TTC OPERATED BY ENSCO
Advanced Track Geometry Condition Forecasting Using ML/AI Three different forecast models are evaluated for degree of accuracy. Radim Bruzek, R&D Program Manager, ENSCO, Inc., Springfield, VA Serkan Sandikcioglu, Product Manager, ENSCO, Inc., Charlottesville, VA Jay Baillargeon, Program Manager, Federal Railroad Administration Office of Research, Development and Technology, Pueblo, CO
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here’s an old adage that says, “You can’t manage what you can’t measure.” Although, it is also said that “it’s difficult to plan without a good prediction.” While the procedures for assessing the condition of track infrastructure are well established, having a good forecast can enable more effective remedial action and optimal maintenance plans. Such data-based forecasting procedures will lead to the safest and most efficient operations. Track geometry is one of the most common measurements used to assess track condition. With the advent of
Autonomous Track Geometry Measurement Systems (ATGMS), the amount of data collected has risen significantly over the past several years. ATGMS was first introduced in the United States through a research project led by the Federal Railroad Administration’s (FRA) Office of Research, Development, and Technology in 2008. Over thirty systems are now operating in revenue service in North America with some exceeding 100,000 miles surveyed per year.[1] This abundance of data allows for integration of machine learning (ML) and other artificial intelligence (AI) techniques to reap greater value from the data, including accurately forecasting future conditions. As part of FRA’s ongoing research into ML/AI applications for advancing rail safety, ENSCO has completed a multiyear effort to advance such forecasting methods for predicting continuous or “foot-by-foot” track geometry measurements. Continuous data is the data collected by track geometry vehicles that show the uninterrupted measurement of track geometry along a section of track which is commonly depicted in strip charts. In North America, this data is sampled at every foot. There are several advantages to forecasting using continuous data rather than relying on values that exceed a predetermined safety or maintenance threshold, otherwise known as exceptions. One example is a warp exception, which is the maximum crosslevel difference within a 62-foot
Figure 1. Predicted versus actual track geometry data using the Autoregressive Integrated Moving Average (ARIMA) model.
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span. As track deteriorates, crosslevel condition can vary in complex ways that make it difficult to summarize the forecast in the context of a single value. As such, predicting the continuous condition is ideal for capturing the full behavior of deteriorating track. Additionally, continuous track geometry is valuable when used in Track Quality Indices (TQI). TQIs are used with segmented lengths of continuous data and provide a track condition quality summary based on the data within a specific segment. Often the segments range from 200 feet to more than a mile. TQI calculations often use standard deviation as a calculation method, which is useful to quantify how “rough” or “smooth” a track is. This is valuable information when developing maintenance plans. Forecasted continuous track geometry assessment is valuable in several areas, including identifying when a condition could become a major safety concern. An example would be forecasting when a near-urgent track geometry exception could become an urgent exception. Similarly, forecasted continuous track geometry can be used to predict TQIs into the future. Lastly, a useful byproduct of ML/AI track geometry prediction is its ability to accurately fill in gaps of missing data that can naturally occur in certain situations, such as blowing snow interfering with the ATGMS laser measurement. ENSCO initially conducted a review of time-series forecasting methods used extensively in other industries that were adaptable to forecasting continuous track geometry data. Based on this review, the rtands.com