INTELLIGENT TRAFFIC MANAGEMENT MODELS

Transcription

1 INTELLIGENT TRAFFIC MANAGEMENT MODELS Heribert Kirschfink, Josefa Hernández*, Marco.Boero** Momatec GmbH Diepenbenden 44, D Aachen Germany Phone: , Fax: * Department of Artificial Intelligence, Technical University of Madrid, Campus de Montegancedo s/n Boadilla del Monte, Madrid (Spain) Phone: , Fax: ** Softeco Sismat SpA Via S. Barborino 7/82, I Genova, Italy Phone: , Fax: ABSTRACT: The paper shows the state of the art of the development of systems capable to reason about the traffic behavior and evolution in similar terms to an expert traffic operator. This type of systems may not be conceived to replace the human operator but to act as intelligent assistants that cooperate in the task of defining and applying traffic control decisions. Such concept of system is that of the Intelligent Traffic Management System (ITMS); a system that embodies a knowledge model of traffic behavior at a strategic level. As this type of knowledge is built from theoretical frameworks together with common sense criteria for making reasonable assumptions, specific methods and techniques have been developed along the last twenty years. The concepts are shown and validated by 2 case studies, the KITS system`s approach and the FLUIDS intelligent manegement approach. Keywords: TRAFFIC MANAGEMENT, INTELLIGENT ARCHITECTURES, ARTIFICIAL INTELLIGENCE, KNOWLEDGE MODELLING THE TRAFFIC INFORMATION MANAGEMENT CONTEXT Traffic management is generally subdivided into two different classes: (i) direct control measures using traffic lights and variable message signs and (ii) indirect control measures like recommendations for the drivers by means of VDS (variable direction signs and text panels), warning messages (via broadcast, RDS/TMC or handy-based services), pretrip information (e.g. via Internet) and individual driver information systems. The emphasis for classical traffic management is on the direct control measures including indirect control through VDS and text panels. Whereas in urban control the focus lies on traffic light control, there a various options for urban- and interurban motorway control. The global architecture of traffic control systems bases on the detection of traffic data and environmental data. Several detection sites are assigned to an outstation that controls isolated control instances. Several outstations are connected to a sub-center that are connected to a traffic control computer center (TCC). The TCC coordinates all control measures as well as interacts with the police to manage congestion situations. For an enhanced congestion management the TCC generates the information, predictions and recommendation that allows that the roads could be used more intelligently [1]. The detected traffic data enable the automatic detection of traffic incidents. Intelligent analysis and prediction algorithms monitor the current traffic situation in the relevant granularities. This situation analysis and prediction allows the automatic derivation of network control measures. Based on ascertained traffic situations incident messages are generated and optimized by grouping equal conditions of related sections as a summarized message. The coding within the European standards DATEX and ALERT-C allow the application all over Europe. ESIT 2000, September 2000, Aachen, Germany 36

2 Furtheron, the Traffic Information Center TIC is responsible for the generation and dissemination of traffic information for indirect traffic control. Fundamental to the implementation of traffic information and mobility management services on a Europe-wide scale is the deployment and networking of regional Traffic Information/Control/Service Centers responsible for integration of information, coordination of mobility and provision of travel related information and services to the citizens [2]. This is currently being implemented in several sites across Europe, realizing the value added information and service chain. BASIC TECHNOLOGIES FOR KNOWLEDGE MODELLING The application of knowledge-based and Artificial Intelligence systems to traffic management operations has been an active research area in the last decade [3]. There are several reasons calling for the introduction of such systems into an integrated road transport management system. Firstly, current traffic management and control systems show limitations when facing critical traffic conditions and congestion. This is the approach followed by systems like SCOOT [4], UTOPIA [5] and others. This is an almost permanent problem in most large-/mid-sized urban centers in Europe, which is usually caused by a locally conceived analysis of traffic behavior, and requires more strategic, high-level control methods to be developed. Secondly (and for this reason) the role of operators of traffic management centers is still crucial in day-by-day operations i.e. no matter how sophisticated and advanced traffic control technology is, the man in the loop paradigm is still a prevailing operational condition in most centralized traffic control systems. Thirdly, the introduction and progressive integration of extended traffic monitoring and management facilities in the new generation of traffic management architectures (e.g. improved monitoring systems, incident detection, collective and individual route guidance systems, etc.) demand for increased, on-line operator support tools to help coping with the complexity of both the information managed and of the resulting, integrated traffic management schemes. In this context, the development of systems capable to reason about the traffic behavior and evolution in similar terms to an expert traffic operator is then required. This type of systems may not be conceived to replace the human operator but to act as intelligent assistants that cooperate in the task of defining and applying traffic control decisions. Such concept of system is that of the Intelligent Traffic Management System (ITMS); a system that embodies a knowledge model of traffic behavior at a strategic level. As this type of knowledge is built from theoretical frameworks together with common sense criteria for making reasonable assumptions, specific methods and techniques have been developed along the last twenty years. In the early times, knowledge management techniques followed a uniform approach based on the basic principle of separating knowledge and inference. In this sense, a collection of declarative knowledge representation formalisms such us production rules, Horn clauses, semantic networks, frames, constraints, etc., together with predefined inference engines like backward/forward rule chaining, pattern matching methods for frame-based representations, pattern matching methods for network exploration, constraint satisfaction algorithms, etc. was considered. Then, the development of a so called first generation knowledge-based system (KBS) began, after a knowledge acquisition stage usually based on personal interviews with a domain expert, with the identification of the suitable knowledge representation formalism and the corresponding inference mechanism. However, the experience in application development showed that this initially simple approach did not fit enough with the complexity of the real world knowledge. Basically, the problems came from the lack of: (i) an explicit representation of the reasoning strategies of the system in a more abstract language than the one imposed by a particular representation formalism, and (ii) a clear distinction between the different types of knowledge used in that reasoning. In the current state of knowledge engineering, a knowledge model is conceived as a hierarchically structured problemsolving model. This approach emerges from the observation that a problem domain can be naturally decomposed in substructures, each of them specialized in the solution of a class of problem. In its turn, each of these substructures may be decomposed in other more simple ones specialized in different issues of the same class of problem. In this way, a complex system may be designed as a modular and hierarchical organization of generic tasks joined to a corresponding problem solving method capable to perform the task. This approach was followed in the KITS [6] and TRYS [7] systems, described in the next section. The design of a complex KBS for a real world problem usually implies the characterization of several classes of problems to be solved. In some cases the way of solving a task may be obtained just from a simple process whereas in other cases a deeper description with more conceptual contents may be required and consequently a more complex method may be used. In its turn, the performance of the method selected may require the solution of several subtasks that again may be realized by different methods and so on, up to a lower level with basic inference methods that can not be further decomposed. Looking at the whole picture of this collection of tasks, methods, subtasks, basic inference methods and knowledge bases the size and complexity of the knowledge model is clearly shown, defining what could be named a Problem Solving Medium as shown in ESIT 2000, September 2000, Aachen, Germany 37

3 Figure 1. In operational terms, the presence of multiple methods to perform a task forces to make a decision about the problem solving method to be applied. However, if all the problem solving the possibility of dynamically selecting the adequate method according to the operation context should be considered. This approach (applied in the FLUIDS system described below [8]) constitutes an advanced knowledge modeling solution that contributes to improve the usersystem interaction level and supports the design of systems with new features of intelligent behavior. High level tasks to be performed by the system Optional methods for the same task Basic subtasks performed by the methods Optional methods for the subtasks PROBLEM SOLVING MEDIUM Basic methods performing the more simple subtasks Domain structure containing conceptual vocabularies and knowledge bases supporting the reasoning steps of the basic methods Figure 1: The Problem Solving Medium CASE STUDIES: KNOWLEDGE-BASED AND INTELLIGENT TRAFFIC MANAGEMENT This section presents the application of advanced knowledge modeling techniques on three ITMS with a common general purpose: to develop AI-based tailorable traffic management tools to be integrated in the current traffic management architectures and TCCs and with an additional functional level in current TCCs, i.e. a "traffic knowledge processing layer" developed on top of the existing traffic control facilities. The ultimate goal of this layer is to improve on-line traffic monitoring and management along two main directions: (1) strategic management and intelligent supervision of traffic control strategies within single subsystems, and (2) integrated management of different traffic control services. The approach can be seen in the wider perspective of the multi-level design strategy currently followed in both the European [9] and American [10] traffic management architectures. ITMS processing layer knowledge level knowledge (time scale: min.) processing L representation L inference knowledge strategic traffic management - typical time horizon min evolutionary models - long term adaptation of KBs as knowledge on the area changes data level data processing (time scale: sec.) L signal control programs L databases optimisation mathematical modelling sensor/actuator level L sensors L signals signal processing (time scale: msec.) ESIT 2000, September 2000, Aachen, Germany 38

4 Figure 2: ITMS operation level in traffic management architectures This knowledge layer supports development and on-line use of models of knowledge and expertise of traffic operators on the controlled area. The embedded knowledge models allow improving several traffic monitoring and management operations, including: estimation of traffic load levels in space and time all over the network analysis and understanding of traffic demand and routes in the area qualitative prediction of demand and routes detection (prediction) of critical traffic situations and bottlenecks selection and implementation of congestion avoidance/reduction strategies management of conflictive control objectives and priorities in the different controlled areas A fundamental characteristic of this layer is that thanks to the explicit knowledge modeling approach, the knowledge and thus the 'competence' of the system - i.e. the traffic control criteria and strategies applied by the system - can be modified and improved over time as new knowledge is gained about long-term modifications of traffic behavior through day by day operation of the traffic network. In the last decade the knowledge-based approach has been applied to develop several traffic management systems in real-life environments: KITS [6,13], CLAIRE [11], TRYS [7, 14], FRED [12], FLUIDS [8, 15].. Next, the KITS and FLUIDS systems are described. KITS addresses motorway and urban traffic management following an architectural approach with interesting concepts, whereas FLUIDS faces private traffic management in urban areas. These systems present a common feature: the knowledge-based approach applied is based on the aim of providing a generic tool (i.e. a platform) which could be used and easily adapted to generate specific application models for specific application sites with the provision of the corresponding site specific knowledge. THE KITS APPROACH KITS is a knowledge-based modeling environment supporting knowledge acquisition, build up and on-line operation of knowledge-based management models. The KITS system was developed in the period under the EC research programme ATT/DRIVE and validated with several prototype models in different European sites including Cologne, Trondheim, Genoa and Madrid. Two fundamental structuring principles guided the knowledge modeling approach in KITS: Functional organization, where problem solving domain knowledge is functionally decomposed in terms of a set of specialized units, each of which able to solve a specific type of problem typical in the domain. Topological organization based on the hierarchical approach to traffic network analysis and control. The organization of knowledge and reasoning matches the topological organization of the traffic network and its spatial break down into a set of distinct problem areas. This implies that knowledge and reasoning related to different parts (problem areas) are kept in distinct elements. From the application of the previous principles, any KITS supported application model consists of a structured collection of knowledge units, providing specialized knowledge and reasoning mechanisms required to deal with the different types of traffic management activities and reflecting the functional and topological knowledge structuring views previously described. Three basic types of knowledge units were considered: s, Actors and Supervisors. Depending on the specific requirements of the application area, a KITS model will be in general comprised of several KITS Actors and Supervisors, each of them integrating different KITS s. In summary,, Actor and Supervisor units provide the building blocks for the KITS knowledge architecture and identify the basic compositional levels underlying the architecture of any application model. KITS s are the lowest level knowledge units in KITS, representing the basic functional capabilities of the KITS knowledge model. They implement generic tasks - in the sense of Chandrasekaran [16, 17] - and are able to perform fundamental traffic supervision and management tasks. In this sense, KITS s meet the functional decomposition view within the knowledge modeling approach supported by KITS. ESIT 2000, September 2000, Aachen, Germany 39

5 Several types of s have been considered and designed during the KITS project, with the aim of building up a library of generic tasks to support traffic network supervision and management. These address several of the main tasks and operations underlying the decision process, including: Data completion. This performs the interpretation of the available data and information on traffic in the area. Observable data usually provide only a partial view of traffic state, due to insufficient number, location, etc. of sensors. Knowledge models are then required to complete the partial picture conveyed by monitoring equipments. Data completion is to be understood in two respects: (a) for rejecting incorrect or unplausible data (e.g. due to malfunctioning of sensors), and (b) for information estimation when data is not available (e.g. missing or faulty detectors). Problem identification.. This evaluates and interprets the available information, including both data collected by automated operations and information provided by inference, using flow estimation knowledge. This knowledge allows understanding the current situation, with the aim of identifying current or potential short-term problems, and involves heuristic classification criteria based on a priori historical knowledge about recurring traffic problems in the area. Flow behavior modeling and causal analysis. This performs a short-term prediction of likely evolution and causal explanation of detected problems supported by deeper models of traffic behavior. Local control decision. This support decision processes generating suitable control actions local to a problem area based on the knowledge and results from the above s. Inconsistency Detection. This identifies inconsistent combinations of control actions related to overlapping problem areas. Strategy Completion. This generates control plan proposals for the whole traffic network based on the general view of the situation in the network as a result from the synthesis of local analyses and control proposals obtained for the problem areas. The first four types of s are used to perform the local reasoning behind monitoring and management decisions in a problem area, whereas the last three are applied to perform global synthesis of management actions related to the task of control coordination over the different problem areas. A KITS Actor is a knowledge unit specialized in traffic evaluation and management within a particular area in the traffic network. Actors are linked to problem areas i.e. each Actor is looking at traffic within a specific problem area and have goals which are specific for the areas they are responsible for. So, they meet the topological decomposition view within the KITS knowledge modeling approach. They are built up with the lower level generic tasks represented by the s, and include reasoning strategies which use the s to solve interpretation and decision problems in the area. The basic elements characterizing an Actor include: a) the problem area (subnetwork) managed by the Actor b) the set of s providing the functional capabilities and knowledge required by the tasks to be solved in the area c) an inference structure describing the reasoning strategy implemented by the Actor; this is defined in terms of the rules of activation of the different s, as required by the reasoning path implemented by the Actor d) an inference engine interpreting the inference structure to obtain Actor conclusions. KITS Actors are essentially combinations of various s and reasoning strategies, and different organizations and levels of generality are possible. An Actor may be specific or generic depending on the type of problem to be solved. KITS Supervisors represent the highest level tasks defined in a KITS model. They are responsible for building up an overall, consistent interpretation of the conditions of the traffic network and for achieving a synthesis of decisions and action proposals produced at the level of problem areas by the relevant Actors. Like Actors, KITS Supervisors are knowledge units introduced to reflect the topological distribution of traffic analysis and management knowledge - i.e. the clustering of knowledge with respect to the different parts and areas of the traffic network - and the hierarchical approach to analysis and control decision. Since different solutions may be obtained as regards the distribution of knowledge and tasks among the different Actors a KITS model is comprised of, different concepts can be introduced for the Supervisor unit. KITS has developed three different conceptual approaches: a strong version of the Supervisor concept - i.e. the Supervisor as a Master - where the Supervisor acts in a prescriptive way imposing decisions to the Actors in case of conflicting proposals generated by different local units; a weaker version i.e. Supervisor as a Mediator - where the Supervisor tries to negotiate ESIT 2000, September 2000, Aachen, Germany 40

6 conflicting proposals coming from different Actors by presenting further constraints for local decisions and tries to facilitate convergence to a common solution; the weakest version i.e. the Supervisor as a Facilitator where the Supervisor acts as an information manager who as the knowledge about which Actor has the external knowledge eventually required by each other Actor to perform local tasks. As regards these three different views of the Supervisor unit, specific modeling choices can be taken, in any given application, only on the basis of the specific characteristics and requirements of the application area. For instance, one can envisage that strong versions of the Supervisor unit are feasible when clear priorities are available to manage incompatible and conflictive objectives in different areas. Based on the above building blocks, a KITS application model architecture is build up by instantiation and combination of several KITS units. In general, depending on the particular knowledge and engineering analysis of the application area, the KITS model will be comprised of several s, Actors and Supervisors. A typical model architecture may be like the one described in the following Figure 3. KITS model Supervisor 2 Supervisor 1 Actor A1 Actor A2 Actor A3 A1.1 A1.2 A2.1 A2.2 A3.1 A3.2 Problem Area 2 Problem Area 3 Problem Area 1 Traffic network model Figure 3: Example of a KITS model architecture The KITS approach was validated and tested in several sites, including Cologne, Madrid, Genoa and Trondheim, as well as in the framework of the EU THERMIE project JUPITER in Florence [18]. Basing on KITS concepts, the TRYS is a knowledge modeling environment supporting models to perform traffic management at a strategic level in urban, interurban or mixed areas. The structuring principles underlying the TRYS knowledge modeling approach were similar to those used in KITS. The first version of the TRYS environment was developed from 1991 to 1994 and funded by the Spanish Directorate for Traffic (DGT). It was validated with several prototypes for the periurban rings and main accesses to Sevilla, Madrid and Barcelona and at present updated versions of the original systems are being exploited in the TCC s of Madrid and Barcelona [14]. THE FLUIDS APPROACH FLUIDS is a knowledge-based modeling environment of intelligent interfaces for decision support [16]. The FLUIDS system was developed in the period under the EC research programme Telematics Applications and validated with three prototype models in Turin and Barcelona, one dealing with public transport management and the other two with private traffic management in urban and periurban networks respectively. The FLUIDS approach to intelligent interfaces emerges from the experience in projects like KITS and TRYS. Due to the increasing functionalities provided by computing systems managing complex information infrastructures, like KITS and TRYS, it was observed the need of improving user-system interaction capabilities and operator acceptance by balancing transparency and interactivity of system behavior and user attitudes. In this sense, FLUIDS aims to support the development of intelligent interpreters capable to bridge the gap between the conceptual models of the users and the systems. This approach led to the conception of a knowledge-based problem solving environment from a conversational framework where both informative and explanatory type questions are considered in such a way that the user gets from the system not only answers but answers adapted to the characteristics of the user and/or the dialogue. In this sense, the underlying principle of the FLUIDS intelligent interface concept is the need of reasoning about how to communicate with users as a requirement to decide how to solve problems [19]. ESIT 2000, September 2000, Aachen, Germany 41

7 Three main classes of questions were identified to configure the user-system conversational framework required in decision support: What is happening.. These questions ask for descriptions of the current situation at different levels of abstraction. These descriptions use more advanced concepts than the simple ones provided by the underlying information system obtained from a data abstraction process. What may happen if <environment conditions, decision actions>. This type of questions looks for answers about the potential short-term evolution of a situation considering different hypothesis of external scenarios. What to do if <environment conditions, decision actions>.. The answers to this type of questions aim to provide suggestions about control actions able to improve problematic situations under different assumptions of external scenarios. The analysis of any of the previous types of questions is directly related to the identification of the class of problem that needs to be solved in order to obtain an answer. Every problem represents a task to be performed, like for instance, problem detection, problem diagnosis, prediction, and evaluation of control actions or suggestion of plans to solve problems. The way of realizing any of these tasks may be in principle performed by different problem solving methods, offering in this way the possibility of getting different answers for the same question. Then, the selection of the most adequate method to perform a task results from a reflective process guided by considerations related to the characteristics of the conversation and the problem solving capabilities available. According to the previous considerations, the FLUIDS architecture approach distinguishes three main components: Presentation Manager specialized in the input-output activities of the system Problem Solving Medium that contains a knowledge model consisting of a structured collection of automatic tools implementing the problem solving functionalities of the system usable to generate the answers Conversation Manager, which plays the role of an intelligent interpreter between the user needs and the system capabilities, and is supported by a metalevel knowledge model used to decide on the fly the elements of the Problem Solving Medium that should be applied to obtain the required answers. This division between the problem solving knowledge and the way of using this knowledge qualifies the system for reflecting about its own capabilities and deciding about the best way of solving the problems according to the needs of the conversation. This approach on the dynamic selection of methods constitutes a novel aspect in the sense that the adequacy of the methods is assessed not only in terms of their applicability but also considering their suitability to fit the conversation characteristics. The right way of using the Problem Solving Medium in order to configure the inference structure that should generate an answer is the responsibility of the Conversation Manager. With this aim, the Conversation Manager applies a metalevel knowledge model in two stages: Analysis of the conversation.. First, taking into account the current question, the recent dialogue and possibly interaction preferences specified by the user through the user interface the set of criteria that should guide the selection of the problem solvers is established. These criteria take into account not only considerations about the information context where a method has to be applied but also peculiarities of the methods related to conversation requirements, such us level of abstraction of the output, level of assistance, precision degree, possibility of providing explanations, etc. Design of the inference structure.. Next, a routine design method is applied to configure, in a top-down fashion, the reasoning strategy to be followed selecting the problem solvers that satisfy as much as possible the criteria established in the first step. Once the inference structure has been determined, the corresponding tools in the Problem Solving Medium are applied to get the answers with their associated explanations. CONVERSATION MANAGER Question Presentation Manager Generic tasks associated to the question Analysis of the conversation Problem Solving Medium Criteria to navigate in the PSM Execution of the problem solving structure Problem solving structure Answer ESIT 2000, September 2000, Aachen, Germany 42

8 Figure 4: Reasoning line followed to generate the answers to user questions The FLUIDS approach was tested with three applications being one of them a private traffic management system for the city center in Turin. At present, this management is performed with the UTOPIA control system [5] that provides general control directives to a collection of subsystems, called SPOT units, responsible of automatically and dynamically regulate the control devices in the intersections of the traffic network. The role of the FLUIDS system in this context was to automatically supervise the state of the traffic network and the SPOT units and alert the operator about the presence of current or foreseeable problems, the possible causes of these problems, and control actions proposals oriented to solve these problems as well as adjustments to the internal parameters of some SPOT units if required. According to these general goals, the operator-system conversation model had to support the following classes of questions: What is happening questions related to the state of the traffic network at different levels: areas, links and intersections and the implication of the SPOT units in this situation. Why does it happen questions asking for the diagnosis of causes which explain the problems currently happening. What may happen questions looking for estimations of the potential short term evolution of the traffic network under specific environment conditions or as an impact of the application of a particular control action proposed by the SPOT units in the past. What to do questions asking for suggestions of control actions that may improve the current or foreseeable situation under different scenarios of resources availability. Furthermore, the previous questions may be followed by Why type questions, asking for explanations which support the results supplied by the application. The generation of the answers for the previous questions requires four main knowledge types. Classification knowledge:used to identify the current state of the system, either in terms of the current incidents or congestion, excess demand or low performance of the active traffic control that may cause the beginning of congestion in a short-term scenario. Diagnosis knowledge: Supports the diagnosis of congestion problems and low performance of the traffic signal. It includes knowledge about patterns of evolution that lead to congestion and a traffic assignment model. Prediction knowledge: If no congestion has been observed but some of its possible causes have been detected in the current state, the short-term evolution of these causes needs to be analyzed in order to confirm or discard the foreseeable presence of a problem. The user may inform the system about different hypotheses of traffic demand in order to obtain alternative forecasts. Control proposals knowledge: Given that traffic problems may be caused, at present or in the future, by a low performance of the UTOPIA control system, there are two kind of control actions that could be applied: (i) the temporary modification of the control cycle of some intersections and (ii) the calibration of part of the SPOT models (e.g. modification of nominal parameters, warnings to the maintenance groups, etc.). In addition, specialized reflective knowledge is used to dynamically select one of the possible methods to perform the required tasks that is also part of the knowledge structure model. The previous knowledge is hierarchically organized in a Problem Solving Medium, defining in some cases alternative methods for the same task (e.g. shallow classification, heuristic classification or look-ahead classification, shallow diagnosis vs. model-based diagnosis). In a certain moment, the inference structure to be used to get an answer is obtained applying a routine design strategy in a top-down fashion on the Problem Solving Medium structure. This design process is supported by specialized reflective knowledge associated to every task regarding the criteria to select among the problem solving methods available, the most appropriate one according to the context of the dialogue. This FLUIDS application was tested during several months by the usual operator of the UTOPIA system, finding interesting functionalities and real possibilities of improving the effectiveness of the decision support task, in spite of the operation complexity attached to this kind of applications. CONCLUSIONS The paper has given an overview on intelligent management system for traffic control and coordination tasks. It has been shown that the concepts of Intelligent Traffic Management Systems (ITMS) are designed to act as intelligent assistants that cooperate with the traffic engineer in the task of defining and applying traffic management decisions. This management decision support takes place on different management levels. On control level, ITMS support the operation of traffic control systems by a comprehensive analysis of the current and expected traffic situation, as shown ESIT 2000, September 2000, Aachen, Germany 43

9 in the KITS case study. On management level, ITMS can support the decision makers to manage complex or intermodal traffic situations as shown in the FLUIDS case study. As far as the knowledge representation concepts are already far progressed by several research and development projects within the last 10 years, as problematic is still the application of this very advanced technology. The acquisition of the knowledge needed for this kind of decision support systems as well as the training of traffic engineers and traffic operators to handle such kind of systems is still a topic to be researched in the next time. ACKNOWLEDGMENT Most of the work reported in this article is largely based upon the activities, results and documentation produced during and after the KITS, TRYS and FLUIDS projects. As such it owes much to all partners and especially to Professor José Cuena (1999 ), from the Technical University of Madrid, who was a main inspirator and provided the conceptual and scientifical foundations of these systems and models. None of the results and achievements with the KITS, TRYS and FLUIDS systems would have been possible without his continuos contributions, precious suggestions, strong and friendly support and enthusiasm. This work is dedicated to his memory. REFERENCES [1] Kirschfink, H.; Ziegler,U.: "Congestion Management concept for traffic computers in North Rhine-Westphalia as part of the Eurotriangle project", XIIth Congreso Mundial IRF: Madrid 1993, Spain. [2] Klinge,L., Engels, J., Kirschfink, H.: Analysis of User Requirements defining the System Architecture and Functionalities of European Traffic Information Centers, 4th World Congress on Intelligent Transportation Systems, Berlin 1997, Germany. [3] Bielli M., Ambrosino G., Marco B. (Eds.): Artificial Intelligence Applications to Traffic Engineering, Utrecht 1994, The Netherland. [4] Hunt, Robertson, Bretherton, Winton, SCOOT. A traffic responsive method of coordinating signals, Transport and Road Research Laboratory, Report No. LR 1014, TRRL, Crowhome 1981, UK. [5] Mauro V., Di Taranto C., UTOPIA, IFAC/IFIP/IFORS Conference on Control, Computers and Communications in Transport, Paris 1989, France. [6] Boero M., Cuena J., Kirschfink H., Krogh C., KITS: A general approach for knowledge-based traffic control models, Technical Days on Advanced Transport Telematics, Brussels 1993, Belgium. [7] Cuena J., Hernández J., Molina M., Knowledge-based models for adaptive traffic management systems Transportation Research, Part C, Issue 3 (5), [8] Cuena J., Hernández J., Molina M., Advanced user interfaces for decision support in real time transport management, 5th International Conference on Applications of Advanced Technologies in Transport Engineering, ASCE 98, pp , American Society of Civil Engineering, 1998, USA. [9] Bolelli, Leighton, Morello, "A top-down approach to open European IRTE architecture: requirements, development paths and assessment methods", 1st World Congress on Applications of Transport Telematics & Intelligent Highway Systems, pp , Paris 1994, France. [10] Gartner, Stamatiadis, Tarnoff, "Development of a real time traffic adaptive control strategies for IVHS", 1st World Congress on Applications of Transport Telematics & Intelligent Highway Systems, pp , Paris 1994, France. [11] Scemama, CLAIRE: A context-free AI-based supervisor for traffic control, Bielli M., Ambrosino G., Boero M. (eds.), Artificial Intelligence Applications to Traffic Engineering, pp , [12] Ritchie, Prosser, A real-time expert system approach to freeway incident management, Transportation Research, record 1320, pp. 7-16, [14] Hernandez, J.: Real-time Traffic Management through Knowledge-based Models: The TRYS approach, ERUDIT Tutorial on Intelligent Traffic Management Models ( Helsinki 1999, Finnland [15] Hernandez, J.: An Intelligent model for real-time private Traffic Management in Uran Networks: The FLUIDS/CRITIC approach, ERUDIT Tutorial on Intelligent Traffic Management Models ( Helsinki 1999, Finnland [16] Chandrasekaran B., "Towards a taxonomy of problem solving types", Artificial Intelligence Magazine 4 (1), pp. 9-17, [17] Chandrasekaran B., "Generic tasks in knowledge based reasoning: High level building blocks for expert systems design", IEEE Expert, [18] Ambrosino G., Boero M., Niccolai, Romanazzo, Turrini, Urban control services integration: The innovative components of the THERMIE-JUPITER architecture in Florence, 4th International Conference on Applications of Advanced Technologies in Transportation Engineering, Capri, ESIT 2000, September 2000, Aachen, Germany 44

10 [19] Hernández J., Modelos flexibles de conocimiento estructurado para soporte de interfaces inteligentes para ayuda a la decisión (Flexible models of structured knowledge to support intelligent interfaces for decision support), Ph.D. Dissertation, Technical University of Madrid, ESIT 2000, September 2000, Aachen, Germany 45