Modeling Individuals' Frequency and Time Allocation Behavior for Shopping Activities Considering Household-Level Random Effects

Abstract

The paper describes a comprehensive frequency and time allocation modelling system for shopping activities. The modelling system is person-based but explicitly considers fixed and random household effects. It has three components: a weekly shopping frequency model, a daily shopping frequency model, and a time allocation model for individual shopping episodes. The frequency models consider activity generation as a latent response, the propensity to participate in shopping activities. This latent response is modelled using an ordinal response model. Both the weekly and daily frequency models are multilevel ordinal logit models, where the household is the highest level and the individual person is the lowest level. The multilevel ordinal logit models incorporate household level influences on an individual's shopping behaviour in term of fixed effects and a random intercept. The time allocation models are hazard duration models that consider household-level random heterogeneity. The entire modelling system is sequential from the weekly frequency component to the time allocation component. The outputs of the earlier components enter as inputs to the later components: weekly frequency is the input to the daily frequency model; weekly and daily frequencies are input to the time allocation model. The system is designed to overcome the deficiencies of household-level information in the personallevel activity diary data that are used to develop the model. Khandker M. Nurul Habib and Eric J. Miller

Activity participation, episode duration and stop-making behavior of pilgrims in a religious event: An exploratory analysis

“…The number of stops taken by an individual during a tour is generally modeled as an ordered variable in the literature (Daisy et al, 2018). Therefore, ordered logit (OL) models have been utilized to not only understand the travel behavior of people through household surveys (Bhat and Srinivasan, 2005;Nurul Habib and Miller, 2007;Daisy et al, 2018) but also tourist behavior at religious events/festival destinations (Kemperman et al, 2003;Yang et al, 2011). The OL approach predicts the preferences of persons in undertaking an ordered number of stops in a temporally constrained activity set up (Nurul Habib and Miller, 2007).…”

“…Therefore, ordered logit (OL) models have been utilized to not only understand the travel behavior of people through household surveys (Bhat and Srinivasan, 2005;Nurul Habib and Miller, 2007;Daisy et al, 2018) but also tourist behavior at religious events/festival destinations (Kemperman et al, 2003;Yang et al, 2011). The OL approach predicts the preferences of persons in undertaking an ordered number of stops in a temporally constrained activity set up (Nurul Habib and Miller, 2007). In the context of home-based tour, frequency of activity episodes rather than total stops during a tour is given as the input in the ordered set up (Bhat and Zhao, 2002;Bhat and Srinivasan, 2005;Nurul Habib and Miller, 2007;Arentze et al, 2011).…”

“…The OL approach predicts the preferences of persons in undertaking an ordered number of stops in a temporally constrained activity set up (Nurul Habib and Miller, 2007). In the context of home-based tour, frequency of activity episodes rather than total stops during a tour is given as the input in the ordered set up (Bhat and Zhao, 2002;Bhat and Srinivasan, 2005;Nurul Habib and Miller, 2007;Arentze et al, 2011). Many researchers have found a variety of factors, including socio-demographics, land use density, work schedule, availability of transportation services, work duration, activity levels etc., affecting the propensity of making stops.…”