A $6.8 million program led by Penn State Altoona is studying how to make cutting-edge safety technology affordable for small, remote railroads.
The project kicks off this fall with funding through the Federal Railroad Administration. The new Rail Center for Research Enhancing Short Line Transportation (CREST), supported by a grant from the Consolidated Rail Infrastructure and Safety Improvements (CRISI) Program, will coordinate 10 research projects in collaboration with multiple universities and railroad partners.
Short line railroads play a vital role in the U.S. rail network, supporting local and regional economies through essential “first mile, last mile” services, the process by which goods are moved from manufacturer to consumer. Often operated by small companies, these railroads face significant challenges, including aging infrastructure, limited resources for safety and maintenance and a lack of sustainable practices.
The project aims to make privatelyowned short lines safer and more profitable by developing low-cost sensors, augmented reality and artificial intelligence that can be used to identify pieces of track that may need special attention, helping prioritize repair or maintenance decisions.
Hai Huang, center director and professor of engineering at Penn State Altoona, and Shihui Shen, associate center director and professor of rail transportation engineering (RTE) at Penn State Altoona, are leading the project.
“RTE faculty began rail research efforts more than a decade ago alongside the establishment of the nation’s first and only ABET-accredited RTE program,” Huang said, referring to the Accreditation Board for Engineering and Technology. “Our partner universities also bring extensive experience in rail-focused research and education, making this collaboration uniquely positioned and well-prepared for this important work.”
Kansas State University, The University of Texas, Auburn University, the University of South Carolina and the University of New Mexico will participate.
Finding spots of decay early can make track maintenance less expensive and improve overall safety in the long run, but it takes feedback from railroad managers, an understanding of computer science and expertise in engineering.
UNM’s project will examine how low-cost sensors and neuromorphic cameras can make maintenance more efficient for small railroad companies.
“The Class 1 railroads are already using AI, but they have way more resources than the short lines, so we want to make AI accessible to them too. If you collect millions of data points as a big railroad, you can bring AI into your operations, but short lines don’t have that,” said Fernando Moreu, associate professor in the Gerald May Department of Civil, Construction and Environmental Engineering at the University of New Mexico. “We’re trying to make accessible for the short lines the same technical benefits that exist for larger railroads.”
The Low-cost Efficient Wireless Intelligent Sensors (LEWIS), a $50 accelerometer-based technology, can be placed on trains to monitor vibrations and detect general areas that should be inspected for repair.
Neuromorphic cameras are a more expensive option, with a price tag of around $7,000, but are more advanced and offer greater potential for integration with artificial intelligence.
The cameras attach to railcars and record the track, ballast and spikes as the train travels over them. The team will use the millions of data points collected by the neuromorphic cameras over time to train an AI system to detect how the railroad components decay over time and when an operator should invest in repairs, preventing potentially dangerous situations in the future.
Eventually, augmented reality could help railroad inspectors visualize structural problems that may be challenging to see with the human eye, improving the reliability of structural reports, UNM researchers said.
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