A model for joint choice of daily activity pattern types of household members

Abstract

Intra-household interactions constitute an important aspect in modeling activity and travel-related decisions. Recognition of this importance has recently produced a growing body of research on various aspects of modeling intra-household interactions and group decision-making mechanisms as well as first attempts to incorporate intra-household interactions in regional travel demand models. The previously published research works were mostly focused on time allocation aspect and less on generation of activity episodes, trips, and travel tours that are necessary units for compatibility with regional travel demand models. Also, most of the approaches were limited to household heads only and did not consider explicitly the other household members as acting agents in the intra-household decision making. A model is proposed for joint choice of daily activity pattern (DAP) types for all household members that explicitly takes into account added group-wise utilities of joint participation in the same activity. The model is based on the aggregate description of individual DAP types by three main categories -mandatory travel pattern, non-mandatory travel pattern, and at-home pattern. Important intra-household interactions can be captured already at this aggregate level. A choice structure considers all possible combinations of DAPs of all household members as alternatives. Utility function of each alternative includes components corresponding to each individual DAP type as well as group-wise interaction terms that correspond to joint choice of the same pattern by several household members. Statistical analysis of intra-household interactions and estimation results of the choice model are presented. The model estimation has confirmed a strong added utility of joint choice of the same pattern for such person types as non-worker or part time worker in combination with child, two retired persons, two children, and others. The proposed model represents a part of the advanced regional model system being developed for the Atlanta Regional Commission.

Building a validation measure for activity-based transportation models based on mobile phone data

“…social visit, sports or going to restaurant) (e.g. Arentze & Timmermans, 2004;Bradley & Vovsha, 2005). The home, mandatory and non-mandatory activities are represented as 'H', 'W' and 'O', respectively.…”

A multiple discrete–continuous nested extreme value (MDCNEV) model: Formulation and application to non-worker activity time-use and timing behavior on weekdays

“…In the context of OH discretionary activity pursuits, the activity timing-related results suggest that non-working women are least (most) likely to time their OH discretionary activities during the early morning and morning (late afternoon and evening) periods. These timing preferences may again be traced to the maintenance activity responsibilities of non-working women during the early morning, morning and night periods (see Bradley and Vovsha, 2005).…”

A multivariate ordered-response model system for adults’ weekday activity episode generation by activity purpose and social context

“…On the other hand, it is the scheduling and sequencing of outof-home episodes that get manifested in the form of travel patterns (Doherty and Axhausen, 1999, Scott and Kanaroglou, 2002, Vovsha et al, 2003. Finally, even among those studies that consider inter-individual interactions at an episode level, almost all of them have adopted a framework that first generates activity episodes by activity purpose, and subsequently "assigns" each of these purpose-specific episodes to a certain accompaniment type (for example, alone versus joint), typically using a discrete choice model (see, for example, Wen and Koppelman, 1999, Gliebe and Koppelman, 2002, and Bradley and Vovsha, 2005. Unfortunately, such a sequential framework cannot accommodate general patterns of observed and unobserved variable effects that are specific to each activity purpose-accompaniment type combination (see also Scott and Kanaroglou, 2002).…”