ARTICLE Improving rail crossing safety with artificial intelligenceNews
Artificial intelligence (AI) and machine learning are increasingly being applied to some of the thorniest issues that rail operators confront, and one of the more promising ones is the monitoring of level crossings.
There are good reasons for the excitement around the application of video analytics, AI and machine learning to improve safety at level crossings. From 2009-2018, the U.S. Federal Rail Administration (FRA) reported an average of more than 900 injuries and 250 deaths at railway crossings annually. In the EU in 2018, Eurostat reported that there were 447 accidents at level crossings, which were also the cause of more than one in four of all railway fatalities.
For several years now, rail operators have used CCTV cameras to monitor level crossings and other parts of their infrastructure in order to reduce incidents and improve safety for the public and employees. The challenge with video, however, is that it often requires someone to continuously watch the feed in order to detect safety anomalies. Unsurprisingly, however, human operators can develop ‘video blindness’, and after relatively brief periods of time monitoring multiple video feeds, they can fail to see incidents that the cameras are recording.
A study by the Transportation Research Record in 2018 looked at the use of AI in video analytics for railways and found that it could significantly reduce the laborious effort needed to process video data from CCTVs and would reduce the effect of ‘video blindness’ on operators, thus improving their well-being and quality of work. Most importantly, it could correctly detect near-miss events associated with unsafe trespassing at railway crossings, which could lead in turn to the development of safer level crossing technologies.