Unveiling the effects of activity-travel chaining on peak and off-peak travel: workers vs. non-workers

Activity-travel chaining involves an interconnected series of activities and associated travel that occur between a pair of anchor (or primary) activities. By this definition, characterizing activity-travel chains requires the attributes of both activities (e.g., location, duration, activity type) and travel (e.g., distance, route, mode). Prior research suggests that the propensity to chain activities can greatly influence travel demand and its temporal distribution. To support reliable travel forecasting and inform travel demand management strategies, we investigate the direct and indirect effects of activity-travel chaining propensity for three activity types on peak and off-peak motorized person miles traveled (PMT). To perform the investigation, we employ a structural equation modeling (SEM) framework with four mediator variables (i.e., travel time savings ratio, activity space area, average daily trips, and non-car mode usage) to capture the indirect effects of activity-travel chain propensity (as well as socio-demographics and land use) on motorized PMT. We also compare workers and non-workers by specifying and estimating separate models for each group. The results indicate that activity-travel chaining propensity increases peak-hour motorized PMT for both workers and non-workers across most activity types. We also find that non-workers chain maintenance activities during off-peak hours, presumably to avoid traveling to these activities during peak hours. Additionally, we find significant effects of socio-economic characteristics, location attributes, and several mediating variables on peak-hour motorized PMT, which policymakers can leverage when formulating travel demand management strategies.