Context-dependent uncertainty-aware parking choices of autonomous car owners

As autonomous vehicle (AV) technology advances, effectively managing parking behavior has become increasingly critical for urban parking systems, traffic congestion, and transportation sustainability. This study aims to investigate AV owners’ parking choices under various activity-travel contexts, uncertainty conditions, and parking attributes. A stated choice experiment was designed, and a mixed logit cumulative prospect theoretical model was developed to analyze parking decisions under uncertain waiting times underlying different parking modes. The findings indicate that individuals exhibit risk preferences in response to waiting time uncertainty. When waiting times are shorter than expected, they show risk aversion with diminishing sensitivity to further reductions. Conversely, longer-than-expected waits lead to risk-seeking behavior, heightened sensitivity, and biases in probability perception. These risk preferences significantly influence individuals' parking choices, particularly in prolonged, uncertain waiting scenarios. Additionally, travel context factors (e.g., purpose, activity duration, and idle time) and parking attributes (e.g., cost, revenue, and social influence) significantly influence AV owners' parking decisions, along with socio-demographic characteristics (e.g., age, education, and car ownership). The findings provide valuable insights for urban planners, policymakers, and car-sharing platforms, suggesting flexible pricing strategies and the promotion of shared mobility services to enhance parking efficiency and support sustainable transportation development.