A macro-micro approach to modeling parking. (May 2021)

• Record Type: Journal Article
• Authors: Gu, Ziyuan; Safarighouzhdi, Farshid; Saberi, Meead; Rashidi, Taha H.
• Is Part Of: Transportation research. Volume 147(2021)
• Page Start: 220
• Page End: 244
• Publication Date: 2021-05
• Subjects: Parking pricing -- Macroscopic fundamental diagram -- Network fundamental diagram -- Cruising-for-parking -- Microscopic simulation -- Macro-micro integration; Transportation -- Research -- Periodicals; Transportation -- Mathematical models -- Periodicals
• DOI: 10.1016/j.trb.2021.03.012
• Languages: English
• ISSNs: 0191-2615

Abstract

Highlights: A microscopic model is developed considering heterogeneous parking search. Low cruising speed does not significantly alter the NFD unless cruising vehicles dominate. Multiscale parking occupancy-driven intelligent parking guidance can reduce distance to park. A macroscopic model is developed, calibrated and validated vs. the microsimulation. A macro-micro approach to modeling parking is proposed via model predictive control. Abstract: 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 simulated 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 duration also contribute; and (iii) multiscale parking occupancy-driven intelligent parking guidance can reduce 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 accommodate the increased parking demand; otherwise, local congestion may arise that violates the homogeneity assumption underlying the macroscopic model.