comscore This startup is using machine learning to predict train arrival time
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This startup is using machine learning to predict train arrival time

The machine learning-based algorithm analyses historical data of train runs spread over many years, and predict the present ETA.

  • Updated: April 13, 2018 9:45 AM IST
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Annoyed of train delays and inaccurate predicted time of arrival? RailYatri, a travel startup, has suggested a technique to predict Estimated Time of Arrival (ETA) using machine learning with precision based on the data. Also Read - Assam floods: Google starts SOS alert and fundraising to help impacted communities

According to RailYatri.in, train travellers in India have accepted delays as part of their train journeys. “But their frustration arises from the inability of the existing systems to correctly guide them on the ETA of trains,” the start-up said in a release. Also Read - Kawasaki Versys 650 launched in India at Rs 7.36 lakh: Check details

It also said that the delays and inaccurate prediction leave many commuters stranded at platforms endlessly. Also Read - World's largest technology startup incubator all set to open in India

“RailYatri has innovated a unique 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,” it said.

It said that the algorithm has been trained to analyse historical data of train runs spread over many years and predict the future outcome.

The startup claimed that “its prediction is nearly 110 per cent better than the existing way of estimating train travel time.”

RailYatri Co-founder Kapil Raizada said, “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 the 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 algorithm is highly adaptive and modify themselves as it learns from subsequent inputs. Hence, the predictions get better with time,” Raizada added.

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  • Published Date: April 13, 2018 9:35 AM IST
  • Updated Date: April 13, 2018 9:45 AM IST



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