Virginia Tech
    • Log in
    View Item 
    •   VTechWorks Home
    • ETDs: Virginia Tech Electronic Theses and Dissertations
    • Doctoral Dissertations
    • View Item
    •   VTechWorks Home
    • ETDs: Virginia Tech Electronic Theses and Dissertations
    • Doctoral Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    A Framework and Analytical Methods for Evaluation of Preferential Treatment for Emergency and Transit Vehicles at Signalized Intersections

    Thumbnail
    View/Open
    louiselldissertationv2.pdf (1016.Kb)
    Downloads: 124
    Date
    2003-04-08
    Author
    Louisell, William
    Metadata
    Show full item record
    Abstract
    Preferential treatments are employed to provide preemption for emergency vehicles (EV) and conditional priority for transit vehicles at signalized intersections. EV preemption employs technologies and signal control strategies seeking to reduce emergency vehicle crash potential and response times. Transit priority employs the same technologies with signal control strategies seeking to reduce travel time and travel time variability. Where both preemption and transit technologies are deployed, operational strategies deconflict simultaneous requests. Thus far, researchers have developed separate evaluation frameworks for preemption and priority. This research addresses the issue of preemption and priority signal control strategies in breadth and depth. In breadth, this research introduces a framework that reveals planning interdependence and operational interaction between preemption and priority from the controlling strategy down to roadway hardware operation under the inclusive title: preferential treatment. This fulfills a current gap in evaluation. In depth, this research focuses on evaluation of EV preemption. There are two major analytical contributions resulting from this research. The first is a method to evaluate the safety benefits of preemption based on conflict analysis. The second is an algorithm, suitable for use in future traffic simulation models, that incorporates the impact of auto driver behavior into the determination of travel time savings for emergency vehicles operating on signalized arterial roadways. These two analytical methods are a foundation for future research that seeks to overcome the principal weakness of current EV preemption evaluation. Current methods, which rely on modeling and simulation tools, do not consider the unique auto driver behaviors observed when emergency vehicles are present. This research capitalizes on data collected during a field operational test in Northern Virginia, which included field observations of emergency vehicles traversing signalized intersections under a wide variety of geometric, traffic flow, and signal operating conditions. The methods provide a means to quantify the role of EV preemption in reducing the number and severity of conflict points and the delay experienced at signalized intersections. This forms a critical basis for developing deployment and operational guidelines, and eventually, warrants.
    URI
    http://hdl.handle.net/10919/26820
    Collections
    • Doctoral Dissertations [14904]

    If you believe that any material in VTechWorks should be removed, please see our policy and procedure for Requesting that Material be Amended or Removed. All takedown requests will be promptly acknowledged and investigated.

    Virginia Tech | University Libraries | Contact Us
     

     

    VTechWorks

    AboutPoliciesHelp

    Browse

    All of VTechWorksCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    Log inRegister

    Statistics

    View Usage Statistics

    If you believe that any material in VTechWorks should be removed, please see our policy and procedure for Requesting that Material be Amended or Removed. All takedown requests will be promptly acknowledged and investigated.

    Virginia Tech | University Libraries | Contact Us