Priced managed lanes (MLs) have been widely implemented in metropolitan areas to provide faster and more reliable travel while generating revenue to fund transportation improvements. Successful implementation of MLs relies on the careful selection of roadways on which to build MLs. This, in turn, relies on accurate travel demand models. Although existing models have been effective in forecasting aggregate travel demand and toll revenues, they often fall short in capturing individual lane decisions. Notably, MLs do not always deliver faster travel compared with adjacent toll-free lanes, called general-purpose lanes. Instances in which MLs have resulted in longer travel times can lead to user distrust and dissatisfaction. Despite this, there is no prior research investigating how negative ML experiences affect future lane choice behavior. Therefore, this study employs a logit regression analysis using 30 months of data from the Katy Freeway, Houston, TX to model individual lane choice decisions and examine the impact of slower ML events on subsequent travel behavior. The results indicated that drivers tend to pay tolls for using MLs when toll rates and speeds are higher. This indicates that travelers are generally willing to pay for faster travel. Conversely, travelers who experienced or observed slower ML events were less likely to use MLs in the future, revealing risk-averse behavior. Although this effect diminished over time as drivers tended to forget the event, repeated exposure to similar incidents reinforced the memory and prolonged the deterrent effect.
Leungbootnak et al. (Thu,) studied this question.