Traditional household travel surveys ask respondents to report their travel behavior for a 24-h period, even though it is well known that travel patterns vary from day to day. Although this information provides an indication of average household behavior or travel on an average week-day, evidence suggests this method may not be the most cost-effective way to collect the data because day-to-day travel variability is substantial, requiring larger sample sizes. In addition, collecting multiday data provides a richness of information that simply cannot be captured with a 1-day survey, offering insights into, for example, differences in week-day versus weekend travel, the longer-term travel impacts of flexible work hours, and trip substitution and cycling patterns that emerge over the course of a week or more. Despite the intuitive appeal of multiday surveys, few examples and little information on sampling issues and sample size requirements are available. Given this situation, the reasons given for not doing multiday surveys (which center around respondent fatigue), why these issues are fast becoming irrelevant (through the use of new passive data-recording technologies), the sample size implications of extending a survey, and the potential for estimator efficiency and cost savings (even accounting for the cost of new technologies) when conducting multiday surveys are explored. The use of Global Positioning System-based travel distance data from Adelaide, South Australia, indicates that the sample size reductions are large; collecting multiday data is feasible, offering a richness not available in 1-day data; and the method results in cost-effective gains in estimator efficiency.
ASJC Scopus subject areas
- Civil and Structural Engineering
- Mechanical Engineering