TRAINLINE IS USING artificial intelligence (AI) to automate disruption alerts in its voice app for Google Assistant.
The Trainline team's model trawls rail operators' Twitter accounts for relevant data. The information is then available to passengers when they ask questions like 'How's my commute doing?' or 'Is this train on-time?'
The system uses natural language processing and machine learning to analyse the data on rail disruptions collected from Twitter feeds. The notification system automatically classifies the importance of a message, while a second layer of contextual scoring determines which stations are affected by a disruption.
The AI automatically matches this information to individual user journeys and uses it to inform customers using the voice app about disruptions. Trainline says that alerts will often be pushed "before this data is available through the national rail data feeds".
Customers can also view the history of the disruption, so they can see its scale, when it started, how it has unfolded and what is being done to fix it.
Senior director of product at Trainline, David Slocombe, said: "By creating an AI that can read information being shared on train operators' Twitter feeds, we were able to overcome a key challenge in collecting data quickly from a variety of different sources.
"It's another example of how Trainline is harnessing the power of AI, big data and voice tech to make travel a smoother experience for everyone."
The voice disruption notification feature is available in the Trainline voice app for Google Assistant now. There is currently no information on an expansion to Amazon Alexa.
Trainline CTO Mark Holt told INQ: "From our seat-finding feature BusyBot, powered by crowdsourced data, to the UK's first price prediction tool for rail tickets, our tech squads are always finding new ways to enhance the travel experience.
"Our AI-powered voice disruption alerts are the latest embodiment of the culture of innovation we've created at Trainline. Our talented team create and build these features at speed and it's brilliant to see the impact they have in making our customers' journeys simpler and smarter." µ
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