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    INTELLIGENT INTEGRATED TRANSPORTATION SYSTEMS  Andrzej Adamski  Institute of Automatics – Cracow University of Mining and Metallurgy  Institute of Construction and Transport Management- Cracow University of Technology E-mail: E-mail:  1. INTRODUCTION The traffic volume world-wide trend is well known: the volume of traffic is rising, the demand for mobility is increasing. In consequences: congested town centre’s loose their attractiveness, tailbacks, noise pollution and exhaust fumes. An enlargement of existing traffic facilities is no longer ecologically and economically justifiable. It seems that the current and future traffic problems in cities and their surroundings are soluble only through intelligent integrated transportation systems. There are several natural and formal integration and intelligence premises.  Integration premises : Firstly, transportation systems features: large-scale systems, complex traffic phenomena (randomness, uncertainty, structural instability, essential non-linearity’s, multicommodity), complex inter and intra networks interactions (diversified dynamics, human behavioural anisotropy), broad spectrum of multigoal decision making (management, surveillance) and control tasks with specific demand behavioural feedback reactions i.e. the control plant is intelligent, low system robustness to interactions (“control by opportunity modes”). In such systems the overall system efficiency measures are transformed on a many interconnected decision making tasks (control, scheduling, supervision, management). In conclusion, the integrated character of the   system decision and control tasks, formulated in terms of elements of a Multilayer and Multilevel Space of Integration is a natural approach to transportation systems problems. Secondly, nowadays technological premises: Information Age which transforms travellers daily activities and altered the decision-making processes, Computer technology progress over the last years stimulates the development of distributed system architectures, Communication revolution is advanced    with future development perspective expressed by Gilder’s Law: communication costs will halve, and speed will double every 18 months. Concluding, Information and Communication create the formal base for transportation systems integration.  Intelligence premises : Formally we can say that ITS is an example of integration of a number of technologies including: information processing, communication, control and electronics to provides the intelligent link between travellers, vehicles and infrastructure, thus ITS offers the integrated solution of transportation  problems. The application of knowledge-based (KB) intelligent traffic management, supervision and control systems is presently motivated by several essential reasons. The traffic control problems are usually ill-structured and are not amenable to be formulated and solved by purely algorithmic techniques like the AI-techniques. The requirements of real-time data and knowledge-intensive stages of automation and supervision deals with the uncertain, unforeseen, heuristic and fuzzy information. The conventional tools fail to  process such information then the new DSS enabling us to understand of traffic evolution on time-space scene are presently necessary. In the paper new potential of various integration   565  and intelligence mechanisms in the integrated transportation systems are presented and illustrated by wide spectrum of generic practical examples. 2. MULTILAYER AND MULTILEVEL SPACE OF INTEGRATION (MMS) The Integrated Transportation Management Systems with endeavours to integrate all modes and all roads into a „system of systems” focus the interest of researches, in consideration of  benefits anticipated within the transport community in elevating current transportation systems to an integrated operation. Benefits over the traditional systems results from: ã   area wide synchronised response to the full range of mobility needs and provides an opportunity to bridge multi-modal planning and real-time scheduling work with day to day inter-modal operations, ã    provision of area-wide transportation demand management, surveillance and control strategies to be deployed both in the real-time, and as strategic interventions to longer travel demand patterns, environment degradation trends and policy of investments, ã   opportunity to development of efficient multi-layered technical architecture for the systems found within the many operations centres, with provision of open system standards for their operating systems (i.e. promotion to use of portable, inter-operable, and vendor neutral architectures), successful integration via data management (i.e. the mechanism to share data between systems and users) and IPC (Inter-Process Communication) process enabling real-time interfaces detailing the manner in which existing systems are integrated. The most of the decisions problems in transportation systems can be in natural way formulated in terms of elements of Multilayer (e.g. process-control-scheduler-supervisor-managing-planning-coordination) and Multilevel (e.g transportation infrastructure-subsystems -networks-subnetworks) of Integration (Fig. 1). In this MMS space the integrated task is formulated by knowledge-based DSS in terms of available resources, control, supervision, management and planning actions distributed between different levels and layers. The following approach is proposed: Formulate general objectives of the Transport Policy according to principles: triad “I” (Integration, Information, Intelligence) and triad of “E” (Efficiency, Economy, Ecology) and specify the data and knowledge requirements. The examples of public transport, individual traffic, pro-ecological, parking options are presented in (Adamski 2000). Prepare the knowledge model for MMS space and formulate integrated tasks. The ATMS integration subsystems proposals are generated and supplemented by PIACON (Adamski 1998, 2000) related management and control integrative tools. 2.1. Integration mechanisms   In general integrated approach is required to combine different subsystems, to represent adequately interactions between subsystems and environment, to formulate sensible overall system objectives for synchronisation, co-ordination and harmonisation tasks, to support high-level decision making tasks e.g. management, supervisory, diagnostic, to carry out intelligent, multi-criteria and adaptive traffic control actions, to realise the principle: think globally act locally„. In transportation systems the integration may concern different system aspects: Integration of subsystems: UTC, PT, PGM, TTS, RGT, ATIS, radio RDS-TMC, TV, VMS. Integration of information (Fig.2). Integration of systems functions and computational power, Integration of different system soft and hard operational tools, Integration of knowledge and learning tools (Fig. 3.) Meta-system manages the selection, operation and communication of the programs. The main difficulty is concerned with the fact that several expert systems may be used together and solve the complicated problem under the condition of proper knowledge distribution and domain expertise separation. At this point we face the problem of knowledge integration and management. The most 566   promising integration directions in transportation systems concerns (Adamski 1999, 2000): Integration of supervision, scheduling and control functions, Intelligent control (data integration, dynamic intelligent traffic control feedback) and integrated pro-ecological traffic planning and management approach. PROCESS CONTROL   PROCESS SURVEILLANCERESOURCES PLANNING and MANAGEMENT Layer Layer Layer Layer LevelLevelLevelLevel im011 jnMonitorin : detection of smtoms   Dianosis Tools Prediction and behaviour Problem-solvin tools: models, Intellient Su   ervisor actions: VMS,AID,ATIS Suervisor Networks models : hsical, abstract Network problems : Management Tools : exert sstems   Planning and Analysis Transport Supernetwork interactionsControl TasControl methods Data and KB Scheduler: integratorController: hbrid Feedback:  control, Financial Control systems Transort exerts Transport means Systeminfrastructure Environment   2.2. Environment for knowledge-based systems Several environments for KB systems equipped with problem-solving methods, mechanisms working with limited ontological tasks and problem domain, tools for automatic generation and customisation of dedicated knowledge-acquisition are presently available (Table 1) (Bielli et all 1994, Cuena et all 1995, Scemama 1994, Ritchie et all 1995). Typical knowledge model includes formulation of method ontology (i.e. tools used by the method), definition of their domain ontology and establishment of inter-ontology’s mapping relations. The knowledge model developer usually is supported by software environment for building, operationalizing and reusing knowledge models. The whole knowledge model is a hierarchical multi-layer and multi-level structure (see MMSI) of knowledge areas i.e.  bottom primary knowledge area and top whole system knowledge area interfaced by  problem-oriented dynamically connected hierarchical structure of knowledge base integrated components with explicit task perspective in problem solving methods. Knowledge level offers hierarchy of knowledge specifications e.g. orientation, types, architectures, interfaces. In Figs 5-7 an example of integrated pro-ecological traffic  planning, management, and control system is presented (Adamski 1999). Table 1 Examples of KB traffic management tools Interated Task   Fig. 1 Multilayer and Multilevel Space of Integration (MMSI) 567   Name Year Management DSS Tool Testing Area/ Developer ZPD-1 to3 1984-1986 dispatching control Bus depot Cracow/IIA-AGHSAGE/CLAIRE 1986/1994 traffic road congestion TCC Paris/ INRETS AURA/TRYS/KSM1994/96/95 Motorway, UTC Madrid, Barcellona/TUM FRED/FIM/ARTIST1990/1993 Freeway (safety, AIDM) Orange Country/ITS Irvine JUPITER proj. 1996 Urban TCC Florence/EU Thermie CORBA client   CORBA client     C O R B A (distribution, heterogeneity, etc) mediating tier Object Database (OBD) (persistence, transactions , crush recovery)  data store   Fig.2   Symbolic processing   Numerical package 1 Numerical package n   Fig. 3 meta - system   ES EEEE   CONCLUSION The modern transportation systems can reach their full potential of efficiency and synergy only if they are intelligent and appropriately integrated in MMI space. The main determinants of integration should include system architecture equipped with advanced communication means and new generation of traffic detectors to be the consistent, interacting framework for analysis and integration of functions (functional layer), organisation in information flow-data (flow layer), co-ordination and multi-criteria intelligent control decisions -(operational layer), interaction of physical units and components -(physical layer) and subsystems into an overall management and control system. REFERENCES Adamski A. (1999) Integrated Transportation Systems. Proc of the Conference  Modeling and Management in Transportation  , Poznan-Krakow 12-16 October   , vol. 1 ,  pp. 21-34 Fig. 2.   Integrated architecture of OBD andCORBA. Object databases   (OBD ) with   CORBA(Common Object Request Broker Architecture ) standard integrating network of objects of theclient-server type   working in heterogeneous and distributed environment by   ORB (Object RequestBroker)   interfaces and    IDL   (Interface DefinitionLanguage) tools. Fig. 3. Integrated Intelligent System Supervisor ) programs management, reasoning, conflict solution parallel processing, communication. Integration of knowledge and learning tools. Large knowledgeintegration environment that consists of severalsymbolic reasoning systems (expert systems) and numerical computation packages. 568
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