A Bivariate Multinomial Probit Model for Trip Scheduling: Bayesian Analysis of the Work Tour

As tour‐based methods for activity and travel participation patterns replace trip‐based methods, time‐of‐day (TOD) choice modeling remains problematic. In practice, most travel demand model systems handle tour scheduling via joint‐choice multinomial logit (MNL) models, which suffer from the well‐known independence of irrelevant alternatives assumption. This paper introduces a random utility maximization model of tour scheduling called the bivariate multinomial probit. This specification enables correlations across TOD alternatives, both outbound and return (on a tour) and over time slots (in a day). The model is estimated in a Bayesian setting on work‐tour data from the San Francisco Bay Area with 30‐minute time slots at most times of day (for both outbound and inbound journeys). Empirical results suggest that a variety of individual, household, and tour characteristics have reasonable effects on scheduling behavior. For instance, older persons typically pursue work tours at earlier times of day, part‐time workers pursue their work tours later, and those with additional activities and tours tend to arrive slightly later and leave much earlier than those undertaking only a single tour, everything else constant. The model outperforms a comparable MNL, while offering reasonable implications under a variety of road‐tolling scenarios.