Probability-Aware Parking Selection

2026-01-02

arXiv.org

AI Summary

This study addresses a critical limitation in existing parking navigation systems, which neglect search time and thereby introduce significant errors in trip duration estimation, degrading user experience and traffic efficiency. The authors propose a probabilistic-aware parking selection framework that explicitly incorporates the probability of parking availability into route planning—a formulation introduced here for the first time. They develop a dynamic programming approach to optimize target parking spot selection, yielding a closed-form solution that guides drivers on when to commit to a specific lot versus continuing to explore alternatives. The method also provides theoretical error bounds for occupancy rate estimation based on stochastic observations. Experiments using real-world data from Seattle demonstrate that increasing observation frequency reduces the mean absolute error of availability estimation from 7% to below 2%, and achieves up to a 66% reduction in total travel time compared to non-probabilistic strategies.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• parking navigation
• travel time estimation
• parking availability
• probability-aware decision-making
• urban mobility

Innovation

• probability-aware parking
• dynamic programming
• parking availability estimation
• stochastic observation
• travel time optimization