Capturing Pedestrian Tours and Activities through Smartphone Data

The efficacy of travel forecasting models is predicated on their ability to reflect travelers’ behavior including the motivation for travel, the choice of destination(s) and mode(s) of travel. Behavioral representations are enhanced by modeling tours rather than individual trips by which multiple activities can be accomplished with reduced travel costs, including time and out of pocket expenses. Given the prevalence of motorized travel, particularly in North America, significant attention has been paid to developing robust representations of auto and transit travel for both trip-based and activity-based travel forecasting models. Similar efforts not dedicated to representing non-motorized travel, particularly pedestrian trips. As transportation professionals and municipalities are eager to increase active transportation utilization, there is a growing need to evaluate the potential impacts of investments and policies that provide incentives for active transportation. Addressing these needs requires a better representation of pedestrian travel in forecasting models. This paper presents methods to identify pedestrian tours from empirical data passively gathered by smartphones. Pedestrian tours are further defined by a number of attributes including the integration with other modes; the sequence of travel and activities; the number of activities completed during a tour; and the duration of scheduled pedestrian activities. These definitions result in typologies and assessments of the complexity of pedestrian tours. This classification of pedestrian tours advances the understanding of pedestrian travel patterns and the links between these tours and the built environment at a regional level.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 17p

Subject/Index Terms

Filing Info

  • Accession Number: 01764256
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-03425
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:23AM