Multi-dimensional scaling of occupancy prediction without the need for structural changes to parking areas by using Floating Car Data and individual characteristics of P+R facilities
Consortium partners: ivm GmbH, [ui!] urban mobility innovations
Duration: September 2022 until August 2025
Funded by: Federal Ministry of Digital Affairs and Transport
Project Lead: Prof. Dr.-Ing. Petra Schäfer
Contact: Seray Künbet and Jonas Hamann
P+R facilities are effective tools to promote intermodal traffic behaviour. One of the most important factors for users is the probability of finding a free parking space. If there is too much uncertainty about the occupancy, the attractiveness of using the car may appear higher compared to a combination of P+R and public transportation. A reliable and area-wide occupancy forecast of P+R facilities can enable intramodality and optimize the utilization of such facilities. Therefore, the aim of the proposed project is to cluster P+R facilities and thus transfer/apply the expected results to all P+R facilities in the area of investigation. Using machine learning algorithms, a prediction model will be implemented for each facility-type to forecast the occupancy/ utilization. Given the predictions of the model park search duration will be reduced and the transition between motorized private transport and public transportation will be increased.
