Document - Connected vehicle simulation framework for parking occupancy prediction (demo paper)

2022

Conference article Open Access

Connected vehicle simulation framework for parking occupancy prediction (demo paper)

Resce P., Vorwerk L., Han Z., Cornacchia G., Alamdari O. I., Nanni M., Pappalardo L., Weimer D., Liu Y.

Traffic simulation Parking occupancy prediction Connected car

This paper demonstrates a simulation framework that collects data about connected vehicles' locations and surroundings in a realistic traffic scenario. Our focus lies on the capability to detect parking spots and their occupancy status. We use this data to train machine learning models that predict parking occupancy levels of specific areas in the city center of San Francisco. By comparing their performance to a given ground truth, our results show that it is possible to use simulated connected vehicle data as a base for prototyping meaningful AI-based applications.

Source: SIGSPATIAL '22 - 30th International Conference on Advances in Geographic Information Systems, Seattle, Washington, 1-4/11/2022

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@inproceedings{oai:it.cnr:prodotti:477678,
	title = {Connected vehicle simulation framework for parking occupancy prediction (demo paper)},
	author = {Resce P. and Vorwerk L. and Han Z. and Cornacchia G. and Alamdari O. I. and Nanni M. and Pappalardo L. and Weimer D. and Liu Y.},
	doi = {10.1145/3557915.3560995},
	booktitle = {SIGSPATIAL '22 - 30th International Conference on Advances in Geographic Information Systems, Seattle, Washington, 1-4/11/2022},
	year = {2022}
}