2026-02-19 マサチューセッツ工科大学
MIT researchers have developed a parking-aware navigation system that helps drivers identify lots offering the optimal balance between proximity to their destination and the likelihood of available spaces.Credit: iStock
＜関連情報＞
- https://news.mit.edu/2026/parking-aware-navigation-could-prevent-frustration-and-emissions-0219
- https://arxiv.org/abs/2601.00521
確率を考慮した駐車場選択 Probability-Aware Parking Selection
Cameron Hickert, Sirui Li, Zhengbing He, Cathy Wu
arXiv last revised 1 Feb 2026 (this version, v2)
DOI:https://doi.org/10.48550/arXiv.2601.00521
Abstract
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.
