Estimating Train Delays in a Large Rail Network Using a Zero Shot Markov Model
This work helps railways manage resources and assists passengers and businesses in planning activities, but it is incremental as it applies existing Markov and regression methods to a new domain.
The paper tackled the problem of systemic train delays in India's large rail network by developing an efficient algorithm using n-order Markov frameworks and regression models, achieving near-accurate delay estimates based on two years of running-status data.
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.