FRA Issues Tech Reports, Research Results

The Federal Railroad Administration (FRA) recently released three technical reports and a research results report covering communications and train control, rolling stock, and automation, which the agency’s Office of Research, Development & Technology (RD&T) is studying with its partners to help improve rail safety.

Interoperable Employee-In-Charge Portable Remote Terminal (EIC PRT) Summary Report

The FRA sponsored the Transportation Technology Center, Inc. (TTCI) to define the requirements for Interoperable EIC PRT functionality for use with Positive Train Control (PTC) systems. According to FRA, the Interoperable EIC-PRT is a safety overlay system that integrates with PTC to protect maintenance-of-way workers by placing the entry of trains into work zones under the control of the EIC. This research was conducted between 2016 and 2021.

Collaborating with the North American railroad industry, the team developed systems engineering documents defining the EIC PRT system, as well a prototype Roadway Worker Terminal application. The team recommended several steps to facilitate the integration of the EIC PRT system into current railroad operations with PTC.

Development of Railroad Trespassing Database Using Artificial Intelligence

FRA reported that trespassing fatalities at highway/rail grade crossings and on rights-of-way have accounted for around 95% of all fatalities in the railroad industry over the past 10 years (U.S. Bureau of Transportation Statistics, 2019). The agency sponsored a research team from Rutgers University to develop a proof-of-concept Trespassing Database using Artificial Intelligence (AI) technology to automatically process large volumes of live or recorded video data. The team used the Rutgers AI algorithm to analyze more than 27,000 hours of live video data and 1,176 hours of recorded video data from rights-of-way and grade crossings at 11 locations in six states. The AI algorithm collected trespassing-related data, including traffic, rail signal activations, train events, and trespass events. Trespass event data were automatically collected for each trespasser, including date, time, type (e.g., person, car, truck, bus, motorcycle), weather, trespasser’s path, and a video clip. The team manually validated all trespass event detection results to ensure that accurate data was included in the database. More than 29,000 trespass events were detected by the AI algorithm across all studied locations in this research. This report also presents two year-long, in-depth case studies of one grade crossing in New Jersey (21,202 trespass events) and one right-of-way location in North Carolina (476 trespass events). This report provides temporal and spatial analyses of trespass events and discusses AI-informed mitigation strategies.

The results of this research and the new trespassing dataset can help FRA, railroads and communities “to better understand trespass event characteristics and potential influencing factors, thereby assisting in the development and evaluation of effective countermeasures,” according to FRA. “The AI-based technology developed in this project can provide important data to justify investments in informed engineering, enforcement, and education solutions for trespassing prevention, ultimately helping improve public safety.”

Impact of Advanced Train Control Technologies on Rail Network Safety and Operational Performance

Over the past two decades, major technology initiatives in the railroad industry have improved the safety and operational performance of freight trains and other trains, according to FRA. Advanced train control technologies such as Advanced Braking Systems (ABS) for freight trains and PTC systems not only improve safety, but also may improve the performance of operating trains in the network, the agency reported. This study evaluated the impact of applying advanced train control technologies (i.e., ABS and PTC systems) on the safety and operational performance of the U.S. rail network using a benchmark mini-network simulation approach. The research team developed a hypothetical mini-network and simulated various traffic mixes on corridors similar to the North American rail network operations. The team modeled approximately 5,000 miles of main tracks in the simulator. However, considering parts of the network with double- and multi-track segments, as well as additional tracks along 150 sidings and yards in the mini-network, approximately 6,200 miles of tracks were built for simulation purposes, according to FRA. A total of 256 daily trains, a mix of intercity passenger, commuter, high-speed rail (HSR), and freight services, were scheduled in the mini-network, including 102 daily freight trains in 17 different configurations. Seven scenarios were simulated in the study to analyze the safety and operational performance impact of deploying advanced train control technologies over the network.

“Based on the results of the study, researchers concluded that ABS would mostly improve the performance of freight trains, particularly in terms of delay, braking at signals, and signal stops, while it may not have any major impact on other train performance metrics,” FRA reported. “The team also found that PTC may have a more beneficial impact on almost every type of train and for every performance metric except for the number of station stops, a metric associated more with the prescribed stops for trains. For example, ABS could increase network velocity throughout the mini-network by 0.3 mph (0.5%), while PTC could increase network velocity by 1 mph (1.7%), and notably higher for HSR trains (3.4 mph). PTC systems could also significantly reduce the amount of braking at signals (about 88% on average) particularly for freight (over 95%) and passenger trains (about 82%). In addition, PTC could significantly reduce the number of stops at signals for HSR (about 73%) and passenger trains (about 54%). No significant benefit was observed in capacity and track utilization rates when equipping freight trains with ABS systems. However, PTC could improve the maximum track utilization rate of most of the network corridors by approximately 30% on average, especially through double- and multi-track corridors, with an average reduction of 7% in capacity utilization throughout the mini-network.”

Scaling the results from the mini-network to the size of the national rail network showed similar improvements using advanced train control technologies compared to conventional braking and signaling systems.

Based on analysis of the study results, the research team suggested four areas for further research.

An Automation Awareness Assistant for Automated Train Operations

FRA from Aug. 8, 2022, to Jan. 31, 2024, sponsored Monterey Technologies, Inc., to design and develop an improved interface between human operators and advanced automation in rail systems. Applying user-centered design (UCD) methodology, the team prototyped and demonstrated a locomotive cab user-interface (UI) concept called the Automation Awareness Assistant (A3). A3 is designed to improve human operator situational awareness of automated rail functionality and provide decision support if a possible automation failure is detected, according to FRA.

The team developed preliminary software to realize the UI prototype in the locomotive cab and successfully tested it at FRA’s Cab Technology Integration Laboratory located at the U.S. Department of Transportation’s Volpe National Transportation Systems Center in Cambridge, Mass. The prototype is available for evaluation and further refinement. According to the FRA, this demonstration was in the context of a freight locomotive cab to help visualize and evaluate the A3 prototype, but its architecture and demonstrated principles apply at all levels of railroad infrastructure.