Latent variable models of Attitudes and Preferences, and their Prediction of Autonomous Vehicle Adoption Intent

Autonomous vehicles (AV) (also referred to as driverless cars or self-driving cars) are capable of navigating without human input using an array of technologies such as radar, lidar, global positioning system (GPS), odometry, and computer vision. Most industry experts suggest that autonomous vehicles will be on the road within a few years (Stoll, 2016). For example, the US Secretary of Transportation expects driverless cars to be in use all over the world by 2025 (Hauser, 2015), and The Institute of Electrical and Electronics Engineers (IEEE) predicts that up to 75% of all vehicles will be autonomous by 2040 (IEEE, 2012). In addition to the availability of AVs, ride-hailing companies, such as Uber and Lyft, have changed the transportation landscape as they provide door-to-door mobility-on-demand through the use of mobile apps. Given these new transportation technologies and services, it is necessary for transportation forecasting models to account for market dynamics that will result from increased penetration of these technological innovations. Enhancing transportation forecasting models based on people’s attitudes toward and perceptions of these technologies and services is necessary. The data collected by TOMNET provide the foundation for enhancing the forecasting models by capturing ample data on attitudes and perceptions, AV adoption expectations, ride-hailing use and attitudes, and background characteristics. Additionally, data from collaborating sites (Tampa, Atlanta, Austin) is available to examine the replicability of the models. The overall goal of this project is use latent variable models to examine the factor structure of participant attitudes toward transportation, and determine whether these attitudes and perceptions are associated with the expected adoption of AVs and the adoption of ride-hailing services. The data analysis of the participant attitudes and perceptions items will include four phases: (1) Exploratory factor analysis, (2) Confirmatory factor analysis, (3) Examination of latent class models, and (4) predictive models of transportation adoption.

    Language

    • English

    Project

    • Status: Active
    • Funding: $89749
    • Contract Numbers:

      69A3551747116

    • Sponsor Organizations:

      Office of the Assistant Secretary for Research and Technology

      University Transportation Centers Program
      Department of Transportation
      Washington, DC  United States  20590
    • Managing Organizations:

      Center for Teaching Old Models New Tricks

      Arizona State University
      Tempe, AZ  United States  85287
    • Performing Organizations:

      Center for Teaching Old Models New Tricks

      Arizona State University
      Tempe, AZ  United States  85287
    • Principal Investigators:

      Grimm, Kevin

      Pendyala, Ram

    • Start Date: 20201001
    • Expected Completion Date: 20220930
    • Actual Completion Date: 0
    • USDOT Program: University Transportation Centers Program

    Subject/Index Terms

    Filing Info

    • Accession Number: 01755253
    • Record Type: Research project
    • Source Agency: Center for Teaching Old Models New Tricks
    • Contract Numbers: 69A3551747116
    • Files: UTC, RiP
    • Created Date: Oct 21 2020 8:24PM