An Integrated Model of Activity-Travel Behavior and Subjective Well-being

Transportation plays a critical role in shaping the quality of life in communities around the world by making it possible for people to engage in activities, participate in societal functions, and interact with various agents and entities that make up a region’s ecosystem. Additionally, transportation enables mobility, thus providing people and businesses access to goods, services and opportunities. By enabling these functions, transportation and logistics systems directly impact the economic vitality of a region, along with the state of the environment, energy consumption, public health, and safety and security. Because of the tight connection between transportation and quality of life, considerable attention has been paid to understanding the linkage between mobility and subjective well-being (Ziems et al., 2010; Bergstad et al., 2011; Lee and Sener, 2016; Friman et al., 2017). Measures of subjective well-being capture the emotions that people feel as they go about their daily lives, undertake activities, and travel. While the quality of life may be viewed as a notion that captures the broader and longer-term outlook that people have on their lives, the notion of subjective well-being may be viewed as capturing the emotions experienced in a specific context or situation (National Research Council, 2013). However, transportation demand forecasting models do not output measures of well-being, and household travel surveys never collect information about feelings of well-being associated with various activity-travel episodes reported in a travel diary. In the absence of any knowledge or data about actual subjective feelings of well-being that are derived from activities and trips, inferences about well-being are often drawn based on the time use pattern. This project proposes estimating an integrated model of activity-travel behavior and subjective well-being that can essentially serve as a well-being scoring tool for activity-travel patterns. The model, when interfaced with an activity-based travel demand model that outputs activity-travel records at the level of the individual agent, can be used to compute well-being scores that are based on the predicted activity-travel and time use patterns.


  • English


  • Status: Completed
  • Funding: $125,000
  • Contract Numbers:


  • 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:

    Pendyala, Ram

    Khoeini, Sara

  • Start Date: 20171001
  • Expected Completion Date: 20191001
  • Actual Completion Date: 20191001
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01755251
  • Record Type: Research project
  • Source Agency: Center for Teaching Old Models New Tricks
  • Contract Numbers: 69A3551747116
  • Files: UTC, RIP
  • Created Date: Oct 21 2020 8:06PM