A novel approach to developing the choice model for multiday activity participation in travel behavior analysis

As society advances and social norm increasingly prioritize the quality of individual life, policies such as the four-day workweek and flextime are being widely adopted and it causes increased participation in discretionary activities. Since this phenomenon cannot be considered in existing travel demand modelling, this study aims to develop a daily activity participation model that reflects these changes and enhances the trip generation model in travel demand analysis. To account for the daily variation in discretionary activity participation, we conducted a multiday analysis instead of 1-day analysis and collected multiday activity diary data using time use survey. Additionally, based on the need-based theory suggested in previous studies, we estimated binary logit models that can predict multiday activity participation patterns and identify the factors influencing these patterns. We statistically identified various factors influencing multiday activity participation patterns, including preferred day of week, socioeconomic attributes and activity/travel conditions. Moreover, the differences in the influencing factors for each activity type were found out. The results suggested in this study are expected to contribute not only to overcoming the limitations of 1-day analysis through multiday analysis but also to the expansion of activity-based model and the improvement of trip generation model.