersection control. Theintell igent traffic signalling agent (ITSA) make s a decision on h ow to control i tsintersection based on goals, capabili ty, knowledge, perception and data. The decisio ncan be fol lowed by act ion. When necessary an agent can request for addi t ionalinformation or receive other goals or orders from its authority agent. This ma y prov e aneces sity to get a less local optimum, e.g. not one optimised intersection but several,

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and so adapting the goals o f a higher level. For a specific ITSA, implem ented to serveas an urban traffic control agent, the following aspects are taken into account(Roozemond, 1996-b, 1998):The ITSA has some goals or tasks to accomplish, for instance maximising thetraffic flow, giv en the underlying rules.

- The ITSA has rules and roles to perform. The rules are based on the normaltraffic regulation rules a nd the rules u sed for designing traffic control centres.Tl~e roles an a gent can perform are com parable to roles a policem an can pe rformby controlling the traffic l ights.- The ITSA is helped to decide what actions to take by its problem solvingcomponent (making diagnosis of t raff ic problems) , the vie w and knowledge i t

has of i ts environ ment, i ts abili ties and its state. Different situations can lead tobetter understanding o f a situation for an agent by gaining additional knowledge.

- The ITSA has skills and tasks that i t can perform depending on situations. Inseemingly similar situations an agent can act differently. The agent solves aproblem as independently as possible, acting on its own "feeling' and itsknowledge.

6. Intelligent Agent Based U rban Signal Control SystemThe U TC should be a deman d responsive, pro-active, sys tem based on actualinformation and adapting to situations. The essence of an IDTMIS/UTC consists ofseveral intersection control ITSA's and some authority agents. In figure 2 a morespecific example of a simplified UTC system is given. There we have one authorityagent controlling several intersection agents, which in their turn manage theintersection controls. An other possible solution is that the intersection agent controlsseveral signal group agents. In this paper we have chosen to elaborate on the firstexam ple for practicality reasons. The authority agents are not discussed here, but theirtasks are controlling, co-ordinating and leading the ITSA's towards a more globaloptimum.The design of a multi-agent system requires flexible autonomy. Meaning that agentswil l somet imes be required to work autonomously, but wi l l of ten be com manded o rinfluenced by others. Often an ITSA should sacrifice some performance for thepurpose o f co-operative behaviour eansed by appointment o f an author i ty agent orself-control of ITSA's. The authority agents are not discussed here, but their tasks arecontroll ing and leading ~e ITSA's towards a mote g~b al optkuum.An ITSA, capable of controlling or advising in real-t ime, should perform thefollowing actions: (Rooze mon d, 1996-b & 1998):

The ITSA receives at (given) t ime intervals the info rmation on the current stateof traffic (data collection).The IT SA rec eives information on oth er adjoining signalised intersections fromother ITSA's (data collection).The ITSA has an accurate model o f the control led intersection and know s thetraffic- and traffic con trol rules (analysis).The ITSA kno ws th e rec ent trends (analysis/interpretation of data).The ITSA should be able to calculate the next cycle mathematically correct(analysis/decisiun).The ITSA should be able to actuate the next cycle and operate the signalsaccordingly (control) .

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The ITSA should be ab le to detect and handle current traffic problems by itse lf(analysis/decision and control/action) and should inform other agents of thenature, severity and probable cause of the problem, if possible (datadistribution).The ITSA passes information on to other adjoining agents (data distr ibution).

An intell igent, agent based, UTC system should be capable of calculating andoptimisiiag control strategies, as w ell have know ledge ab out the intersection(s). For abetter overv iew of the internal ITSA mode ls and functions see figure 3. The centralpart o f the ITSA , acts as a control strategy agent. There can be several controlstrategies, such as anti-blocking, public transport priority and other adaptives trategies . There m ay be communicat ion with other ITSA 's of nearby intersections ,the urban traffic control centre, other control eentres (bus / route guidance, etc.). Thecontrol strategy agent uses the estimates of the prediction model agent whichestimates the states in the nea r future. The ITSA-prediction mod el age nt estimates thestates in the near future. The pre diction model ag ent gets i ts data specified and relatedto intersection and road segm ents; as an agent that 'kn ow s' the forecasting equations,actual traffic conditions and constraints and, in combination with the above givendata, future traffic situations can be calculated by way o f an inference engine an d it 'sknow ledge- and data-base. The kind of calculus used is effectively a state predictionmode l: predicting the states on t ime tj ', given the state on t im e tj '_dt. The kno wledge ispresented as production rules: IF .. . then.. .< prediction rule m> OR . . . (Roozemond,1997). Norm ally in n on conflicting cases (or single junctions) w hen only op timisationplays a role, a decision model agents ' choice based on a number of criteria withdifferent weigh ts will b e sufficient. Minlmising objectives is , in theory, quite simple:M,n~,object~e,*,~e,ght," That process in this sys tem becomes far more complex due to

1=0several different, continuously changing, weights and different goals o f the differentITSA's. For this problem we stil l need to fred a solution, other then directly giving thedecisions to authority agents. The ITSA -decision mode l agent calculate signal plans topro-act on these predictions. The control strategy agent checks that signal plan withother adjoining ITSA's and if approved it plans signal control strategy. Theeffectivene ss of a control strategy largely depen ds on the actual traffic and the cycleplan. An opt imisat ion procedure in which the cycle plan may be calculatedminimising several criteria, such as sum of total delay, total number of stops, totalcosts of losses, fuel consumption, t ime of day, etc. , can be included in the decisionmodel agent. For isolated traffic signals such an optimisation is already been made(Bang 1976). For arterial and c onnec ted junctions several of-line and heu risticmeth ods are beco ming available and are sti l l bee n developed. A voting system ma y behelpful for agents to co-ordinate and choose between contradicting actions andsynchronise actions between agents. Moreover, since the decisions are not centrallycontrolled, the agents independently and asynchronously select from the availablechoices the one with the highest efficiency. A blackboard architecture or a votingsys tem may be helpful for agents to prevent deadlocks , co-ordinate and choosebetween contradicting actions or non-optimal phases between agents. Meta-rules andauthority agents w ill be included to hand le contradictory or unforese eable system widetraffic control situations. If necessary an a gent can req uest for additional informa tionor receive goals or orders from its authority agent. Often an ITSA should sacrificesome performance in favour of co-operative behaviour caused by appointment of anauthority agent or se lf control of ITSA's. Here we shou ld ad that f inding the optimal

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1) After def ining what we need and what we have got , we need to determine how weare going to build such a system. It may be possible that such an age nt based systemcan be implemented in current UTC sys tems l ike SCOOT, UTOPIA-SPOT,LHO VRA or MOV A. I t looks that we need prototyping to tes t the given claims andfeasibility.2) One other aspect to be researched further is the way how to develop intell igenttraffic control a lgorithms that can handle dynamic cycle-recalculation in real-t ime. Tobe able to do th is pro-actively we need so me forecasting properties incorporated in thesystem. To forec ast properly we should use both historic data and actuated data frommonitoring de vices as input for our dynam ic prediction-, decision- and control- modelwhic h in i ts turn is the basis for the control strategy calculation process. We should beable to forecast travel t imes in the near future based largely on actuated data as well astravel t imes for som e time ahead based on actuated and historic data.3) Feasibili ty has our attention. The agent based sy stem should not only give the be stsolution, but in the process there should be an emphasis towards gett ing a feasiblesolution. The optimisation of a junction co uld introduce severe problems for others, soa global optimisation process / optimisation control via authority agents should beresearch ed. Relative ly little is know n on this, highly impQrtant, sub ject.8. Conclus ions

For urban traffic control the research agenda comprises to adjusting the controlschemes to ones that can deal with dynamic and actuated data. Further research on acontrol strategy, based on intell igent autonomous agents, is necessary to prov ideappropriate evidence on the usabili ty of AI / agent based control systems. Theparticular techniques proposed are experimental and not yet mainstream, especiallywhen proposed for such a large, on-line, application. The pro-active and re-activenature of agents can be a helpful paradigm in intell igent traffic management andcontrol. Further (real-life) tests on a control strategy, based on intelligent andautonom ous agents, are necessary to provide appropriate eviden ce for operational useas relatively l i t t le is known about the global behaviour of these intell igent agentsystems when they are scaled up to deal with more realist ic problems. With theintroduction of two un-coupled loops, whether agent technology is used or not, adifferent theory of traffic control can be met. Intell igent agents is a metaphor to beused for theoretical and implemen tation purposes. Primarily results indicate that giv enan autom ated control strategy implemen ted in the traffic signalling devices we can geta sys tem that makes bet ter use of the capaci ty of the intersection. It has been shownthat control systems based on agent technology can adapt and respond to changingconditions in real-t ime. As this research is st i l l ongoing we hope, in the end, todemonstrate that an UTC sys tem based on agent technology can adapt and respon d toreal world traffic conditions in real-time. A wo rking prototype of such a sy stem shouldgive appropriate evidence on th e usability of AI agent based control systems.References

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Ambrosino, G., Bielli, M., Boero, M. (1994), Artificial intelligence approach to roadtraffic control, artificial intelligence applications to traffic engineering, Bielli, M.,Ambrosino, G., Boero, M. (eds), VSP, UtrechtBang, K. (1976), Optimal control of isolated traffic signals, traffic engineering +control, ju ly 1976Hogg, T, Huberman, B.A. (1991), Controlling Chaos in Distributed Systems, IEEETransactions on Systems, Man & Cybernetics. v 21 n 6 Nov-Dec 1991Kronbeg, P., Davidson, F. (1993), MOVA and LHOVRA: traffic signal control forisolated intersections, traffic engineering + control, april 1993Romph, E. de (1994), A dynamic traffic assignment model: theory and applications,DUT-press, DelftR.oozemond, D.A. (1996-a), Analyzing and managing traffic, a m odel based onintelligent objects and self organization, Analyzing complex societal problems,DeTombe , D.J., Dijkum, C. van (eds.) Ralner Hampp verlag, MiinehenRoozemond, D.A. (1996-b), Intelligent traffic m anagement an d urb an traffic controlbased on autonomous objects, Sixth annual conference on: AIS'96, Artificialintelligence, simulation, and planning in high autonomy systems; San Diego,March 23-37, 1996Roozemond, D.A. (1997), Intelligent Transport Systems: autonomous u rba n trafficcontrol, 4th world congress on Intelligent transport Systems, 21-24 Oct 1997,Berlin, ITS America, Ertico and VertisRoozemond, D.A. (1998), Self-optimising and self-organising urban traffic controlsystems, Trail Conference proceedings no P98/3, T RA IL Research School, DelftSaito, T, Yasui, K, Ukyo, T, Nakano, J (1997), Study on traffic demand predictionmethods for intelligent traffic signal control, 4th worm congress on IntelligentTransport Systems, 21-24 Oct 1997, Berlin.Vincent, R., (1993), Safer signalized junction design and self-optimizing control,jou rna l o f advanced transportation, vo128, no 3Zeigler, B.P. (1990), High autonomy systems: concepts and models, proceedings A1,simulation, a nd plannin g in high autonomy systems, Zeigler, B., Rozenblit, J (eds.),IEEE, Los Alamos

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Q Agent-- Communication ink

Authodty linkr- -] Group of agents

Figure 1: Groups of agents and their connections

C o m m u n i c a t i o n l i n kI A u t h o r i t y l i n k

S o c i e t a l g r o u p

Figure 2: simplified agent based UTC system

I ~ '~ ~_ ~ ITSAI metamodel meta model t control strategypl~dic'd decision modelI detectors ~-'

Figure 3: Actu ated control strategy based o n agents

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