Machine learning to improve train delay predictions

Figuring out and letting the passengers know when a train is going to arrive at a station is like predicting the future. Multiple variables associated with the run of a train can affect the arrival time of a train at the station and passengers are most often left waiting for hours before their train finally arrives.

RailYatri, a travel start-up, has innovated a unique Estimated Arrival Time (ETA) prediction algorithm using Machine Learning and Statistical Modelling techniques to predict the arrival time of running trains at their upcoming stoppage with much better precision. The algorithm has been trained to analyse historical data of train runs spread over many years and predict the future outcome.

RailYatri’s Smart ETA Prediction makes use of Clustering Algorithms which organizes historical train runs into thousands of patterns where time series data attributes are similar. Based on the symptoms exhibited by a running train, the ETA prediction algorithm matches through millions of permutations of patterns to make an optimized prediction in real time. As it does the forecast, the Machine Learning algorithm also determines any new running pattern which the train exhibits.

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