Statistical modelling of on-street parking spot occupancy in smart cities

Abstract Many studies suggest that searching for parking is associated with significant direct and indirect costs. Therefore, it is appealing to reduce the time that car drivers spend on finding an available parking spot, especially in urban areas where the space for all road users is limited. The prediction of on-street parking spot occupancy can provide drivers with guidance on where clear parking spaces 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 spot occupancy data. In this paper, we pursue a statistical approach for the prediction of parking spot 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 that a parking space already spends in its initial state.