A copula-based continuous cross-nested logit model for tour scheduling in activity-based travel demand models. (March 2021)

• Record Type: Journal Article
• Authors: Ghader, Sepehr; Carrion, Carlos; Tang, Liang; Asadabadi, Arash; Zhang, Lei
• Is Part Of: Transportation research. Volume 145(2021)
• Page Start: 324
• Page End: 341
• Publication Date: 2021-03
• Subjects: Continuous cross-nested logit -- Copula -- Activity scheduling -- Time-of-day modeling -- Continuous choice modeling -- Activity-based models; Transportation -- Research -- Periodicals; Transportation -- Mathematical models -- Periodicals
• DOI: 10.1016/j.trb.2021.01.001
• Languages: English
• ISSNs: 0191-2615

Abstract

Highlights: We extended the continuous choice models that were mainly limited to one-dimensional choice into a multi-dimensional framework. We introduced the copula-based continuous cross-nested logit as a framework that utilizes copula and continuous-cross-nested logit model to capture all possible sorts of correlation required for an activity-based travel demand model. Through an empirical application example, we compared the copula-based continuous cross-nested logit with the continuous logit, the continuous cross-nested logit, and the copula-based continuous logit and showed that it outperforms them in terms of the goodness-of-fit and out-of-sample validation. Abstract: This paper introduces a multi-dimensional continuous activity scheduling choice modeling framework. The paper is focused on modeling the joint choice of arrival to an activity and departure from the activity. Each of the choices is modeled in continuous time using the continuous cross-nested logit model. The continuous cross-nested logit model is able to capture various types of correlation between alternatives in continuous time. In addition to the correlation between alternatives, this paper uses copula to capture the correlation between the two dependent choices of arrival to an activity and departure from the activity. Copula can model the correlation structure without knowing the actual bivariate distribution function. With its multidimensionality and ability to capture different sorts of correlations and model demand in fine time resolution, the introduced framework can provide a sufficient tool for the time-of-day component of various travel demand models.