Specification of a tour-based neighborhood shopping model

Abstract

This paper presents a state-of-the practice neighborhood shopping travel demand model. The model structure is designed to incorporate decisions across five dimensions of shopping travel, including decisions of: (1) household tour frequency; (2) participating party; (3) shopping tour type; (4) mode, and (5) destination choices using a tour-based nested-logit model. As a neighborhood model, we have also captured the interrelated effects of three main factors associated with shopping travel decisions both within and outside of the neighborhood, including the residential location within the neighborhood, the neighborhood regional setting and the household structure. The model was validated using the travel data collected in three neighborhoods located in the Puget Sound region, WA. Results show that household socio-demographics have significant effects on the decisions for household tour frequency, mode and destination choices, while the characteristics of the traveling party have considerable impacts on the decisions for tour type. The level of service and the zone attractions influence decisions about mode and destination choices. The day of week variable (weekday versus weekend) is statistically significant in all models, indicating that weekday shopping travel decisions differ from weekend, across all five dimensions of interest. The paper concludes with a discussion about how the model can be used to examine policy-related neighborhood issues (e.g. accessibility).

The validity of using activity type to structure tour-based scheduling models

“…Note especially that the planning/scheduling decision sequence may differ fundamentally from the observed sequence of executed activities/trips. Tour-based models first deconstruct a person's day into a set of primary and secondary ''tours'' (defined as the travel from home to one or more activity locations and back home again), then model the number, purpose and sequence of activity stops by destination, mode of travel, and time of day based on a set sequence of discrete choice models (Shiftan 1998;Bowman and Ben-Akiva 2001;Limanond et al 2005;Yagi and Mohammadian 2010). The most current generation of activity/tour-based regional travel demand models systems are based on a sequence of discrete choice models applied in a micro-simulation fashion (Vovsha et al 2005;Yagi and Mohammadian 2010).…”

“…Shiftan 1998;Bowman and Ben-Akiva 2001;Limanond et al 2005). However, several authors (Shiftan 1998;Limanond et al 2005;Yagi and Mohammadian 2010) recognize that activity type alone may not be sufficient in capturing the inherent flexibility and priority of activities, and instead attempt to adopt combined prioritization schemes that recognize that activities farthest from origin, or activities with longer durations, are of higher priority. The validity of these assumptions, and the limitations they may place on forecasting potential, have been scarcely addressed.…”

Should we abandon activity type analysis? Redefining activities by their salient attributes

“…For example, ''work'', ''school'' and other ''mandatory'' or recurring activities are often assumed to be fixed in space and time and thus higher ''priority'', whereas more ''discretionary'' activities such as ''leisure'', ''visiting friends'', ''entertainment'' and especially ''shopping'', are assumed to be more flexible and thus of lesser priority. Based on these assumptions, fixed or high priority activities are assumed planned/modelled first in emerging trip chain (e.g., Kitamura et al 2000), tour (e.g., Shiftan 1998;Bowman and Ben-Akiva 2001;Limanond et al 2005) and activity scheduling models (e.g., Arentze and Timmermans 2000;Miller and Roorda 2003). Even for studies of new activity-related phenomena, such assumptions continue to be made.…”

“…In particular, past suggestions have focused on the spatial and temporal flexibility of activities as key dimensions-i.e., the degree to which activities could take place at different locations and at different times, or alternatively the degree to which they are fixed to a specific location at a specific time (e.g. Shiftan 1998;Limanond et al 2005;Miller 2005). To a similar extent, interpersonal flexibility could also be considered-i.e., the degree to which activities could optionally take place with different people, or alternatively the degree to which an activity must be conducted with or for other people.…”

Modeling constrained destination choice for shopping: a GIS-based, time-geographic approach

“…First, since information on destination choice corresponds in reality to stores, many studies (especially in marketing, retailing, and consumer services) have focused on points as shopping destinations (Bell et al, 1998;Fox et al, 2004;Guy, 1985;Lloyd and Jennings, 1978;Richards and Ben-Akiva, 1974;Timmermans, 1981Timmermans, , 1996 rather than on zones as destinations (Landau et al, 1982;Limanond et al, 2005). In transportation studies, however, most data are collected and disseminated based on traffic analysis zones (TAZs).…”