Fully automated machine learning process
The basis for the machine learning prediction is inola’s ML-Core. This software provides predictions using historical data and real-time information. It is independent from platforms and operating systems and can process large amounts of data (Big Data). In MOBILEstatistics, INIT’s system for analysis and statistics, the operational data (e.g. GPS data) is collected and processed with a lot of additional information, as well as historical driving times. Based on this data, various trainer systems are available to the ML-Core. The best one is automatically recognised and used by the software. After each training session using processed historical data, the ML model is updated and therefore improved – so that a suitable model is available at all times.
Based on the trained model, the ML-Core calculates driving time predictions for all trip sections. The ML prediction then compiles the newly predicted departure times for the stops from these individual values and transmits them to various processes. Passenger Information shows the live predictions, taking into account possible current traffic restrictions or the driving time of the previous vehicle.