Open Toll Lanes in a Connected Vehicle Environment: Development of New Pricing Strategies for a Highly Dynamic and Distributed System - Phase II

The future driving experience is expected to be vastly different from today's environment: driverless vehicles will free up passengers and "drivers" allowing them to communicate with fellow road users and infrastructure via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. This new transportation environment provides new opportunities for conducting road operations including new methods of tolling. Tolled travel lanes with congestion pricing are an effective method to address the growing congestion problems on freeways. In the current state of practice, toll lanes are typically separated from the regular lanes (with physical barriers) with toll rates either fixed or varying by time-of-day or by congestion level. Vehicles that sense their own locations (including the lanes they are in) can exchange information about their positions and speeds will serve as the basis to develop and support an open tolling system with the number of "tolled lanes" varying dynamically to maximize throughput. In addition, toll rates paid by vehicles may change not only by congestion level but by when/how the driver decides to use (or reserve) the toll lane(s). The tolls paid by users may also vary by demographic factors (e.g., income) and trip purpose if the system is designed to allow drivers to bid for the privilege of getting on the toll road. In previous research, this project team developed the analytical solutions for a new tolling approach based on a combinatorial Vickrey auction designed for a single toll road with multiple entry points where travelers can make multiple bids to gain access to part or the entire toll lane. In this phase II study, the team is proposing to develop and conduct surveys to gain insights into how people would choose to travel on toll roads when they are given the opportunity to bid. Surveys provide a means to collect some information on individuals bidding behavior, even if only stated preferences, and can be used to form the foundation of the human behavior model of previous research. Modeling human behavior is challenging especially when accounting for heterogeneous behavior of drivers. Recently, a new approach for modelling human behaviors, within agent-based model (ABM), was published by the leading ABM expert in the world: Joshua Epstein. The approach is called Agent_zero and it overcomes some of the existing problems of human modeling within an ABM environment, e.g., limitations of adaptive behaviors. Thus, the focus of phase II of this research is: (1) Collection of survey data of stated preference of individual behavior within a future tolling scenario that requires V2I communication. (2) Analysis and incorporation of survey data results into existing auction model. (3) Simulate new auction model using the new Agent_zero approach.

Language

  • English

Project

  • Status: Active
  • Funding: $129719.00
  • Contract Numbers:

    DTRT13-G-UTC30

    NTC2015-MU-R-09

  • Sponsor Organizations:

    National Transportation Center @ Maryland

    1173 Glenn L. Martin Hall
    University of Maryland
    College Park, Maryland  United States  20742
  • Project Managers:

    Zhang, Lei

  • Performing Organizations:

    University of Maryland, College Park

    Department of Civil and Environmental Engineering
    1173 Glenn Martin Hall
    College Park, MD  United States  20742

    Old Dominion University

    Norfolk, VA  United States  23529
  • Principal Investigators:

    Zhang, Lei

    Robinson, Mike

    Collins, Andrew

    Cetin, Mecit

  • Start Date: 20150601
  • Expected Completion Date: 0
  • Actual Completion Date: 20160831
  • Source Data: RiP Project 39047

Subject/Index Terms

Filing Info

  • Accession Number: 01578276
  • Record Type: Research project
  • Source Agency: National Transportation Center @ Maryland
  • Contract Numbers: DTRT13-G-UTC30, NTC2015-MU-R-09
  • Files: UTC, RiP
  • Created Date: Oct 21 2015 1:00AM