A macro-micro approach to modeling parking

In this paper, we propose a new macro-micro approach to modeling parking. We first develop microscopic parking simulation model considering both on- and off-street with limited capacity. the model, search algorithm is proposed mimic cruising-for-parking based on principle of proximity, parking-related state tracking acquire an event-based simulated data set. Some key aspects are discussed sim-ulated evidence theoretical analysis. Results suggest (i) although low cruising speed reduces network performance, it does not significantly alter macroscopic or fundamental diagram (MFD NFD) unless vehicles dominate traffic stream; (ii) distance park uniquely determined by occupancy because factors such as dura-tion also contribute; (iii) multiscale occupancy-driven intelligent guidance can re-duce yielding considerable efficiency gains. Using then extend, calibrate, validate dynamics NFD representation. The demonstrated consistency between macro- micro-models permits integration two for online pricing optimization via predictive control. Numerical experiments highlight effectiveness well one caveat. That is, when on-street parking, road connected alternate lots must have sufficient capacity ac-commodate increased demand; otherwise, local congestion may arise that violates ho-mogeneity assumption underlying model.