Current parking navigation systems often underestimate total travel time by failing to account for the time spent searching for a parking space, which significantly affects user experience, mode choice, congestion, and emissions. To address this issue, this paper introduces the probability-aware parking selection problem, which aims to direct drivers to the best parking location rather than straight to their destination. An adaptable dynamic programming framework is proposed for decision-making based on probabilistic information about parking availability at the parking lot level. Closed-form analysis determines when it is optimal to target a specific parking lot or explore alternatives, as well as the expected time cost. Sensitivity analysis and three illustrative cases are examined, demonstrating the model's ability to account for the dynamic nature of parking availability. Acknowledging the financial costs of permanent sensing infrastructure, the paper provides analytical and empirical assessments of errors incurred when leveraging stochastic observations to estimate parking availability. Experiments with real-world data from the US city of Seattle indicate this approach's viability, with mean absolute error decreasing from 7% to below 2% as observation frequency grows. In data-based simulations, probability-aware strategies demonstrate time savings up to 66% relative to probability-unaware baselines, yet still take up to 123% longer than direct-to-destination estimates.

翻译：当前停车导航系统常因未考虑寻找停车位所耗时间而低估总行程时间，这显著影响用户体验、出行方式选择、交通拥堵及排放。为解决此问题，本文提出概率感知停车选择问题，其目标是将驾驶员引导至最佳停车位置而非直接前往目的地。我们提出一种基于停车场层面停车可用性概率信息的自适应动态规划决策框架。通过闭式分析确定了何时以特定停车场为目标或探索替代方案为最优策略，并计算了预期时间成本。敏感性分析与三个示例案例研究表明，该模型能够有效处理停车可用性的动态特性。考虑到永久性传感基础设施的经济成本，本文通过解析与实证评估量化了利用随机观测估计停车可用性时产生的误差。基于美国西雅图市真实数据的实验表明该方法的可行性：随着观测频率增加，平均绝对误差从7%降至2%以下。在数据驱动的仿真中，概率感知策略相较于无概率感知基线方案最高可节省66%的时间，但仍比直达目的地预估耗时最多长123%。