• Login
    • Submit
    View Item 
    •   Repository Home
    • UT Electronic Theses and Dissertations
    • UT Electronic Theses and Dissertations
    • View Item
    • Repository Home
    • UT Electronic Theses and Dissertations
    • UT Electronic Theses and Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Optimal dynamic pricing for managed lanes with multiple entrances and exits

    Icon
    View/Open
    PANDEY-THESIS-2016.pdf (3.093Mb)
    Date
    2016-08
    Author
    Pandey, Venktesh
    0000-0003-0213-702X
    Share
     Facebook
     Twitter
     LinkedIn
    Metadata
    Show full item record
    Abstract
    Dynamic pricing models are explored in this thesis for high-occupancy/toll (HOT) lanes, which are increasingly being considered as a means to relieve congestion by providing a reliable travel time alternative to travelers. The work is focused on two aspects of dynamic pricing: (a) utilizing real-time traffic measurements to inform parameters of the pricing model and (b) developing a optimal pricing formulation for managed lanes with multiple entrances and exits. The first part of the thesis develops a non-linear estimation model to determine the parameters of the value of time (VOT) distribution using real-time loop detector measurements. The estimation model is run on a HOT network with a single entrance and exit assuming the VOT has a Burr distribution. The estimation results show that the true parameter values of a VOT distribution for a population can be learned from loop detector readings measured before and after the toll gantry location. Differing toll profile predictions are observed for different choices of initial conditions. The observability of the collected measurements to estimate the parameters of the model is identified as a primary factor for the non-linear estimation to work in real-time. Further research areas are identified to extend the analysis of using real-time loop detector data for complex HOT networks and for different toll optimization objectives. The second part proposes a dynamic programming (DP) formulation to solve distance-based optimal tolling for HOT lanes with multiple entrances and exits (HOT-MEME) under deterministic demand conditions. The simplifying assumptions made to model HOT-MEME networks found in the literature are relaxed. Two objectives are considered for optimization: maximizing generated revenue and minimizing experienced total system travel time. A spatial queue model is used to capture the traffic dynamics and a multinomial logit model is used to simulate lane choice at each diverge. A backward recursion algorithm is applied, under simplifying assumptions for the definition of the state of the system, to solve for the optimal toll. The results indicate that the DP approach can theoretically determine optimal tolls for HOT lanes with multiple entrances and exits, but further research needs to be conducted for the algorithm to work practically for medium to large size networks. Recommendations are made in the conclusion about how advanced methods can be utilized to tackle the computational constraints.
    Department
    Civil, Architectural, and Environmental Engineering
    Subject
    Managed lanes
    Dynamic pricing
    HOT lane with multiple entrances and exits
    Non-linear estimation
    Loop detector data
    URI
    http://hdl.handle.net/2152/44016
    Collections
    • UT Electronic Theses and Dissertations
    University of Texas at Austin Libraries
    • facebook
    • twitter
    • instagram
    • youtube
    • CONTACT US
    • MAPS & DIRECTIONS
    • JOB OPPORTUNITIES
    • UT Austin Home
    • Emergency Information
    • Site Policies
    • Web Accessibility Policy
    • Web Privacy Policy
    • Adobe Reader
    Subscribe to our NewsletterGive to the Libraries

    © The University of Texas at Austin

    Browse

    Entire RepositoryCommunities & CollectionsDate IssuedAuthorsTitlesSubjectsDepartmentThis CollectionDate IssuedAuthorsTitlesSubjectsDepartment

    My Account

    Login

    Information

    AboutContactPoliciesGetting StartedGlossaryHelpFAQs

    Statistics

    View Usage Statistics
    University of Texas at Austin Libraries
    • facebook
    • twitter
    • instagram
    • youtube
    • CONTACT US
    • MAPS & DIRECTIONS
    • JOB OPPORTUNITIES
    • UT Austin Home
    • Emergency Information
    • Site Policies
    • Web Accessibility Policy
    • Web Privacy Policy
    • Adobe Reader
    Subscribe to our NewsletterGive to the Libraries

    © The University of Texas at Austin