Skip to main content
ICTD 2024 Main banner
Icon Legend
This session is not in your schedule.
This session is in your schedule. Click again to remove it.

Abstract for presentation (Poster or Podium)

Express Lane Route Choice under Real-time Information: A Joint Revealed and Stated-Preference Study

Sunday, June 16, 2024
1:15 PM - 2:30 PM ET
Room: Galleria

    Primary Author(s)

  • RT

    RIFA TASNIA, n/a

    Graduate Assistant
    North Carolina Agricultural and Technical State University
    Greensboro, North Carolina, United States

    • Co-Author(s)

  • MA

    Mikal Ali, n/a

    Student
    NCAT
    Greensboro, North Carolina, United States

  • DH

    Daud Nabi Hridoy, n/a

    Graduate Assistant
    VTECH
    Blacksburg,, Virginia, United States

  • VP

    Venktesh Pandey, PhD (he/him/his)

    Assistant Professor
    North Carolina A&T State University
    Greensboro, NC, United States

  • MH

    Md Sami Hasnine, n/a

    Assistant Professor
    Virginia Tech
    Blacksburg, Virginia, United States

  • HP

    Hyoshin (John) Park, n/a

    Associate Professor
    old dominion university
    Norfolk, Virginia, United States

    • Submitter(s)

  • RT

    RIFA TASNIA

    North Carolina Agricultural and Technical State University
    Greensboro, North Carolina, United States

Abstract:

Express Lane Route Choice under Real-time Information: A Joint Revealed and Stated-Preference Study

 

Rifa Tasnia, Mikal Ali, Daud Nabi Hridoy, Venktesh Pandey, Sami Hasnine, and Hyoshin (John) Park

Abstract


The paper investigates the impact of real-time information on individuals' route choice on express lanes using a joint revealed and stated preference (SP) survey conducted in Washington DC, and Charlotte, North Carolina. The data were collected using an online survey in August and September 2023, including 911 respondents from Washington DC, and 922 respondents from Charlotte. The D-efficient method was adopted to generate SP scenarios. An exploratory data analysis was conducted on factors influencing path choices on express lanes as a function of trip purpose/urgency, tolls and travel times, and individual-specific variables such as age, income, and gender. Econometric models were estimated using these datasets, including a dynamic discrete choice (DDC) model, while evaluating sequential route choices under real-time information. DDC models can capture the state dependence and forward-looking behavior of travelers, where they maximize intertemporal payoffs in the context of route choice building off stated-preference scenarios. Model results reveal that trip urgency and relative values of tolls and travel times impact individual route choices. The findings could be used for evidence-based tolling or credit-based policy recommendations and designing congestion prices, accounting for sequential choice-making behavior of travelers.

Learning Objectives:

Attendees can expect to learn the following from this session:
  • 1. Goal: Participants will be able to develop a basic budget for personal financial management by the end of the training program.
  • Measurable: By evaluating participants' accuracy in developing a budget, it is simple to gauge their capacity to establish a basic budget.
  • Achievable: Within the parameters of a training program, participants should be able to create a basic budget.