Longitudinal multilevel models analyzing the trends of land use effects on non-driving travel choice

Land use and transportation researchers have conducted numerous studies about land use effects on travel mode choice, and probed for effective policies to reduce driving, since less driving and more non-driving are widely recognized as more sustainable travel behaviors to resolve many environmental, energy and social equity issues. However, most of the previous studies rely on methodologies developed by cross-sectional data; only limited attention is explicitly given to explore the statistical techniques for longitudinal design and analysis. Using the neighborhood-level land use and persona-level travel mode choice data of 1997 and 2006 in the city of Austin, this paper attempts to establish and compare three distinct modeling approaches to analyze the trends of land use effects on people’s choice behavior of non-driving travel mode. The three modeling approaches are: a comparison approach with two cross-sectional multilevel Logit models using single-year data, a pooling approach by building one multilevel model with two-year data, and a longitudinal multilevel model. Empirical modeling results indicate that the longitudinal multilevel model is the most reasonable model for analyzing the longitudinal and multilevel datasets, since it is capable of estimating both time-invariant and time-variant land use effects, and internalizes time-variant random effects. The other two approaches may have several shortcomings. For example, the comparison approach fails to distinguish the time-variant and time-invariant effects; while the pooling model may lead to underestimated standard errors and t-statistics, and thus overestimate the significance of variables.