Modeling daily mobility and trip purpose choices in activity budgets considering spatiotemporal constraints: A Case Study in the City of Seoul

Activity-based models (ACBMs) are frequently used to estimate a person’s travel patterns and improve the accuracy of forecasting aggregated demand. Two essential elements of ACBM are the decision to travel, often known as mobility, and the purpose of the journey, using an activity budget. The goal of this study is to develop sequential models of daily mobility and trip purpose choice using an activity-based framework. Random utility maximization (RUM)-based mobility and trip purpose choice models explain key elements of persons’ daily activity patterns, incorporating spatiotemporal and socio-demographic characteristics. Based on the national household travel survey data collected in South Korea in 2016. The mobility model reveals that licensed drivers and high-income travelers are more likely to travel, whereas the elderly and homemakers are less likely to do so. The previously selected trip purpose, as well as the total number of trips for each purpose during the departure time of the trip influence the choice of the current trip. The test for corrected prediction on another sample examined the replication capability using estimated parameters. This study discusses about demand forecasting and the policy implications of the empirical results to build the environmentally sustainable developments.