Prediction of Car Parking Occupancy in Urban Areas Using Geostatistics

• Thales Cervi Universidade Tecnológica Federal do Paraná (UTFPR)
• Luiz Melo Jr. Universidade Tecnológica Federal do Paraná (UTFPR) https://orcid.org/0000-0003-0563-3889
• Myriam Delgado Universidade Tecnológica Federal do Paraná (UTFPR) https://orcid.org/0000-0002-2791-174X
• Semida Silveira Cornell University https://orcid.org/0000-0001-7123-1824
• Ricardo Lüders Universidade Tecnológica Federal do Paraná (UTFPR) https://orcid.org/0000-0001-6483-4694

DOI: https://doi.org/10.5753/sbsi_estendido.2022.222975

Resumo

Public on-street car parking is an important shared resource of a city infrastructure with a significant impact on traffic. This paper proposes a geostatistical model aimed to predict parking occupancy rates for different periods of the day. In the study case, the occupancy representation considers the georeferenced position of spots for a particular area of Los Angeles (USA). Different models are compared and their parameters are estimated using the available dataset of the parking area. The final model is chosen to generate a kriging map that helps to understand and predict the occupancy rates. The end goal is to open doors for modeling and predicting urban phenomenons with Geostatistics to help with planning public parking policies in high density urban areas.

Palavras-chave: parking, forecasting, geostatistics, smart city

Referências

Al-Turjman, F. and Malekloo, A. (2019). Smart parking in IoT-enabled cities: A survey. Sustainable Cities and Society, 49.

Awan, F. M., Saleem, Y., Minerva, R., and Crespi, N. (2020). A comparative analysis of machine/deep learning models for parking space availability prediction. Sensors (Switzerland), 20.

Bernardini, F., Viterbo, J., Vianna, D., Martins, C., Medeiros, A., Meza, E., Moratori, P., and Bastos, C. (2017). General features of smart city approaches from information systems perspective and its challenges. In Boscarioli, C., Araujo, R., and Maciel, R., editors, I GranDSI-BR: Grand Research Challenges in Information Systems in Brazil 2016-2026, pages 25–40. Brazilian Computer Society (SBC).

Caicedo, F., Blazquez, C., and Miranda, P. (2012). Prediction of parking space availability in real time. Expert Systems with Applications, 39.

Camero, A., Toutouh, J., Stolfi, D. H., and Alba, E. (2019). Evolutionary deep learning for car park occupancy prediction in smart cities. volume 11353 LNCS.

Inci, E. (2015). A review of the economics of parking. Economics of Transportation, 4.

LADOT (2022). Los Angeles city open data: Parking meter occupancy.

Lin, T., Rivano, H., and Mouel, F. L. (2017). A survey of smart parking solutions. IEEE Transactions on Intelligent Transportation Systems, 18.

Rajabioun, T. and Ioannou, P. (2015). On-street and off-street parking availability pre- diction using multivariate spatiotemporal models. IEEE Transactions on Intelligent Transportation Systems, 16.

Ribeiro, P. J. and Diggle, P. J. (2001). geoR: A package for geostatistical analysis. R- NEWS, 1(2).

Ribeiro, P. J. and Diggle, P. J. (2007). Model-based Geostatistics. Hardcover, 1 edition.

Shoup, D. C. (2006). Cruising for parking. Transport policy, 13(6):479–486.

Stolfi, D. H., Alba, E., and Yao, X. (2017). Predicting car park occupancy rates in smart cities. volume 10268 LNCS.

Vlahogianni, E. I., Kepaptsoglou, K., Tsetsos, V., and Karlaftis, M. G. (2016). A real- time parking prediction system for smart cities. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 20.

Zhu, Y., Ye, X., Chen, J., Yan, X., and Wang, T. (2020). Impact of cruising for parking on travel time of traffic flow. Sustainability (Switzerland), 12.