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We propose a method for approximating the probability p({\tau}, n) ofsearching for on-street parking longer than time {\tau} from the start of aparking search near a given destination n, based on high-resolution maps ofparking demand and supply in a city. We verify the method by comparing itsoutcomes to the estimates obtained with an agent-based model of on-streetparking search. As a practical example, we construct maps of cruising time forthe Israeli city of Bat Yam, and demonstrate that despite the low overalldemand-to-supply ratio of 0.65, excessive demand in the city center results inparking searches of longer than 10 minutes. We discuss the application of theproposed approach for urban planning.

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