FREIGHT RAIL TECHNOLOGY KEY FACTS

  • Daily automated inspections exceed 3.5 million, helping accident rates 11% since 2023.
  • Trackside sensors use AI and imaging to detect hidden equipment and track issues.
  • Freight railroads, partnering with MxV Rail and Railinc, have advanced rail technology for decades.

Thanks in part to freight rail’s ongoing use of technology, since 2023, the train accident rate is down 11%. Between 2020 and 2023 alone, U.S. railroads more than doubled the number of daily automated inspections, which use advanced trackside detectors to identify equipment and track defects invisible to the naked eye*. Today, railroads conduct over 3.5 million of these automated inspections daily.

Rail Inspection Detectors 101

Since the 1940s, freight railroads have voluntarily used a variety of inspection detectors to enhance safety and efficiency. These detectors have evolved over time, incorporating technological advancements including a variety of acoustic, infrared and optical sensors alongside tracks (wayside) or embedded directly into tracks to monitor trains as they move at speed across the nearly 140,000-mile network. Here are a few examples:

Wheels

  • Hot Bearing Detectors (aka Hot Box Detectors): Infrared sensors measure the temperature of wheel bearings as trains pass by.
  • Hot Wheel Detectors: Targets specific wheels that may be experiencing overheating due to sticking brakes.
  • Wheel Impact Load Detectors (WILD): Track-embedded sensors measure defects of wheels through their impact on the rail.
  • Wheel Profile Detectors: Lasers and optical sensors capture detailed images of a wheel’s surface, measuring their wear and profile.
  • Wheel Temperature Detectors: Infrared sensors provide a more comprehensive view of temperature conditions across all wheels to find braking issues or other mechanical problems that could arise in the future.
  • Acoustic Bearing Detectors: Microphones and sound analysis algorithms are used to detect bearing defects.

Railcars

  • Dragging Equipment Detectors: Mechanical arms, laser beams or infrared sensors find objects hanging or dragging beneath a train.
  • Truck Performance Detectors: A combination of sensors measure the performance and stability of railcar trucks to identify issues like misalignment or worn components. Trucks are the framework under a railcar that wheels connect to through axles and bearings.
  • Brake Shoe Detectors: Optical or laser sensors inspect brake shoe conditions.
  • Load Measurement Systems: Strain gauges or load cells monitor the distribution and weight of cargo in railcars to prevent overloading and ensure even load distribution. Strain gauges are devices that measure strain on an object and load cells are sensors that convert a force acting on it into an electrical signal.
  • Laser Scanning Systems: High-precision laser scanners create 3D models of railcar parts, such as the undercarriage, for maintenance and safety checks.

Track

  • Clearance Detectors: Laser or optical sensors measure the space around passing trains to ensure they have sufficient clearance from trackside structures.
  • Broken Rail Detectors: Various technologies, including ultrasonic sensors, identify fractures or breaks in the rails.
  • Rail Profile Detectors: Laser scanning and other measurement tools measure the wear and profile of the rails themselves to ensure they are within safe operating limits.

Early 1990s: Rail’s digital transformation gains momentum.

The early 1990s marked an accelerated shift in technology adoption as freight railroads increasingly used computerized management systems. These systems revolutionized operations and logistics, streamlining processes that were once manual and time-consuming. Improved radio communication and Computer Aided Dispatcher systems significantly enhanced operational efficiency and safety.

Cab signaling systems began using electronic signal transmission from track circuits to the train’s onboard equipment, giving train operators more timely and precise information about upcoming signals and track conditions. And Radio Frequency Identification (RFID) technology, which uses electromagnetic fields to automatically identify and monitor tags attached to objects, was widely adopted for better tracking of freight and rail assets, offering real-time visibility and improved inventory management.

Mid-1990s: The era of advanced detection systems.

Ultrasound detectors emerged to identify flaws deep inside rails, enhancing the safety and reliability of rail infrastructure. Wayside detectors became more sophisticated as the first generation of data analytics and machine learning grew, enabling railroads to prioritize preventative maintenance.

☝ Example of a modern wayside detector. This one is a hot bearing detector, which uses infrared sensors to monitor the temperature of railcar bearings in real-time, identifying potential overheating issues to prevent equipment failure and derailments.

Late 1990s: Automation and real-time monitoring increasingly come online.

The late 1990s saw freight railroads adding more wayside detectors that automatically monitored railcars and their wheels as they passed by at speed. These included truck geometry detectors, truck performance detectors, wheel profile detectors and wheel impact load detectors. This period also marked an improvement in freight tracking through Electronic Data Interchange (EDI) systems and remote monitoring systems that tracked locomotives and railcars in real-time.

In July 1999, the Transportation Technology Center, Inc. (TTCI, now MxV Rail), a subsidiary of AAR, conducted a system evaluation test to evaluate improved wayside acoustic bearing detection systems, with TTCI providing the prototype and other suppliers collecting data. While not commercially available during this time, in the years to come, acoustic bearing detectors will become capable of detecting bearings that may be reaching the end of their service lives by monitoring and assessing noise and vibrations.

Early 2000s: Virtual becomes more of a reality.

In the early 2000s, freight railroads began testing machine vision systems that could inspect trains at speed, significantly reducing inspection times and improving accuracy by providing a consistent, ongoing inspection. Compared to today’s modern, digital systems, these versions relied on analog cameras, basic optical sensors and limited processing power. Ground penetrating radar started assessing sub-surface conditions of the track bed, helping to identify issues like ballast degradation and subgrade problems, while hot bearing detectors gained wider use.

Virtual reality simulators were introduced to enhance training programs for train operators, providing a safe and immersive learning environment. Automated Single Car Test Devices demonstrated the ability for technology to pinpoint anomalies far better than manual test devices. Improvements to the Automated Single Car Test Devices have been ongoing, and the Association of American Railroads (AAR) has a schedule for requiring the latest in automated technology.

The beginning of the 2000s also saw enhanced locomotive technologies, including improved radio systems between distributed power locomotives and the End of Train Device to improve train brake communication throughout the train. A distributed power locomotive, also known as a Distributed Power Unit (DPU), is a locomotive that can be placed in the middle or at the end of a train to help it haul more and climb steep hills, improving train handling. An End of Train Devices, or EOT, is mounted on the rear of a freight train and provides essential information to the locomotive crew and trackside systems, including brake monitoring.

☝ Example of today’s machine visioning portal, which uses advanced imaging and AI technology to take hundreds of thousands of high-res images per second of trains passing through at speed.

Mid-2000s: Widespread implementation of PTC changes the course of rail safety.

In the mid-2000s, Positive Train Control (PTC) emerged as a pivotal safety technology in the rail industry. Designed to automatically stop a train to prevent accidents, PTC was developed to mitigate human errors such as speeding and running red signals. The technology is highly advanced, relying on real-time data communication between trains, trackside equipment and centralized control systems; GIS integration for tracking train locations; and data analytics to process huge amounts of information from multiple sources.

In 2006, Railinc’s Equipment Health Monitoring System (EHMS) began gathering comprehensive equipment data, helping to inform preventative maintenance. In the years to come, hot bearing detector information will become part of EHMS. It was also during this time that the integration of satellite communication systems enabled real-time tracking and monitoring of trains, significantly enhancing the management and responsiveness to operational challenges. Distributed power use also increased, improving train handling.

2010-2015: Big data gets much bigger.

This period saw a surge in advanced data analytics and increased automation. Drones take flight, revolutionizing track inspection and maintenance by accessing hard-to-reach areas such as bridges. This not only enhances infrastructure safety but also protects employees. Light Detection and Ranging (LIDAR) technology began creating precise 3D models of the track environment, helping to find track geometry issues and other structural problems.

2015-Today: Digital transformation in a rapidly changing world.

Since 2015, the freight rail industry has undergone a technological revolution driven by rapid advancements in data analytics, Internet of Things (IoT) integration and predictive maintenance systems. As these technologies have allowed for more efficient data processing, storage and sharing, railroads have fine-tuned their existing technologies while developing and implementing more advanced solutions, including through AI. It was also during this time that PTC became interoperable, meaning different railroad systems could seamlessly communicate and operate together using the technology.

Tomorrow: MxV Rail and Railinc lead the charge in innovation.

MxV Rail, an AAR subsidiary in Pueblo, Colorado, is at the forefront of developing and testing many of these safety technologies. Recently moving its multi-campus operations, the team completed a new loop track for the renowned Facility for Accelerated Service Testing (FAST®). This state-of-the-art facility is at the forefront of rail research, gathering essential data for over 30 annual experiments. Supported by the Strategic Research Initiative (SRI), MxV Rail drives innovation across infrastructure, mechanical and operations systems in the rail industry from inception to deployment.

Railinc, also a subsidiary of AAR, collects extensive data to help North America’s freight railroads make their operations even safer. Launched by the Class Is and railcar owners, the Asset Health Strategic Initiative (AHSI) is a multi-year, multi-phase effort that the AAR Safety and Operations Management Committee oversees with Railinc. The project uses a wide range of technologies to provide the health of rolling stock to all stakeholders, particularly to the railroads on which the cars and locomotives are operating. These insights help inform railroads’ proactive maintenance plans.

☝ The 2.8-mile FAST loop can be used to conduct tests with trains traveling up to 40 mph.

* Railroads do not report 100% of their automated railcar inspection events to Railinc; therefore, these numbers may be an underestimate of the overall data collection process. Along with the increased number of automated inspections, the computer algorithms used to detect equipment failures have also improved since 2020, improving the efficiency to predict failures. About 800,000 railcars (half of the fleet) move on any given day i.e. not in yards, loading/unloading, storage or being repaired.