Real-time Transportation Social Media Analytics using Pulse (Pulse-T)

As city planners and transportation system planners consider changes and upgrades to transportation systems and infrastructure, they require models that accurately reflect communities’ needs. Planners need access to advanced activity-travel demand analysis models that are responsive and sensitive to emerging transportation technologies; models are needed that not only provide insights into communities’ current travel demands and behaviors, but also help understand people’s attitudes and expectations toward a change — or a proposed change — in a community’s transportation infrastructure or transportation options. For example, using this system public sentiment can be tracked when accidents involving autonomous vehicles occur or when transportation milestones are achieved in this field. However, the data on which the current models rely has limitations that prevent planners and policymakers from tapping into residents’ attitudes and perceptions widely, across the population and across time. Current models utilize surveys or opinion polls and yield a regimented set of responses to fixed questions. Moreover, the surveys reach a relatively small number of self-selecting individuals. They measure attitudes or self-reports of behavior at a single point in time, and to update them with new research topics or at new points in time is laborious and expensive. And, while some research requires datasets that extend over time, other, critical research requires real-time data that allows gauging current community sentiment around a topic. In this project the research team builds the Pulse-T, which will exponentially expand the access of TOMNET researchers and other organizations to an up-to-date, filtered dataset of public opinion and discussions around virtually any transportation research area. Researchers and organizations will have user perceptions on transport demand at their fingertips, enabling them to take appropriate measures and actions and undertake planning projects much more effectively than is possible today.

    Language

    • English

    Project

    • Status: Active
    • Funding: $90000
    • 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:

      Kandala, Srivatsav

    • Start Date: 20191001
    • Expected Completion Date: 20211001
    • Actual Completion Date: 0
    • USDOT Program: University Transportation Centers Program

    Subject/Index Terms

    Filing Info

    • Accession Number: 01755254
    • Record Type: Research project
    • Source Agency: Center for Teaching Old Models New Tricks
    • Contract Numbers: 69A3551747116
    • Files: UTC, RiP
    • Created Date: Oct 21 2020 8:32PM