Deutsche Bahn is to begin using a new data analytics service provided by T-Systems to improve their existing real-time system for projecting arrival and departure times on their rail passenger services. The timetable data for more than two million stops per day for essentially the whole of Deutsche Bahn’s scheduled passenger services will be compared minute-by-minute against the current real-world transport situation. This comparison is then used as the basis for forecasting probable arrival times and, at the same time, for predicting the impact of the real-time information on subsequent passenger transfer connections.
From T-Systems’ data centers, the system analyzes geo-positioning reports received for all trains currently running in a matter of seconds, and produces a projection of expected arrival times up to the trains’ final destinations. The algorithm used in these calculations is based on machine learning (artificial intelligence). To achieve its results, the algorithm chooses from a variety of projection models depending on the current traffic situation. At 24-hour intervals, the model trains itself up on the basis of historical data during the night. Deutsche Bahn customers will be provided with real-time information on expected departure times up to 90 minutes in advance. This service is set to help passengers plan and use their time more efficiently.
The solution is expected to be launched in the second quarter of 2017 to improve passenger information on delays in both long- and short-distance rail traffic.
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