Optimizing curbside parking resources subject to congestion constraints

Abstract

To gain theoretical insight into the relationship between parking scarcity and congestion, we describe block-faces of curbside parking as a network of queues. Due to the nature of this network, canonical queueing network results are not available to us. We present a new kind of queueing network subject to customer rejection due to the lack of available servers. We provide conditions for such networks to be stable, a computationally tractable "single node" view of such a network, and show that maximizing the occupancy through price control of such queues, and subject to constraints on the allowable congestion between queues searching for an available server, is a convex optimization problem. We demonstrate an application of this method in the Mission District of San Francisco; our results suggest congestion due to drivers searching for parking stems from an inefficient spatial utilization of parking resources.

The Nature of the On-Street Parking Search

“…Millard-Ball et al (Millard-Ball et al 2014) established a queueing model to evaluate the relationship between occupancy and the amount of cruising vehicles when studying the impacts of the San Francisco SFPark adaptive parking pricing program. Dowling et al (Dowling et al 2017) (Dowling et al 2018) (Dowling et al 2020) consider parking search as a process that can be described by a network of interacting queues, each representing cars that want to park at a certain link, with cruising cars migrating between queues in respect to the network topology. They apply their model to estimate the share of cruising drivers in the neighborhood of Belltown, Seattle, WA, USA and find that at the most demanded streets cruising drivers can comprise up to 50% of the traffic.…”

Decision-Dependent Risk Minimization in Geometrically Decaying Dynamic Environments

“…For example, many municipalities announce quarterly a new quasi-static set of prices for on-street parking. In this setting, the institution may adjust parking rates for certain blocks in order to to achieve a desired occupancy range to reduce cruising phenomena and increase business district vitality (Fiez et al 2018;Dowling et al 2017;Pierce and Shoup 2013;Shoup 2006). For instance, in high traffic areas, the institution may announce increased parking rates to free up parking spots and redistribute those drivers to less populated blocks.…”

A study on parking supply optimization in central business districts considering the two way interaction between car traveling and parking

“…Regarding these mechanisms, Dowling et al (2017) found that drivers who arrive in search of parking find that all spaces occupied must move onto an adjacent block face. This search dynamic driven by the rate of drivers turned away from a full block face is representative of the impact of drivers searching for parking and, hence, the impact of parking on through traffic.…”