Deriving Commuting Patterns From Tweets: ​Investigating the Benefits and Limits of Using Publicly Available Volunteered Geographic Information

Transportation policy decisions have historically relied on understanding commuting patterns, the journey-to-work. While scholars often acknowledge the value inherent in examining commuting patterns holistically by considering both work and non-work trips, the difficulty of assembling data about non-work trips has restricted their inclusion in regional/large scale analyses. The overarching goal of this research project is to examine the feasibility of using geo-referenced tweets and Open Street Map (OSM) as proxy measures to understand and explain a robust range of commuting behaviors and spatio-temporal movement patterns. Specifically, the research team uses Longitudinal Employer-Household Dynamics (LEHD) Origin Destination Employment Statistics (LODES), a dataset created by the US Census Bureau to develop a baseline of movement patterns in the nine-county San Francisco Bay Area region. The LODES dataset provides spatial distributions of workers' employment and residential locations and the relation between the two at the Census block-level. Working with research partners at the University of Salzburg and Hunter College, CUNY, the research team extracts commuter patterns from social media data (in this case, geo-referenced tweets). The team uses semantic clustering methods (interpreting the text of the tweets themselves) to identify the purpose of the trip. The team compares Census-derived data with social media-derived data in order to identify spatial movement clusters. In other words, the research team uses social media data to explain movement patterns that fall outside of conventional journey-to-work trips. The incorporation of address-level points-of-interest data derived from OSM provides an additional opportunity to characterize the trip purpose. In addition to the locational references, the social media data also contains date/time stamps that allow for a spatio-temporal analysis (weekday-weekend, seasonal) of movement patterns. The unique shutdown as a result of COVID-19 allows the research team an additional opportunity to examine non-work trips. This research project investigates the benefits and limitations of using continuously and publicly available volunteered geographic information (tweets and OSM) to complement official census-derived data to conduct data-driven transportation policy research.

    Language

    • English

    Project

    • Status: Active
    • Funding: $9472
    • Contract Numbers:

      69A3551747127

    • 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:

      Office of the Assistant Secretary for Research and Technology

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

      Mineta Consortium for Transportation Mobility

      San Jose State University
      San Jose, CA  United States  95112
    • Principal Investigators:

      Laxmi Ramasubramanian, Laxmi

    • Start Date: 20200501
    • Expected Completion Date: 20210201
    • Actual Completion Date: 0
    • USDOT Program: University Transportation Centers

    Subject/Index Terms

    Filing Info

    • Accession Number: 01741907
    • Record Type: Research project
    • Source Agency: Mineta Consortium for Transportation Mobility
    • Contract Numbers: 69A3551747127
    • Files: UTC, RiP
    • Created Date: Jun 3 2020 5:00PM