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

Pierpaolo Resce, Lukas Vorwerk, Zhiwei Han, Giuliano Cornacchia, Omid Isfahani Alamdari, Mirco Nanni, Luca Pappalardo, Daniel Weimer und Yuanting Liu

SIGSPATIAL '22: Proceedings of the 30th International Conference on Advances in Geographic Information Systems,

November 2022 · DOI: ttps://


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.