Browsing by Subject "Railroad grade crossings"

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    Accident Prediction Models using Macro and Micro Scale Analysis: Dynamic Tree and Zero Inflated Negative Binomial Models with Empirical Bayes Accident History Adjustment
    (Center for Transportation Studies, 2019-02) Mathew, Jacob; Benekohal, Rahim F; Medina, Juan C
    This report presents two ways to analyze accidents at highway rail grade crossings: a microscopic approach that looks at individual accidents at a crossing or a group of crossings, and a macroscopic approach to identify correlations between accident counts at crossings and crossing characteristics. The outcome of the microscopic approach is a data-driven dynamic tree that helps to visualize accident trends at a single crossing or a group of crossings. The dynamic tree is also used to identify new variables (crossing angle and distance to nearby highway intersection). The outcome of the macroscopic approach were new accident prediction models for crossings with gates, flashing lights, and crossbucks. Zero Inflated Negative Binomial models were used to predict the accident counts and the Empirical Bayes approach was used to adjust the predicted based on accident history at the crossing. Data from the state of Illinois was used to develop the model and data from four other states were used to validate the model. The newly developed models resulted in cumulative predicted accident distributions that closely represent the field data. The EB adjusted ZINB accident predictions value were significantly closer to the actual accident counts for the crossings than the USDOT models. More accurate predictions from the EB-adjusted ZINB model were obtained for the top 10, 20, 30, 40 and 50 locations with highest accident frequency for all three warning devices.
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    Improving Railroad Grade Crossing Safety: Accurate Prediction of Train Arrival Times for Emergency Response Management and Driver Decision Support
    (Center for Transportation Studies, 2019-02) Work, Daniel B; Barbour, William; Wang, Ren
    Incidents at highway-rail grade crossings, locations where railroad tracks intersect surface streets at grade, are a primary driver of safety in rail transportation in the United States. Addressing safety at these locations through technology is a focus of the Federal Railroad Administration and United States Department of Transportation. Effective management of emergency response resources on the road network requires knowledge of when trains will arrive at grade crossings and temporarily disconnect emergency vehicles from parts of the community they serve. Generating estimated times of arrival (ETAs) for trains at grade crossings on a long time horizon can be used to proactively address surface transportation safety and emergency response management. This project investigates train delays to accurately estimate train arrival times at grade crossings to support in-vehicle driver alerts. The prediction of arrival times uses train-positioning information, properties of the train, properties of the network, and properties of potentially conflicting traffic on the network as input. A historical algorithm is developed to accurately model delays using train- positioning information and an online algorithm integrates real-time train position information into the forecasts. Amtrak data and over two years of CSX freight rail data are used to test and validate the proposed algorithms. Results on ETA prediction are presented for various sets of input features, machine learning algorithms, and prediction locations. ETAs at control points located close to grade crossings are dramatically improved over baseline algorithms, particularly for predictions made multiple hours from a crossing that are useful for proactive safety measures.