Between Dec 19, 2024 and Jan 2, 2025, datasets can be submitted to DRUM but will not be processed until after the break. Staff will not be available to answer email during this period, and will not be able to provide DOIs until after Jan 2. If you are in need of a DOI during this period, consider Dryad or OpenICPSR. Submission responses to the UDC may also be delayed during this time.

Browsing by Author "Barbour, William"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    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.