As part of the cooperation between ISMLL and DnA three data-driven case studies are being investigated: Predictive Parking, Damage Assessment, Residual Value

Work Package 1: Predictive Parking

By applying advanced Machine Learning techniques, it is possible to predict free parking spots and enhance with them FS parking apps. Hence, the integration enables a more convenient and seamless user experience for drivers. This in turn will increase the usage of the parking apps and possibly enable new pricing-options for the new service. The idea is to identify customer behavior, seasonality or peaks of parking behavior to predict available parking spots. This is done by using historical and real-time on-street parking data and transaction capabilities. In addition, existing data can be enriched by using publicly available open data, i.e. weather forecasts, city infrastructure or events, to improve the model.

Work Package 2: Automatic Damage Assessment for Cars

Machine Learning as well as sophisticated object detection and image processing techniques can help in improving the mobile customer experience by knowing and avoiding turn-in penalties beforehand. This will be achieved by providing a smart digital assistant on the mobile phone device using its internal camera only. The idea is to streamline the lease-end process. Undoubtedly, there is more the customer can do besides washing the car and cleaning from the inside before the hand-over to minimize costly penalties. There are supposed to be almost identical, recurring damage patterns when returning leased vehicles which can be used to make predictions on current images based on labeled past ones (supervised learning). The initial model generation will be based on a large set of historic, labeled and priced pictures and inspection reports.

Work Package 3: Residual Value Forecast for Cars

Given the car’s configuration settings, list price and contract start date we want to estimate the car’s residual value at a future date X with estimated mileage per-year Y.