Quantifying the Influences of Telecommuting on Household Total Trips and VMT Generation

The Covid-19 pandemic, alongside changes in technology and shifts in the job market, has led to increasing numbers of workers in the U.S. telecommuting, or ‘working from home.’ As many workers and businesses shift to models allowing for some or all work to be carried out off-site, traditional patterns of commuting are shifting, causing potentially widespread impacts on all aspects of the transportation system. Much of the previously available data on telecommuting classified workers as either fully remote or fully on-site. In the post-pandemic economy, increasing numbers of workers are ‘hybrid’ telecommuters, working from home part-time. While telecommuting provides workers with the ability to decrease VMT by eliminating commutes, previous research has shown that in many cases those who work from home actually generate more VMT than their counterparts, perhaps due in part to having more time to generate non-work trips for leisure and other purposes. As telecommuting becomes increasingly popular, understanding the broader impacts of this trend on travel outcomes is necessary to allow for planning processes that limit VMT generation and its negative social, environmental, and health effects, and to reassess transit systems to meet the changing travel needs of the population. Using a dataset from California, where a proliferation of high-tech companies and industries allowed for the early adoption of telecommuting models, this research aims to quantify the influences of telecommuting on household VMT generation and total trip generation. It will advance the current understanding of the influences of telecommuting on VMT and travel behavior usage in three ways. First, it will explore these influences at the household level with precise locations of where people live to control for both sociodemographic characteristics and neighborhood built environmental features. Second, it will employ a hierarchical two-stage modeling approach. Not only it is the appropriate method to analyze variables with large numbers of zeros, such as transit trip counts, but also it can handle a nested data structure and take spatial autocorrelation and heterogeneity into account. Third, this research will use data from California, where most telecommuting started earlier than elsewhere. That gives a longer time for the impacts of telecommuting to be felt.

• Supplemental Notes:
  • Funding: USDOT $120,000, Matching $60,000

Language

• English

Project

• Status: Active
• Funding: $180000
• Contract Numbers:

  69A3552348337

• 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 Equitable Transit-Oriented Communities (CETOC)

  University of New Orleans
  New Orleans, LA  United States 
• Project Managers:

  Kline, Robin

  Tian, Guang

• Performing Organizations:

  University of New Orleans

  Department of Planning and Urban Studies
  New Orleans, LA  United States  70148
• Principal Investigators:

  Tian, Guang

• Start Date: 20230601
• Expected Completion Date: 20241031
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Public transit; Ridership; Telecommuting; Trip generation; Vehicle miles of travel
• Geographic Terms: California
• Subject Areas: Operations and Traffic Management; Passenger Transportation; Planning and Forecasting; Public Transportation;

Filing Info

• Accession Number: 01901129
• Record Type: Research project
• Source Agency: Center for Equitable Transit-Oriented Communities (CETOC)
• Contract Numbers: 69A3552348337
• Files: RIP
• Created Date: Nov 30 2023 3:55PM