| Title | A Generic Predictive Model for On-Street Parking Availability |
|---|---|
| ID_Doc | 40383 |
| Authors | Unlu, E; Delfau, JB; Nguyen, B; Chau, E; Chouiten, M |
| Title | A Generic Predictive Model for On-Street Parking Availability |
| Year | 2020 |
| Published | |
| 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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