IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v42y2008i5p799-810.html
   My bibliography  Save this article

System architecture of a decision support system for freeway incident management in Republic of Korea

Author

Listed:
  • Akhtar Ali Shah, S.
  • Kim, Hojung
  • Baek, Seungkirl
  • Chang, Hyunho
  • Ahn, Byung Ha

Abstract

Traffic Management Centers play a vital role in efficient functioning of freeway network in the post-incident scenario. As per present practice in Korea, the traffic managers use a heuristic approach for incident analysis based on their experience of similar scenarios. However, this approach induces uncertainty thereby reducing the overall effectiveness of the subsequent incident management and rescue operations. This paper proposes a decision support system to account for these shortcomings. We name our system as 'FIAS' - Freeway Incident Analysis System. The novel idea presented in this paper is the use of historical, real-time and spatial data simultaneously to forecast post-incident traffic flows on a microscopic simulation platform, Cellular Automata. FIAS incorporate two additional rules in the conventional model to depict more realistic incident flow characteristics. This paper focuses on the system architecture of the model and tests its performance by comparing its predicted values with a real incident data. The evaluation results confirm the validity of FIAS as it can model the time dependent microstructure of traffic flows with significant accuracy.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transa:v:42:y:2008:i:5:p:799-810
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965-8564(08)00032-3
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Brahim Herbane, 2014. "Information Value Distance and Crisis Management Planning," SAGE Open, , vol. 4(2), pages 21582440145, April.

    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. 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.

    More about this item

    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:eee:transa:v:42:y:2008:i:5:p:799-810. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    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.