and

This study contributes to the literature on activity time-use and activity timing analysis by developing a comprehensive, high resolution, out-of-home non-work activity generation model that considers daily activity time-use behavior and activity timing preferences in a unified framework. More specifically, a random utility maximization-based model is formulated to simultaneously predict workers ’ activity participation and time allocation patterns in seven types of out-of-home non-work activities at various time periods of the day. From a methodological standpoint, this study uses an advanced multiple discrete-continuous nested extreme value (MDCNEV) model, which recognizes the possibility of multiple choices of activity purpose and timing. Further, the “nested extreme value ” model structure allows for flexible substitution patterns in activity time-use behavior across different activity types and/or across different time periods. At the same time, the model provides closed-form probability expressions. The empirical analysis is undertaken using data from the 2000 Bay Area Travel Survey. Several important household and commuter demographics, commute characteristics, and activity-travel environment attributes are found to be significant determinants of workers ’ activity time-use an