Computerized Household Activity-Scheduling Survey for Toronto, Canada, Area: Design and Assessment

Sean T. Doherty · Erika Nemeth · Matthew J. Roorda · Eric J. Miller

71 citations0/0 files storedTransportation Research Record Journal of the Transportation Research Board

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Transportation Planning and Optimization Survey Methodology and Nonresponse Human Mobility and Location-Based Analysis

Abstract

Traditional activity-travel diary surveys have for some time served as the primary source of data for understanding and modeling travel behavior. Recent changes in policy and forecasting needs have led to the development of an emerging class of activity-scheduling process surveys that focus on the underlying behavioral mechanisms that give rise to travel and condition future change. Many of these surveys involve the use of computers for data entry over multiday periods. These changes pose new challenges and opportunities for quality assessment. At this early stage it is more important than ever to document closely the quantity and quality of data provided by such surveys as well as the associated burden and experience of respondents. This study reviews existing quality standards and seeks to develop several new data-quality measures suitable to this emerging class of surveys. Data are used from a recent household activity-scheduling survey of 271 households in Toronto, Ontario, Canada. A detailed description of the survey instrument is provided, along with an in-depth examination of key results that shed light on data quality. Included are results from a separate survey of 31 respondents concerning their experiences and perceptions of the survey. Overall, although the survey was generally successful in tracking both observed patterns and underlying decision processes over a multiday period within a household, it did come at a price in terms of respondent burden. On the basis of these results several new data-quality guidelines are suggested that incorporate use of activity-trip rates, scheduling-step rates, planning-time horizons, and log-in durations. Further specific suggestions for reducing respondent burden are suggested.

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References

1994Stated preference analysis of travel choices: the state of practice 1993Developments in dynamic and activity-based approaches to travel analysis 1997Activity-Based Approaches to Travel Analysis 1999Can using global positioning system (GPS) improve trip reporting?2000A computerized household activity scheduling survey 2003Qualitative Methods in Travel Behaviour Research 2003Trip Rate Analysis in GPS-Enhanced Personal Travel Surveys 1998Travel Behaviour Research: Updating the State of Play

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2006Activity–Travel Behaviour Research: Conceptual Issues, State of the Art, and Emerging Perspectives on Behavioural Analysis and Simulation Modelling 2010A random utility maximization (RUM) based dynamic activity scheduling model: Application in weekend activity scheduling 2005Modeling activity scheduling time horizon: Duration of time between planning and execution of pre-planned activities 2008Social Context of Activity Scheduling 2009Modelling activity generation: a utility-based model for activity-agenda formation

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