Statistical modeling of on-street parking lot occupancy in smart cities

TL;DR

This paper introduces a statistical semi-Markov model for predicting on-street parking occupancy, leveraging real-time data to improve guidance and reduce search time in urban environments.

Contribution

It applies semi-Markov process theory with Laplace transformations to parking data, demonstrating improved prediction accuracy over traditional Markov models.

Findings

Semi-Markov model outperforms Markov model in accuracy.

Model respects current parking duration for better predictions.

Application to Melbourne data validates the approach.

Abstract

Many studies suggest that searching for parking is associated with significant direct and indirect costs. Therefore, it is appealing to reduce the time which car drivers spend on finding an available parking lot, especially in urban areas where the space for all road users is limited. The prediction of on-street parking lot occupancy can provide drivers a guidance where clear parking lots are likely to be found. This field of research has gained more and more attention in the last decade through the increasing availability of real-time parking lot occupancy data. In this paper, we pursue a statistical approach for the prediction of parking lot occupancy, where we make use of time to event models and semi-Markov process theory. The latter involves the employment of Laplace transformations as well as their inversion which is an ambitious numerical task. We apply our methodology to data from the City of Melbourne in Australia. Our main result is that the semi-Markov model outperforms a Markov model in terms of both true negative rate and true positive rate while this is essentially achieved by respecting the current duration which a parking lot already sojourns in its initial state.