Approximation Method for Estimating Search Times for On-Street Parking

We propose an approximation method for estimating the probability $p\left(\tau ,n\right)$ of searching for on-street parking longer than time $\tau$ from the start of a parking search near a given destination $n$ based on high-resolution maps of parking demand and supply in a city. We verify the method by comparing its outcomes to the estimates obtained with an agent-based simulation model of on-street parking search. As a practical example, we construct maps of cruising time for the Israeli city of Bat Yam and demonstrate that, despite the low overall demand-to-supply ratio of 0.65, excessive demand in the city center results in a significant share of parking searches that last longer than 5 or even 10 minutes. We discuss the application of the proposed approach for urban planning.