Asymptotics of Parking Search in Hyperfractal Networks
Provides asymptotic scaling laws for parking search in fractal networks, relevant for urban planning and network theory.
The paper derives the asymptotic expected distance to the first available parking slot in hyperfractal networks, showing a power-law decay with exponent equal to the inverse hyperfractal dimension. The exponent is robust under random multiplicative modulations of street intensities.
We study the asymptotic behaviour of the distance to the first available parking slot in a recursive Manhattan street network endowed with a hyperfractal intensity structure, where slot-release events occur according to Poisson processes along the streets. We establish, by analysing the associated self-similar harmonic sums via Mellin-transform asymptotics, a power-law decay of the expected distance as the total intensity grows, with exponent equal to the inverse of the hyperfractal dimension. In particular, the scaling exponent depends only on the large-scale geometry of the network. We further prove that this exponent is robust under random multiplicative modulations of the street intensities: mild stochastic heterogeneity affects only the multiplicative constant. Similar scaling behaviour holds for the variance, the number of turns before parking, and for a jump-over variant of the search strategy.