Capturing Well-Being in Activity Pattern Models within Activity-Based Travel Demand Models

Details

• Creators:
  Ben-Akiva, Moshe ; Abou-Zeid, Maya ; Turner, Edmund K.
• Corporate Creators:
  Massachusetts Institute of Technology ; American University of Beirut
• Corporate Contributors:
  United States. Department of Transportation. Research and Innovative Technology Administration
• Subject/TRT Terms:
  • Travel demand
  • Traffic models
  • Activity choices
  • Traffic forecasting
  • Travel patterns
  • Life styles
  • Public health
• Publication/ Report Number:
  MITR22-1
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  New England University Transportation Center
• Abstract:
  The activity-based approach which is based on the premise that the demand for travel is derived from the demand for activities, currently constitutes the state of the art in metropolitan travel demand forecasting and particularly in a form known as the day schedule approach. This approach first models the day activity pattern of an individual (number of activities and tours by type), and then models the travel dimensions including destination, mode, and time-of-travel given an activity pattern. Several modeling developments have been incorporated into these models over the last decade or so. Yet, the specification of the activity pattern model in operational activity-based model systems is not founded in a behavioral theory, but rather combines in ad-hoc ways a number of socio-economic, demographic, lifestyle, and accessibility variables based on empirical considerations. We postulate that activities are planned and undertaken to satisfy needs so as to maintain or enhance subjective well-being, and extend activity pattern models in this direction.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/26960/dot_26960_DS1.pdf
• File Type:
  [PDF - 162.76 KB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha256:cff4847b3ac57568efbf9c017378cbc540ab936d8f8053cf4e44719119f83927