Enhanced Destination Choice Models Incorporating Agglomeration Related to Trip Chaining While Controlling for Spatial Competition

Abstract

It is common knowledge that travelers often choose clusters or groups of nearby destinations that can be visited conveniently in a single tour. This propensity is becoming increasingly important in the context of rising fuel costs. However, gravity models, as well as most destination choice models, ignore these agglomeration effects and treat each trip or destination choice as independent. Some models have captured economies of agglomeration related to trip chaining through the use of accessibility variables. Accessibility variables, however, generally do not identify trip chaining effects uniquely, but measure differential spatial competition that arises because nearby destinations are generally better substitutes than distant ones. Because spatial competition effects generally dominate trip chaining agglomeration effects, models with a single accessibility variable have been called competing destinations models following Fotheringham. This paper presents an advance on Fotheringham's approach by introducing two distinct accessibility variables to represent agglomeration and spatial competition among destinations separately rather than their net effect. These new agglomerating and competing destination choice models were applied in Knoxville, Tennessee. The new models, which outperformed both gravity and competing destinations models, began to present a new alternative to activity-based models by allowing the incorporation of some of the most important trip chaining effects in trip-based travel demand models. For example, a sensitivity test showed that a new factory employing 1,000 workers would attract 125 new nonwork trips to the surrounding area on an average day as a result of stops on the way to and from work.

A workplace choice model accounting for spatial competition and agglomeration effects

“…2 This combined accessibility measure has a statistically significant parameter of -1.584, which represents the net effect of agglomeration and spatial competition (-1.832 + 0.282 = -1.550). This result is consistent with previous findings (Bernardin et al, 2009) and suggests that a CD model for work location choice may be satisfactory if we are interested in the net effect only.…”

“…Thus, destination choice models with accessibility measures allow for heterogeneity in spatial effect, be it the competition or the agglomeration. This approach was first introduced by Fotheringham (1986) and adapted in many subsequent studies (see Bernardin et al, 2009 for a review). Generally, the accessibility index of a destination (Aj) is measured to a single attraction variable (Bk), such as employment, in other destinations (k), and the travel cost (cjk) between the origin (j) and each potential destination (k):…”

“…To remedy for this limitation, Bernardin et al (2009) (Lieberson, 1969) for the estimation of the number of substitutes and complements to one zone (j) to another zone (k). Given a classification scheme, Lieberson's D dissimilarity statistic can be defined as the probability that two items (e.g., activities) selected at random will belong to the same category.…”

Trip distribution for limited destinations: a case study for grocery shopping trips in the Netherlands

“…the choice of a supermarket does not only depend on its location with respect to the residence, but also on the locations of other activities, e.g. (Bernardin et al 2008). The Omnibus survey only provides information of home-bound trips.…”

Development of a Pedestrian Demand Estimation Tool

“…Existing literature offers guidance for developing destination choice models, especially with respect to choice set generation (Pagliara & Timmermans, 2009) and variable specification (BenAkiva & Lerman, 1985;Bernardin, Koppelman & Boyce, 2009;Borgers & Timmermans, 1988;Pozsgay & Bhat, 2001). Common options for constructing choice sets include: 1) using deterministic rules (Ortúzar & Willumsen, 2011;Pagliara & Timmermans, 2009); and 2) using a form of sampling of alternatives (Ben-Akiva & Lerman, 1985;Lemp & Kockelman, 2012).…”