Fachbereich Architektur und Bauingenieurwesen

Siavash Saki

Thema (Englisch) Using Machine Learning Techniques to Explain and Predict Cruising for Parking
Start / Ende der Promotion: 01.05.22 bis 31.12.23

1. Prüfer:In / Hochschule / Universität Prof. Dr. Tobias Hagen / Hochschule Frankfurt

2. Prüfer:In / Hochschule / Universität Prof. Dr. Matthias Kowald / Hochschule RheinMain Fachbereich AB / Fachgruppe Mobilitätsmanagement

Based on the previous research, the following research gaps can be identified and will be closed
1. No clear definition of the used terms with regard to cruising for parking
2. In previous research, the start of parking search is unknown, leading to incorrect data collection and estimations
3. Previous studies fail to estimate causal effects since they neglect the counterfactual
4. Previous studies fail to reveal determinants of cruising due to inadequate data
5. No parking search predictions particularly designed for drivers
6. No general city segmentation method for defining city districts
7. No analysis of parking search duration separated by city districts