@conference {1493,
	title = {Connected Vehicle Simulation Framework for Parking Occupancy Prediction (Demo Paper)},
	booktitle = {Proceedings of the 30th International Conference on Advances in Geographic Information Systems},
	year = {2022},
	publisher = {Association for Computing Machinery},
	organization = {Association for Computing Machinery},
	address = {New York, NY, USA},
	abstract = {This paper demonstrates a simulation framework that collects data about connected vehicles{\textquoteright} 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.},
	isbn = {9781450395298},
	doi = {10.1145/3557915.3560995},
	url = {https://doi.org/10.1145/3557915.3560995},
	author = {Resce, Pierpaolo and Vorwerk, Lukas and Han, Zhiwei and Cornacchia, Giuliano and Alamdari, Omid Isfahani and Mirco Nanni and Luca Pappalardo and Weimer, Daniel and Liu, Yuanting}
}
