Parking Availability Prediction

Nov 23, 2021

Designed an end-to-end transfer learning framework to sense parking availability and predict occupancy.

Worked with limited data inputs and dynamic parking availability sensor data.

Aided by heterogeneous spatio-temporal contextual data from external environment (weather, points of interest, etc) and 35 million+ parking data records from sensors in two different cities.

Framework based on adversarial domain adaptation predicts parking occupancy with accuracy similar to existing state-of-the-art methods.

Main Participants

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© 2021 Flora Salim - CRUISE Research Group.