MSc Thesis - UvA - Analysing and Predicting the Parking Occupancy of Micromobility Vehicles on Sidewalks - Johanna Fiebag

Master Thesis by Johanna Fiebag

Micromobility vehicles, such as bicycles, are often left on the sidewalk, where they limit the space of the already narrow pedestrian zone. A better understanding of micromobility parking and the possibility to predict the demand is needed to improve the management of these facilities and ultimately to prevent the obstruction of public space. Previous research was mainly focused on the parking of other vehicles, such as cars, introducing a lack of research and data related to micromobility parking. Therefore, this research aimed to use historical counts of the number of parked micromobility vehicles along with neighborhood characteristics to analyze and predict the parking occupancy on the sidewalk.

To achieve this goal, both supervised and unsupervised machine learning techniques were applied. Tree-based ensemble models proved to be suitable for predicting parking occupancy. In terms of predictive features, historical observations were the most influential predictor. The inclusion of the cluster results and neighborhood variables such as land use and the presence of points of interest further improved the predictions. Furthermore, clustering has made it possible to summarize multivariate information and to identify areas of similar characteristics.


This research was conducted by Johanna Fiebag in collaboration with AI Team, Urban Innovation and R&D, City of Amsterdam.

Involved civil servants: Shayla Jansen & Cláudia Pinhão & Lino Miltenburg

Supervisors: Shayla Jansen & Frank Nack

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