While the Indian Railways is introducing newer technologies and systems to reduce inefficiency, here's a travel startup that's decoding this problem. RailYatri has introduced a technique to calculate the 'Estimated Time of Arrival' using techniques like Machine Learning based on previously collected data.
In a press release, the startup said that most of travellers are miffed because of the present system's inability to predict the right arrival and departure time. The innovative ETA prediction algorithm of RailYatri fills in that gap with statistical modelling techniques where precision is duly noted.
The startup which is known for using big data, analyses train timings by using its previous data that is collected from many years and sends across a prediction. It also claims that "its prediction is nearly 110 per cent better than the existing way of estimating train travel time."
Co-founder, Kapil Raizada added that the present system has remained the same since many years and is based on an old-age system of dividing distance with the speed of the train plus some buffer time.
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.
The founders also claim that the system is easy to adapt and is open sourced. In 2016, the startup had raised a fresh round of funding, with participation from all its existing investors - Nandan Nilekani, Helion Ventures, Omidyar Partners and Blume Ventures.
(With inputs from IANS)
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