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