Abstract
Despite the previously demonstrated considerable negative effects of on-street parking availability on a city’s traffic flux, the developed literature on this issue is far from being voluminous. It is shown that, the duration for finding a vacant parking space consume a sizeable portion of a driver’s time. Especially, for huge megacities, even small, local traffic disturbances can generate chaotic results due to their complex, inter-connected nature. Hence, being able to predict the probability of finding a vacant on-street parking place on a spot at a given time up to a reasonable degree shall be at paramount of interest for future smart-city oriented conurbations. In this paperwork, we present a generic framework supported by a machine learning model, which predicts the spatio-temporal on-street parking availability, where spots are characterized according to amenities in their vicinity.
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Unlu, E., Delfau, JB., Nguyen, B., Chau, E., Chouiten, M. (2020). A Generic Predictive Model for On-Street Parking Availability. In: Martins, A., Ferreira, J., Kocian, A. (eds) Intelligent Transport Systems. From Research and Development to the Market Uptake. INTSYS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 310. Springer, Cham. https://doi.org/10.1007/978-3-030-38822-5_4
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DOI: https://doi.org/10.1007/978-3-030-38822-5_4
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