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

    Exploratory Study of Distracted Behaviors of Transit Operators

    Thumbnail
    View/Open
    Arbie_N_T_2014.pdf (7.093Mb)
    Downloads: 1493
    Supporting documents (1.591Mb)
    Downloads: 274
    Date
    2014-08-30
    Author
    Arbie, Nurlayla
    Metadata
    Show full item record
    Abstract
    Bus transit driving is an occupation that requires high concentration in driving and is demanding due to work overload, time pressure, and responsibility for lives. In 2006, there were 103 fatal crashes involving transit buses. As the number of distraction-related crashes increases, it is important to conduct a transit distraction study to reduce future crashes. This thesis focused on the analysis of the likelihood of the operator distraction behaviors and the analysis to find a predictive model to classify different distraction categories. An ordinal logistic regression was carried out to evaluate how age, gender, driving experience of the operators, and their driving frequencies accounts for the likelihood of 17 potential distracted driving behaviors. The results of this analysis showed that there were only 5 best models (p-value of model fit less than 0.005 and p-value of parallel line test more than 0.005) that could be constructed, including: listening to the radio/ CD/DVD/MP3 player (D1); picking Up and Holding 2-way Radio (D5); listening to the Dispatch Office broadcast (D6); adjusting switches/controls on dashboard (D15); and utilizing mentor ranger (D16). On the other hand, a discriminant analysis was performed to predict how different transit operator driving behaviors when exposed by 10 different distraction activities and 16 predictors were considered in this analysis. The final results showed that there are 4 predictors that seem to be able to classify distraction groups across all 4 models; those include segment length, average duration of idling time/stop delay at speed interval 0—4 km/hr, frequency of speed transitions that deviate by ± 0 to 4 km/hr from its speed, and frequency of speed transitions that deviate by ± 8 to 12 km/hr from its speed.
    URI
    http://hdl.handle.net/10919/50433
    Collections
    • Masters Theses [20811]

    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