A pair of Rutgers engineers have developed a tool aided by artificial intelligence to detect trespassing on railroad crossings and curb fatalities that have been increasing over the past decade.
Asim Zaman, a Rutgers project engineer, and Xiang Liu, an associate professor in transportation engineering at the Rutgers School of Engineering, created an AI-aided framework that automatically detects railroad trespassing events, differentiates types of violators and generates video clips of infractions. The system uses an object detection algorithm to process video data into a single dataset.
“With this information we can answer numerous questions, like what time of day do people trespass the most, and do people go around the gates when they are coming down or going up?” said Zaman.
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Every year, hundreds of people in the U.S. are killed in trespassing accidents at the country’s 210,000 rail crossings, according to the Federal Railroad Administration. Despite concerted efforts to reduce fatalities, deaths by train strike continue to rise. In 2008, the FRA estimated about 500 people were killed annually trespassing on railroad rights-of-way. Ten years later, the number inclusive of suicides had climbed to 855, the FRA reported.
In their research, Zaman and Liu define trespassers as unauthorized people or vehicles in an area of railroad or transit property not intended for public use, or those who enter a signalized grade crossing after it has been activated.
Until now, most research into railroad trespassing was derived from casualty information. But the research overlooked near-misses—occasions Zaman and Liu said can provide valuable insights into trespassing behaviors, which in turn can help with the design of more effective control measures.