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A Knowledge- Based Decision Support Architecture for Advanced Traffic Management

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  • Ritchie, Stephen G.

Abstract

Fundamental to the operation of most currently envisioned Intelligent Vehicle-Roadway System (IVRS) projects are advanced systems for surveillance, control and management of integrated freeway and arterial networks. A major concern in the development of such Smart Roads, and the focus of this paper, is the provision of decision support for traffic management center personnel, particularly for addressing nonrecurring congestion in large or complex networks. Decision support for control room staff is necessary to effectively detect, verify and develop response strategies for traffic incidents. The purpose of this paper is to suggest a novel artificial intelligence-based solution approach to the problem of providing operator decision support in integrated freeway and arterial traffic management systems, as part of a more general IVRS. A conceptual design is presented that is based on multiple real-time knowledge-based expert systems (KBES) integrated by a distributed blackboard problem-solving architecture. The paper expands on the notions of artificial intelligence and Smart Roads, and in particular the role, characteristics and requirements of KBES for real-time decision support. The overall concept of a decision support architecture is discussed and the blackboard approach is defined. A conceptual design for the proposed distributed blackboard architecture is presented, and discussed in terms of the component KBES functions at an areawide level, as well as at the subnetwork or individual traffic control center level.

Suggested Citation

  • Ritchie, Stephen G., 1990. "A Knowledge- Based Decision Support Architecture for Advanced Traffic Management," University of California Transportation Center, Working Papers qt9818b161, University of California Transportation Center.
    • Handle: RePEc:cdl:uctcwp:qt9818b161
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    Cited by:

    1. S. Aly & I. Vrana, 2006. "Integrating multiple fuzzy expert systems under restricting requirements," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 52(4), pages 187-196.
    2. Kühl, Niklas & Schemmer, Max & Goutier, Marc & Satzger, Gerhard, 2022. "Artificial intelligence and machine learning," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 135656, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    3. Niklas Kühl & Max Schemmer & Marc Goutier & Gerhard Satzger, 2022. "Artificial intelligence and machine learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2235-2244, December.
    4. Bielli, Maurizio & Reverberi, Pierfrancesco, 1996. "New operations research and artificial intelligence approaches to traffic engineering problems," European Journal of Operational Research, Elsevier, vol. 92(3), pages 550-572, August.
    5. Prosser, Neil A. & Ritchie, Stephen G., 1992. "Real-Time Knowledge-Based Integration Of Freeway Surveillance Data," University of California Transportation Center, Working Papers qt3j5655n7, University of California Transportation Center.
    6. Akhtar Ali Shah, S. & Kim, Hojung & Baek, Seungkirl & Chang, Hyunho & Ahn, Byung Ha, 2008. "System architecture of a decision support system for freeway incident management in Republic of Korea," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(5), pages 799-810, June.
    7. Ritchie, Stephen G. & Prosser, Neil A., 1992. "A Real-Time Expert System Approach To Freeway Incident Management," University of California Transportation Center, Working Papers qt432020jq, University of California Transportation Center.

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    Keywords

    Architecture;

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