Current navigation systems conflate time-to-drive with the true time-to-arrive by ignoring parking search duration and the final walking leg. Such underestimation can significantly affect 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 that leverages probabilistic, lot-level availability to minimize the expected time-to-arrive. 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. Given the high cost of permanent sensing infrastructure, we assess the error rates of using stochastic observations to estimate 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 increases. 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 time-to-drive estimates.

翻译：当前导航系统通过忽略停车搜索时长和最终步行距离，将行驶时间与真实到达时间混为一谈。这种低估可能显著影响用户体验、出行方式选择、交通拥堵和排放。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。我们提出了一种适应性动态规划框架，该框架利用概率化的停车场级可用性信息来最小化期望到达时间。通过闭式分析确定了何时以特定停车场为目标最优、何时探索替代方案，以及相应的期望时间成本。敏感性分析和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。鉴于永久性传感基础设施的高成本，我们评估了使用随机观测数据估计可用性的误差率。基于美国西雅图市真实数据的实验表明该方法具有可行性：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可实现高达66%的时间节省，但仍比行驶时间估计多耗费长达123%的时间。