# Optimizing Curbside Parking Resources Subject to Congestion Constraints

arXiv: https://arxiv.org/abs/1703.07802 · HTML: https://mlsanity.com/paper/1703.07802/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2017.

## Built on by (most cited first, 2 of 2)

- [Decision-Dependent Risk Minimization in Geometrically Decaying Dynamic Environments](https://mlsanity.com/paper/2204.08281/) (2022)
- [Learning to Control Unknown Strongly Monotone Games](https://mlsanity.com/paper/2407.00575/) (2024)

## Abstract (arXiv)

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.