On the Nonlinearity of Response to Level of Service Variables in Travel Mode Choice Models

It is important to accommodate variations in responsiveness (or response heterogeneity) to level of service attributes in travel mode choice models. This response heterogeneity may be disaggregated into a systematic (observed) component and a random (unobserved) component. Earlier studies have typically considered systematic response heterogeneity by examining differences in LOS response sensitivities due to individual demographic and other attributes. In this research, our emphasis is on another element of systematic response heterogeneity – systematic response heterogeneity originating from nonlinear responsiveness to LOS attributes. Specifically, we consider both the components of systematic response heterogeneity (due to individual characteristics and due to nonlinear responsiveness) as well as unobserved response heterogeneity at the same time, and compare the empirical results of models that assume a traditional linear responsiveness to LOS attributes with those that adopt a nonlinear responsiveness to LOS attributes. The empirical analysis uses the Austin Commuter Stated Preference Survey data to examine commute travel mode choice. The nonlinear specifications for travel time and travel time unreliability indicate that commuters place a small value to travel time, and a very high value to travel time reliability, in the first 15 minutes. Beyond 15 minutes, however, the valuation of travel time increases rapidly, while that of travel time reliability drops dramatically. In addition to clearly indicating the nonlinear nature of responsiveness to travel time and travel time unreliability, the results indicate that ignoring nonlinear responsiveness leads, in the current empirical context, to (a) biased parameter estimates, (b) an inflated estimate of unobserved heterogeneity, (c) counterintuitive signs on the LOS variables for a high fraction of individuals, (d) inaccurate estimates of willingness-to-pay measures, and (e) loss in model fit.