Estimating on-street parking occupancy using smart meter data

TL;DR

This paper presents a novel method to estimate on-street parking occupancy and cruising using only parking meter payment data, eliminating the need for additional sensing technologies, and validated with real and simulated data.

Contribution

It introduces a Particle Markov Chain Monte Carlo approach to accurately estimate parking occupancy from payment transactions, a scalable method using existing city data.

Findings

The approach provides asymptotically unbiased estimates of parking occupancy.

It accurately estimates model parameters like arrival rates and parking times.

Validated with real SFpark data and simulated queue data.

Abstract

The excessive search for parking, known as cruising, generates pollution and congestion. Cities are looking for approaches that will reduce the negative impact associated with searching for parking. However, adequately measuring the number of vehicles in search of parking is difficult and requires sensing technologies. In this paper, we develop an approach that eliminates the need for sensing technology by using parking meter payment transactions to estimate parking occupancy and the number of cars searching for parking. The estimation scheme is based on Particle Markov Chain Monte Carlo. We validate the performance of the Particle Markov Chain Monte Carlo approach using data simulated from a GI/GI/s queue. We show that the approach generates asymptotically unbiased Bayesian estimates of the parking occupancy and underlying model parameters such as arrival rates, average parking time, and the payment compliance rate. Finally, we estimate parking occupancy and cruising using parking meter data from SFpark, a large scale parking experiment and subsequently, compare the Particle Markov Chain Monte Carlo parking occupancy estimates against the ground truth data from the parking sensors. Our approach is easily replicated and scalable given that it only requires using data that cities already possess, namely historical parking payment transactions.