A new travel startup has decoded the problem of predicting train arrival time. RailYatri, a travel startup, has introduced a technique to calculate the ‘Estimated Time of Arrival’ (ETA) using techniques like machine learning based on previously collected data.
The innovative ETA prediction algorithm 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.
RailYatri 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.
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.