Machine Learning for rail transport

An ML model that predicts the time of arrival of trains by reducing Mean Absolute Error (MAE) by approximately 30% compared to traditional models.

hero trasporto treni
  • Big Data 
  • Machine Learning 
  • Predictive Analytics 

Delays, slowdowns, cancellations. Bother of commuters and more or less regular train passengers, and a daily challenge for t those who manage transport systems.

The Mahsfrog Data Lab has developed a Machine Learning (ML) model for the prediction of train timetables at the service of companies operating in the cargo and passenger transport industry on the Italian railway infrastructure.

The implemented solution trains ML models on historical travel data to make them capable of predicting, for a new travel, the time of arrival at the destination based on the train number, the departure and destination stations, and the scheduled start time of the travel.

In the experimentation phase, based on the continuous improvement logic, our Lab developed a model for predicting the time of arrival that reduces the Mean Absolute Error (MAE) by approximately 30% compared to traditional models.

logo python

We are here for you.

If you would like to receive more information about our services or products or if you are interested in getting directly in touch with our teams and competence center, please fill out the form and we will get back to you as soon as possible.

If you would like to work with us instead, please visit our open positions page.

I authorize the processing of my data as described in the privacy policy for the purposes of recontacting.

You may be interested in