Artificial Intelligence and data analytics are set to play a key role in train operations, according to Thameem Kamaldeen, managing director (signalling and infrastructure) at Alstom India.
Speaking to reporters in Bengaluru, Kamaldeen said, "AI has opened up fresh avenues for ensuring safety in the rail industry. By analysing real-time data, systems can detect potential hazards and promptly alert operators."
"AI-powered cameras can monitor track conditions, detecting abnormalities and preventing derailments. The machine learning tool automatically analyses images and data, classifying the size and type of obstacle—be it human, animal, vehicle, rockslide, etc — and calculates the distance between the train and the obstacle, enabling operators to take corrective action" he said.
In India, Alstom operates six manufacturing units. Around 12,000 people are employed in India, with 4,000 in signalling alone. "Signalling system of around 40 Metro projects, including in Sydney and Montreal are designed in India. Alstom's R&D center in Bengaluru, with 3,000 employees, contributes significantly, accounting for 90% of this work," said Kamaldeen.
Alstom has also unveiled a digital signaling experience center in Bengaluru, featuring a control center, track elements, and signaling equipment to demonstrate projects to customers.
"We currently have around 350 professionals in signalling in Hyderabad, and we anticipate doubling this number. We are also planning to establish R&D centers in both Hyderabad and Delhi," he said.
Predictive maintenance
Kamaldeen highlighted predictive maintenance as a promising aspect of railways' digital transformation. "It involves leveraging data collected during equipment operation to identify real-time maintenance issues, enabling accurate repair planning. Trains can then operate without unexpected interruptions due to emergency or unnecessary routine maintenance," he said.
This approach proactively detects challenges, increasing reliability, reducing downtime, and minimising repair costs.
Alstom has also leveraged big data and AI-based automated solutions to track radio signal performance. "This includes predicting failures before they occur and efficiently investigating and optimizing maintenance activities in real-time" he said.
Multimodal mobility orchestration
Kamaldeen also touched upon the potential of intelligent multimodal systems for coordinated travel in and around cities.
"AI-based solutions forecast occupancy, crowdedness impacts on services, and optimise travel time by compiling data from various sources. These sources include ticketing, video, and sensors. AI also integrates external factors like weather, strikes, and events to optimise travel time, anticipate and control passenger density in real-time, manage demand peaks, and control passenger flows" he said.
Autonomous trains
According to Kamaldeen, the next phase of digitalisation in the railway industry is the introduction of autonomous rail operations. "Autonomous mobility in railways enhances safety and operations by eliminating guesswork, human error, and variability. This system is combined with Automatic Train Control, Automatic Train Protection, and Automatic Train Supervision, allowing operators to manage train fleets more optimally". Taichung Metro's Green Line in Taiwan is being operated in a fully automatic UTO (Unattended Train Operation) since 2021.
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