Rail Temperature Prediction Model : Research Results

011568872008PDFTech ReportRR08-06BucklingHot weatherMathematical predictionRail (Railroads)Railroad safetyRailroad tracksTemperatureTemperature sensorsResearch HubOperations and Traffic ManagementRailroadsSafety and Human FactorsRailroad tracksTemperatureTemperature sensorsTrail temperaturetrack bucklingweather dataprediction modelslower ordersUnited StatesUnited States. Federal Railroad AdministrationAl-Nazer, LeithUnited States. Federal Railroad AdministrationUS Transportation CollectionPreventing track buckling is important to the railroad industry's goal of operational safety. It is a common practice for railroads to impose slow orders during hot weather when the risk of track buckling is high. Numerous factors affect track buckling, but the instantaneous rail temperatures and stress-free (neutral) rail temperatures are the most critical factors. Unfortunately, neither of these two temperatures is easily obtainable. Decisions for slow orders are often based on an arbitrary, ambient temperature limit. The Federal Railroad Administration (FRA) Office of Research and Development has initiated a research project to develop a model for predicting rail temperatures based on real-time meteorological forecast data. The rail temperature prediction model is based on the heat transfer process of a rail exposed to the sun. In developing such a model, a rail-weather station was established, composed of a portable weather station and a short segment of rail track with temperature sensors installed on both rails. The model has proven to be able to predict the maximum rail temperature within a few degrees and within 30 minutes of the actual time when the maximum rail temperature occurs during the day. The model is being validated for three locations where real-time weather data and rail temperature are collected. A prototype web-based software application has been developed, as is shown in Figure 1. The application is also being tested by Amtrak