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130 I N N O VAT I O N S I N T R AV E L D E M A N D M O D E L I N G , V O L U M E 2 7. The popularity of modeling travel during week- DATA DESCRIPTION ends and for special events reinforces the need to accom- modate joint activity and travel patterns in travel models. This study uses data from the American Time Use Survey (ATUS). Conducted by the Census Bureau under contract Recent years have seen increasing efforts in the field with the Bureau of Labor Statistics, ATUS collects of transportation engineering on studying interpersonal detailed individual-level daily time use information. The interactions in activitytravel patterns. These studies sample is drawn from a subset of households responding may be classified into two categories. The first category to the Current Population Survey interviews. One indi- adopts econometric modeling methods to relate joint vidual aged 15 years or older is selected from each house- activitytravel choices with characteristics of the deci- hold for the survey. Data collection began in January sion makers (see Srinivasan and Bhat 2006). Most of 2003. Currently, data samples collected in 2003 (412,611 these studies use data from conventional travel surveys activity episodes from 20,000 individuals) and 2004 but very few have examined individuals' interactions (279,042 activity episodes from 13,973 individuals) are with nonhousehold members. The second category is available. Additional details can be obtained from the largely focused on the concept of social networks and ATUS website, http://www.bls.gov/tus/home.htm. seeks to explore the nature and extent of individuals' The ATUS data are attractive for our analysis for sev- social interactions (see Arentze and Timmermans 2006). eral reasons. First, the data sample is large (34,693 per- Thus, this latter group of studies is not restricted to ana- sons surveyed over 2 years) and represents the nation as a lyzing within-household interactions. whole as opposed to a specific geographic area. Second, Despite this increasing interest, our empirical knowl- the survey obtained information on all persons (both edge of individuals' interactions with nonhousehold household and nonhousehold members) accompanying members is limited, largely because conventional house- the respondent for each activity episode. The companions holdtravel surveys (which form the basis of activ- were classified using the scheme presented in Table 1. itytravel modeling) typically do not collect this data. An Third, the survey used a disaggregate three-tier activity exception is the recent CentreSIM travel survey (Goulias classification scheme thereby facilitating the analysis joint and Kim 2005), which included an open-ended question, activity participation at a fine resolution of activity types. "with whom was this activity episode undertaken," to It is also necessary to point out that an issue with using collect data from about 1,400 individuals on the types of ATUS for analyzing joint activity participation decisions is companions with whom each activity was undertaken. the absence of time use information for the respondents' The first analysis results indicate that approximately companion(s). ATUS collects time use data only for one one-third of activitytravel episodes and daily time is person per household. Therefore, the complete activity spent alone and a significant fraction of joint episodes participation decisions of even the respondents' own are pursued with nonhousehold members (both relatives household members are unknown. Consequently, it is not and nonrelatives). The goal of this study is to contribute to the under- TABLE 1 Companion-Type Classification standing of activities and travel pursued by individuals Scheme Adopted in ATUS jointly with household and nonhousehold members. Household Members Toward that end, there are two major tasks. First, an Spouse (husband/wife) analysis is undertaken to determine the extent to which Unmarried partner Own household child each activity type is pursued jointly. Further, this analysis Grandchild aims to illustrate the differences in the companion-type Parent (father/mother) choices (household versus nonhousehold members) Brother/sister Other related person (aunt, cousin, nephew) across the activity types. The next task is focused on Foster child leisure activities. The motivation for this focus is that, Housemate/roommate among all activity types, the desire for companionship Roomer/boarder Other nonrelative for leisure is likely to be highest. Specifically, models are developed to examine the impacts of demographic char- Nonhousehold Members acteristics, day of the week, and activity episode dura- Own nonhousehold child Parents or parents-in-law (not living in household) tions on the choice of companion type. Other nonhousehold family members (age <18) The rest of this paper is organized as follows. The sec- Other nonhousehold family members (age 18) tion immediately below describes the data used in this Friends Co-workers/colleagues/clients analysis. The empirical results are presented in the sec- Neighbors/acquaintances tion that follows. The final section provides a summary Other nonhousehold children (age <18) and highlights the insights from this study. Other nonhousehold adults (age 18)