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Intelligence series: Predictive Analytics for Optimised Rail Operations

Predictive analytics is a form of advanced analytics that uses big data and statistics, modelling and machine learning techniques to identify the likelihood of future outcomes based on historical data. There are two types of predictive models that are used – classification and regression. Classification models predict class membership and present a binary result e.g. 1 for spam emails or 0 for normal emails. Regression models instead present a quantitative value e.g. annual revenue.

 

The rail industry is already using predictive analytics for some of its operations. These techniques facilitate predictive maintenance and asset management. For example, applying predictive analytics with machine learning to collate large volumes of multi-variable data establishes a comprehensive profile for each asset. This delivers detailed knowledge of the asset’s performance at varying operating rates. The use of real-time dashboards, automated diagnostics and root-cause analysis provides real-time alerts of potential issues before breakdown, thus potentially reducing downtime and accident risks, and optimising maintenance and asset life management. Moreover, predictive analytics can be implemented for safety monitoring and risk analysis to obtain the real-time status of the overall system, as well as providing tactical and planning recommendations. This can improve safety and reduce risk throughout the rail network. Train delay times can be predicted throughout the network using predictive analytics in conjunction with machine learning, therefore improving route-planning and train operations.

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