Friday , 25 May 2018
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Machine Learning significantly improves train delay predictions in India

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New Delhi: 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. The result; unending anxiety of passengers, many many man hours wasted and unnecessary congestion at all the stations.
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.
Train delays can safely be considered part and parcel of train travel in India giving the current delay trends, but what bothers the travellers most is the uncertainty around their train travel. Surveys show that while train travellers have submitted to delays being part of their travel, their frustration arises from the inability of the existing systems to correctly guide them on the estimated time of arrival (ETA) of their trains. This leaves them waiting endlessly at platforms without any idea of the exact time of arrival of their trains.
According to Kapil Raizada, Cofounder of RailYatri – “The existing method to predict the ETA of trains in India have not changed over decades and is typically based on the ‘distance divided by speed of the train added with some buffer time for safety formula. We believe that a much better technique is to make the ETA prediction based on historical data as it takes proper considerations of ground realities such as increasing traffic, rush, seasonality, etc. Our ETA prediction algorithms is highly adaptive and modify themselves as it learns from subsequent inputs. Hence, the predictions get better with time.
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.
Passengers typically check the arrival time of trains about 2-4 hrs before the start of journey and the new algorithm shows nearly 25 mins savings from unwanted wait time. This can also be a boon for Railways as they have to manage less crowd at stations with people knowing exactly when their train would be reaching the station.
“Considering the running complexity associated with trains in India – it takes multiple learning iterations to train (learn) the data sets to make a meaningful prediction. We are glad that we undertook this effort few years back. The train ETA predictions done by our algorithms are now the best in class and offer the highest degree of accuracy and reliability in an uncertain operating environment. We believe that better prediction will not only saves millions of hours which are wasted by passengers waiting for their train, but can also significantly impact traffic and rush at railway stations.” adds Mr Raizada.

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error: Haha. Not so smart.. buddy!