Exploring Variation Properties of Departure Time Choice Behavior by Using Multilevel Analysis Approach

Abstract

This paper examines the variation properties of departure time choice behavior by activity type, using a continuous six-week travel survey collected in the cities of Karlsruhe and Halle in Germany in 1999. Total variation of departure time choice is decomposed into five variation components: spatial variation, temporal variation at aggregate level, inter-household variation, inter-individual variation, and intra-individual variation. These variations are first quantitatively analyzed using multilevel modeling approach without considering the influences of explanatory variables. Then, based on the clarified variations patterns, the paper further examines how much of the variations can be captured by explanatory variables. It is confirmed that different activities show quite different variations and among the variations, the intra-individual variation accounts for the largest percentage of the total variation. Model estimation results further underscore the needs of simultaneously dealing with unobserved macro-level variations, especially inter-individual, inter-household and spatial variations, as well as the intra-individual variation, even when the explanatory variables are included in the model. Such quantitative assessment of various types of variations based on the multi-level modeling approach could provide a useful guide for the specification of behavior models, and deepen the knowledge of behavioral variability by going beyond the traditional paradigm which only focuses on a limited set of variations.

The same mode again? An exploration of mode choice variability in Great Britain using the National Travel Survey

“…Variability in activities and time patterns has received a relatively large amount of scientific attention (e.g. Jones and Clarke, 1988;Kitamura et al, 2006;Chikaraishi et al, 2010Chikaraishi et al, , 2009Keuleers et al, 2001;Timmermans et al, 2001;Horni et al, 2011), whereas destination variability (Buliung et al, 2008), and modal variability have received less attention. While the focus of this paper is transport mode choice variability, we summarise some broader research into travel behaviour variability which provides relevant insights for mode choice variability.…”

Identifying variations and co-variations in discrete choice models

“…These results seem to be quite significant compared to the proportions of variations in other behavioral aspects. Concretely speaking, previous studies have shown higher shares of intra-individual variations in many cases: around 50-60% for the number of trips per day, travel time and travel distance (Pas 1987;Pendyala 1999); 35-85% (depending on the activity type) for departure time choice (Kitamura et al 2006;Chikaraishi et al 2009); and 17-65% for time use behavior (Goulias 2002;Chikaraishi et al 2010). Given these results, we could say that mode choice behavior is relatively stable from day to day compared to other behavioral aspects.…”

“…An individual can move in and out of different origin and destination pairs, while an origin and destination pair can be moved by different individuals; the relationship between individuals and space therefore also follows a cross-classified structure. Such complicated crossclassified variation structures have been examined simultaneously by Chikaraishi et al (2009Chikaraishi et al ( , 2010, with a focus on departure time choice and time use behavior. However, in the standard multilevel cross-classified model, we have to assume that all variations at the different cross-classified levels are independent of each other.…”

Wider Dissemination of Household Travel Survey Data Using Geographical Perturbation Methods

“…The past inability to release such data has been an obstacle toward an improved understanding of the linkages that exist between travel behavior and the built environment because these disaggregate data are most suitable to study since they circumvent the methodological concern of an ecological fallacy (Handy et al, 2002). Accordingly, past studies of the relationship between household travel patterns and the built environment have been suspect of any inferential comparison of the disaggregate housing unit to a more aggregate representation of neighborhood (Goulias and Kim, 2001;Bhat and Zhao, 2002) and have confirmed the inherent risk of aggregating household data into the zones traditionally exhibited in four-step travel demand models (Chikaraishi et al, 2009). To avoid this modeling pitfall, travel behavior research has continued to advance in the direction of employing activity-based travel demand models that rely extensively on disaggregate built environment and socioeconomic measures in order to properly capture their effects on observed household travel (Badoe and Miller, 2000;Davidson et al, 2007).…”