  India runs the fourth largest railway transport network size carrying over 8
billion passengers per year. However, the travel experience of passengers is
frequently marked by delays, i.e., late arrival of trains at stations, causing
inconvenience. In a first, we study the systemic delays in train arrivals using
n-order Markov frameworks and experiment with two regression based models.
Using train running-status data collected for two years, we report on an
efficient algorithm for estimating delays at railway stations with near
accurate results. This work can help railways to manage their resources, while
also helping passengers and businesses served by them to efficiently plan their
activities.
