首页|University of Alberta Researcher Adds New Data to Research in Machine Learning (Machine Learning Approach to Enhance Highway Railroad Grade Crossing Safety by Analyzing Crash Data and Identifying Hotspot Crash Locations)
University of Alberta Researcher Adds New Data to Research in Machine Learning (Machine Learning Approach to Enhance Highway Railroad Grade Crossing Safety by Analyzing Crash Data and Identifying Hotspot Crash Locations)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – A new study on artificial intelligence is now available. According to news reportingout of Edmonton, Canada, by NewsRx editors, research stated, “Safe railway operation is vital for publicsafety, the environment, and property.”Our news correspondents obtained a quote from the research from University of Alberta: “Concurrentwith climbing amounts of rail traffic on the Canadian rail network are increases in the last decade in theannual crash counts for derailment, collision, and highway railroad grade crossings (HRGCs). HRGCs areimportant spatial areas of the rail network, and the development of community areas near railway tracksincreases the risk of HRGC crashes between highway vehicles and moving trains, resulting in consequences varying from property damage to injuries and fatalities. This research aims to identify major factorsthat cause HRGC crashes and affect the severity of associated casualties. Using these causal factors andensemble algorithms, machine learning models were developed to analyze HRGC crashes and the severityof associated casualties between 2001 and 2022 in Canada. Furthermore, spatial autocorrelation andoptimized hotspot analysis tools from ArcGIS software were used to identify hotspot locations of HRGCcrashes. The optimized hotspot analysis shows the clustering of HRGC crashes around major Canadiancities.”
University of AlbertaEdmontonCanadaNorth and Central AmericaCyborgsEmerging TechnologiesMachine Learning
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