Occupancy prediction in smart parking lots
The Luxembourg National Railway Company or SNCFL is one of the leading economic players in the Grand Duchy. With 22 companies and more than 4.800 employees, the CFL Group ranks among Luxembourg's largest employers. Willing to provide a user-friendly transportation service, CFL has been working with us to predict the occupancy rate of two of their parking lots adjacent to train stations to make it easier for commuters to park at their facilities.
CFL installed smart sensors in two of their parking lots in 2020, and they have been collecting and centralizing the data ever since in Pentaho: a business intelligence software hosted in-house.Since CFL already invested in the platform and the necessary infrastructure to host it, the developed solution had to be able to integrate into their existing workflow, allowing their employees to continue using the same tools that they were previously using to explore and process the new data.
Type of company: State-owned
error in predictions
for first prototype running in production
The developed models were integrated within the client's in-house business analytics platform,
allowing them to seamlessly analyze the outcome of the predictive models.
Our solution consisted of two machine learning models for time-series prediction deployed on the client infrastructure. These models are periodically updating the predictions to provide real-time information to passengers.
Feature extraction and model selection