Approximation of Search Times for On-street Parking Based on Supply and Demand

TL;DR

This paper introduces a method to estimate the probability of long parking searches in cities using detailed supply and demand maps, verified against agent-based models, with practical application to Bat Yam.

Contribution

The paper presents a novel approach for approximating parking search times using high-resolution demand and supply data, validated against agent-based simulations.

Findings

In Bat Yam, high demand in city center causes parking searches over 10 minutes.

Despite a low overall demand-to-supply ratio of 0.65, long searches are localized.

The method can inform urban planning to improve parking efficiency.

Abstract

We propose a method for approximating the probability p({\tau}, n) 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 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 parking searches of longer than 10 minutes. We discuss the application of the proposed approach for urban planning.