Psychological items: a useful addition in modeling travel behavior on managed lanes

Abstract

Managed lanes (MLs) are a tool to more efficiently operate segments of a freeway. As ML prevalence increases in the United States of America, it is important to understand travel behavior in a ML setting (i.e., lane choices and carpooling decisions). Socio-demographic and trip data, along with travel time and toll, have been commonly used in this endeavor. However, there are some travelers who pay to use the ML despite there being little to no improvement in travel time over the adjacent general purpose lanes. This gives rise to the possibility that psychological traits and characteristics are a greater influence on ML use than even travel time savings for some travelers. This research examined this issue through a set of largely transportation-framed psychological items. After an initial creation and refining process, 25 psychological items were included in a survey advertised in five cities with MLs. In addition to psychological items, trip and demographic questions, and three SP questions were included in the online survey. Mixed logit models were estimated based on survey responses obtained from three study areas. Models that included psychological items performed better (in terms of adjusted rho squared value and percent correctly predicted values) than models with only trip and demographic variables. Likewise, models including psychological items plus trip and demographic data performed best. This information may be useful for traffic and revenue estimating firms interested in potentially including psychological items in future ML surveys intended to facilitate better estimation of ML use.