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The capabilities of condition-based maintenance and predictive maintenance regimes for railways are thought to be significantly enhanced by AI-powered video analytics. For instance, one can utilize a multi-layer neural network (deep learning) and a statistics optimization model to automatically detect cracks in tunnel linings.
The efficiency and safety of the maintenance workers are also major goals of this project. Combating fare evasion issues by using the automatic real-time analytics system to detect suspicious fraudulent behavior at ticket gates and send alerts to help inspectors perform selective controls is another way that AI-powered video analytics are used to increase operational staff efficiency and security management. It is obvious that AI systems have great potential and can address the pressing issues that the railway industry is currently facing.
The Europe Railway AI Market accounted for $XX Billion in 2023 and is anticipated to reach $XX Billion by 2030, registering a CAGR of XX% from 2024 to 2030.
By utilising real-time monitoring and machine learning algorithms, the AI-based Nokia Scene Analytics system being implemented by Swiss public transportation provider Baselland Transport (BLT) in Münchenstein intends to increase the safety of railroad crossings.
This implementation, done in partnership with Nokia, is a positive step toward employing analytics as an additional layer of security in risky environments. By giving crucial information in real-time, Nokia Scene Analytics serves as an intelligent set of “eyes” and helps to prevent or lessen the effects of an occurrence.
By reducing downtime and delays, the installation of Scene Analytics at railroad crossings also improves operating efficiency. AI-based platforms detect the object type in addition to sending anomalies to railway security in real-time, giving forensic investigators a fuller picture of the scenario after an incident.
