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A Real-Time Parking Prediction System for Smart Cities
Abstract
A methodological framework for multiple steps ahead parking availability prediction is presented. Two different types of predictions are provided: the probability of a free space to continue being free in subsequent time intervals, and the short-term parking occupancy prediction in selected regions of an urban road network. The available data come from a wide network of on-street parking sensors in the “smart” city of Santander, Spain. The sensor network is segmented in four different regions, and then survival and neural network models are developed for each region separately. Findings show that the Weibull parametric models best describe the probability of a parking space to continue to be free in the forthcoming time intervals. Moreover, simple genetically optimized multilayer perceptrons accurately predict region parking occupancy rates up to 30 minutes in the future by exploiting 1-minute data. Finally, the real time, Web-based, implementation of the proposed parking prediction availability system is presented.
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Caragliu, A., Del Bo, C., & Nijkamp, P. (2011) · Journal of Urban Technology · doi:10.1080/10630732.2011.601117 · Cited by 3,409
Cited by
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Al‐Turjman, F., Zahmatkesh, H., & Shahroze, R. (2022) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.3677 · Cited by 313
Zou, W., Sun, Y., Zhou, Y., Lu, Q., Nie, Y., Sun, T., & Peng, L. (2022) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2020.2970185 · Cited by 84
Metadata
- Authors
- Vlahogianni, E. I., Kepaptsoglou, K., Tsetsos, V., & Karlaftis, M. G.
- Venue
- Journal of Intelligent Transportation Systems
- Publisher
- Informa UK Limited
- Published
- 2015-06-15
- Volume
- 20
- Issue
- 2
- Pages
- 192-204
- ISSN
- 1024-8072 (ISSN-L)
- 1547-2450
- 1547-2442
- OpenAlex
- W2055651663
- Cited by
- 246
- References
- 1
- FWCI
- 19.84
- Type
- journal article
- Language
- en
- Open access
- closed
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- Vlahogianni, E. I., Kepaptsoglou, K., Tsetsos, V., & Karlaftis, M. G. (2016). A Real-Time Parking Prediction System for Smart Cities. Journal of Intelligent Transportation Systems. https://doi.org/10.1080/15472450.2015.1037955
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
