A Bivariate Multinomial Probit Model for Trip Scheduling: Bayesian Analysis of the Work Tour
提出二元多项Probit模型分析工作出行的时间安排，允许不同时段和往返行程之间存在相关性，基于旧金山湾区数据估计，发现个人和出行特征对时间选择有合理影响，且模型优于多项Logit。
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.