Regret aversion and anchoring effects in advanced parking information system adoption: The behavioral economics of parking choices

Advanced parking information systems (APISs) aim to alleviate urban parking challenges by providing real-time information and reservations. Their effectiveness, however, depends on how drivers respond to these services when they coexist with uncertain current parking options. This decision process is shaped by psychological mechanisms which standard utility-maximizing models often fail to capture. This study addresses this gap by examining drivers’ APIS choices through an anchored generalized random regret minimization (AG-RRM) model, extended within a latent class framework. By introducing the Self-Report Parking Habit Index (SRPHI), we further quantify the strength of ingrained parking routine and its impact on APIS choices. Results from a stated preference survey reveal parking behavior spans a spectrum from asymmetric regret aversion to linear attribute trade-offs. Familiar current parking serves as a reference anchor associated with stronger regret responses when APIS options underperform. We identify two distinct driver segments based on SRPHI scores. Efficiency-oriented drivers (38.8%) respond rationally to quantifiable APIS benefits. Conversely, habitual or anchor-bound drivers (61.2%) represent the majority, exhibiting stronger regret aversion and anchoring-related responses involving current parking. For the anchor-bound majority, the estimated responses suggest that marginal price signals alone may be insufficient to nudge adoption. These insights provide a behavioral foundation for personalized measures and de-anchoring interventions promoting APIS usage. We suggest APIS operators leverage psychological nudges, such visualizing invisible cruising costs, to weaken the perceived surcharge-free advantage of current parking options. By reframing the mental anchor, such non-monetary interventions have the potential to address behavioral inertia and improve parking efficiencies.