Spatiotemporal Clustering of Parking Lots at the City Level for Efficiently Sharing Occupancy Forecasting Models.
- DOI
- 10.3390/s23115248
- Published
- 2023 May 31
- Container
- Sensors (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/s23115248,
title = {Spatiotemporal Clustering of Parking Lots at the City Level for Efficiently Sharing Occupancy Forecasting Models.},
author = {Mufida MK and Ait El Cadi A and Delot T and Trépanier M and Zekri D},
year = {2023},
journal = {Sensors (Basel, Switzerland)},
doi = {10.3390/s23115248},
url = {https://doi.org/10.3390/s23115248}
}RIS
TY - JOUR TI - Spatiotemporal Clustering of Parking Lots at the City Level for Efficiently Sharing Occupancy Forecasting Models. AU - Mufida MK AU - Ait El Cadi A AU - Delot T AU - Trépanier M AU - Zekri D PY - 2023 JO - Sensors (Basel, Switzerland) DO - 10.3390/s23115248 UR - https://doi.org/10.3390/s23115248 ER -
APA
MK, M., A, A. E. C., T, D., M, T., & D, Z. (2023). Spatiotemporal Clustering of Parking Lots at the City Level for Efficiently Sharing Occupancy Forecasting Models.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s23115248
Source records
- pubmed · retrieved 2026-09-27T12:15:33.504Z