IDEAS home Printed from https://ideas.repec.org/p/cdl/uctcwp/qt432020jq.html
   My bibliography  Save this paper

A Real-Time Expert System Approach To Freeway Incident Management

Author

Listed:
  • Ritchie, Stephen G.
  • Prosser, Neil A.

Abstract

Fundamental to the operation of most Intelligent Vehicle-Highway System (IVHS) 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 non-recurring 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. These are events that disrupt the orderly flow of traffic, and cause non-recurring congestion and motorist delay. non-recurring congestion can be caused by accidents, spilled loads, stalled or broken down vehicles, maintenance and construction activities, signal and detector malfunctions, and special and unusual events. The ultimate objective of our research is to implement 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 IVHS. In this paper, we present and discuss the development of FRED (Freeway Real-Time Expert System Demonstration), a component prototype real-time expert system for managing non-recurring congestion on urban freeways in Southern California. The application of FRED to a section of the Riverside Freeway (SR-91) in Orange County is presented as a case study, and illustrates the current capabilities of the system.

Suggested Citation

  • 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.
    • Handle: RePEc:cdl:uctcwp:qt432020jq
    as

    Download full text from publisher

    File URL: https://www.escholarship.org/uc/item/432020jq.pdf;origin=repeccitec
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Ritchie, Stephen G., 1990. "A Knowledge-Based Decision Support Architecture for Advanced Traffic Management," University of California Transportation Center, Working Papers qt7qv4w8kj, University of California Transportation Center.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    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.

    More about this item

    Keywords

    Social and Behavioral Sciences;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cdl:uctcwp:qt432020jq. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Lisa Schiff (email available below). General contact details of provider: https://edirc.repec.org/data/itucbus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.