| 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 |
|
| DOI |
10.1007/978-3-030-38822-5_4 |
| 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. |
| Author Keywords |
On-street parking; Machine learning; Smart city |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000656432500004 |
| WoS Category |
Operations Research & Management Science; Transportation Science & Technology |
| Research Area |
Operations Research & Management Science; Transportation |
| PDF |
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