AI could reduce fatalities at railway crossings

Asim Zaman, a Rutgers University project engineer, and Xiang Liu, an associate professor in transportation engineering at the Rutgers School of Engineering in New Jersey, USA 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?” Zaman said in a statement.

In their research, Zaman and Liu define trespassers as unauthorised people or vehicles in an area of railroad or transit property not intended for public use, or those who enter a signalised 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 behaviours, which in turn can help with the design of more effective control measures.