  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.
