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129 Companionship for Leisure Activities An Empirical Analysis Using the American Time Use Survey Sivaramakrishnan Srinivasan, University of Florida Chandra R. Bhat, University of Texas at Austin The activity- based travelâmodeling paradigm recog-nizes that individuals undertake activity and travelnot only independently but also together with other household and nonhousehold members. It has also been argued that the desire for interaction with other people is an important stimulus for activityâtravel generation and therefore warrants treatment in travelâdemand models. However, Axhausen (2005) notes that this important social dimension of activityâtravel behavior is not accommodated in travel modeling. Further, the modeling of interpersonal interdependencies in activityâtravel patterns is necessary for realistic forecasts of travel patterns under alternate socioe- conomicâtechnological scenarios and due to changes in land use and transportation system characteristics. The fol- lowing examples serve to illustrate this point: 1. Vehicle occupancy levels are determined by indi- vidualsâ decisions to travel together, which are motivated by the desire to participate in the destination activity jointly. Thus, the modeling of joint activityâtravel pur- suits is necessary to determine the volume of vehicular travel in the system, and consequently for the evaluation of policies such as HOV/HOT lanes (Vovsha et al. 2003). Similarly, the individualsâ response to carpooling incen- tives depends on their ability to synchronize their travel patterns with those of others. 2. Though participation in leisure activities is con- strained by individualsâ obligations (Gliebe and Koppel- man 2002; Srinivasan and Bhat 2006), employer- based demand management strategies (such as flextime and telecommuting) could lead to increased leisure time and likelihood of joint activities, as well as alter the travel patterns of persons not directly impacted by the policy. These secondary impacts cannot be captured by models that do not accommodate interpersonal interactions (Srinivasan and Bhat 2006). 3. Individuals may be willing to travel farther and pursue activities for longer durations when the activity or travel is being pursued with family or friends. Further, such joint activity could be restricted to certain periods of the day. For example, Kemperman et al. (2006) iden- tify three peak periods for social activity participation using data from The Netherlands. The timing and dura- tions of trips and stops have substantial implications for determining the impacts of mobile- source (i.e., from vehicles) emissions on air quality. 4. When individuals participate in activities with non - household members, they may also undertake travel to pick up and drop off their companions. Such additional travel cannot be effectively captured by individual- level models. 5. Social activities are perhaps not as flexible as they have been treated traditionally (Kemperman et al. 2006). For example, some of the joint leisure activities pursued with nonhousehold members could be at the residence of friends or family. Consequently, the destination choice for such travel may have limited sensitivity to the trans- portation system characteristics (see also Carrasco et al. 2006). 6. The increasing adoption of ICTs (information and communication technologies) like cell phones, Internet, and e- mail can have strong impacts on the social lifestyles of people and hence on activities pursued with family and nonfamily members (Carrasco and Miller 2006).