Intelligence series: Machine Learning and Rail

Machine learning is a subfield of Artificial Intelligence (AI) that provides systems with the ability to automatically learn and improve from experience. Machine learning algorithms are categorised as either supervised or unsupervised. Supervised learning algorithms are trained on datasets where the desired output is known. This allows the machine to iteratively adapt the way it transforms input information to determine the method to achieve the correct output. Unsupervised learning algorithms, on the other hand, are used against data that do not have historical labels, which means that the system finds patterns within the dataset.


Machine learning techniques can assist in addressing operational and maintenance issues. Train delay times can be predicted throughout the network using machine learning, thus improving route-planning and train operations. Machine learning techniques can be used to analyse historical sensor measurements to find patterns and predict potential failures, consequently reducing maintenance costs through preventative measures. In addition, image recognition systems make use of machine learning and can be applied to autonomous systems and facial recognition, reducing operational costs and protecting revenue through detecting fare evaders. Using machine learning for recommendation systems could increase revenue and enhance customer experience by streamlining the purchasing process. Tailoring web content based on their history could allow customers to quickly find a desired route.

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