Occupancy prediction in smart parking lots
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.
Demand forecasting up to 1 day in advance
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.
Challenge
The client required a custom machine learning model that could predict the occupancy rate of parking lots adjacent to train stations. Having that forecast could encourage users to take the train more often, without the fear of driving to the train station to find out that no parking spots were available.
Solution
The client envisioned that the predictive model for parking occupancy could be used in two cases. First, to provide passengers about to drive to the train station an estimate of how full the parking lot will be when they arrive. They also wanted to offer their customers the possibility to plan their trip for the next morning the night before. For the first use case, a short-term model capable of forecasting up to 4 hours ahead was built, while a long-term model was built for the second use case, as day-ahead predictions were required.
Discover our approach
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.
Data collection
The data for this project was obtained from a third-party company that installed sensors in two parking lots in Luxembourg. From these sensors, our client calculated the number of occupied spots in each parking area.
At the moment of starting the project, the client had identified that some sensors were producing noisy measurements. This was mitigated by re-sampling the data to a 5-minute granularity, avoiding some errors.
