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Service reliability is often the reason people prefer using private vehicles rather than public transportation. If AI can help us improve many aspects of our lives, can it help make public transportation more reliable? The answer is yes.

By leveraging AI and Machine Learning to automatically collect and analyses vehicle data, predictive, AI-enabled maintenance represents the most advanced method for achieving reliability in transportation. Providing fleet managers with real-time insights into individual bus components, it allows for more accurate scheduling of maintenance and repairs, improving fleet utilization, and reducing costs. Additionally, vehicle breakdowns can be prevented by predicting equipment failure, reducing downtime, and protecting revenue and customer satisfaction.

Compared to preventive maintenance, which requires visual inspections and assumes all vehicles have the same requirements, predictive maintenance accounts for variations between vehicles assigned to different routes.

For instance, a city bus will brake more frequently than a long-haul vehicle, and AI predictive maintenance can identify patterns in the data to predict when, for example, a brake pad replacement will be necessary. This allows for bulk ordering of parts and scheduling of maintenance during off-peak periods, reducing the costs associated with unplanned or pre-emptive part replacement and minimizing downtime.

AI-enabled predictive maintenance solutions, by virtue of requiring a complete vehicle data collection, also enable the digitization of repetitive tasks. Furthermore, the system proactively alerts engineers of possible risks, enabling improved service planning, according to

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