Public transit systems globally depend on buses for billions of rides annually. Effective maintenance of these buses is critical yet challenging, with traditional reactive practices often leading to unforeseen breakdowns and increased costs. AI technologies are changing this landscape by implementing predictive maintenance techniques that utilize real-time data to forecast potential issues, significantly reducing maintenance costs and downtime. Furthermore, condition-based maintenance schedules bus upkeep based on their specific operational conditions rather than on a fixed timetable, offering further cost efficiencies and reliability benefits.
AI algorithms can sift through vast amounts of real-time data from onboard sensors to monitor critical systems such as engine performance, brake functionality, and tire pressure.
Predictive maintenance can reduce maintenance costs by up to 30 percent and unplanned downtime by up to 45 percent.
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