Probability-Aware Parking Selection

TL;DR

This paper introduces a probabilistic parking selection model that improves driver routing by accounting for parking search times, significantly reducing expected travel time and enhancing navigation efficiency.

Contribution

It proposes a dynamic programming framework for probability-aware parking selection, incorporating stochastic availability estimates to optimize expected time-to-arrive.

Findings

Probability-aware strategies reduce travel time by up to 66%.

Model accuracy improves as observation frequency increases, with error rates dropping below 2%.

The approach effectively accounts for dynamic parking availability in real-world scenarios.

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.