Estimating Train Delays in a Large Rail Network Using a Zero Shot Markov Model

TL;DR

This paper introduces a zero shot Markov model to accurately estimate train delays in India's extensive railway network, aiding better resource management and passenger planning.

Contribution

It presents a novel application of n-order Markov models and regression techniques for systemic delay estimation in large-scale rail networks.

Findings

High accuracy in delay estimation using the proposed model

Efficient algorithm demonstrated on two years of data

Potential to improve resource allocation and passenger experience

Abstract

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.