Cable Transport Infrastructure Safety Management: Preventing falls with intelligent emergency braking

In large cable-transport infrastructure such as lifts and gondolas, fall risk remains whenever passengers fail to secure safety bars or behave unsafely while boarding. Safety personnel cannot realistically monitor dozens of operating units at all times with the naked eye, especially during night operations or severe weather. If a boarding defect is not linked immediately to live equipment control, a single incident can escalate into major casualties.

To close that monitoring gap, high-performance Vision AI was deployed in boarding and operating zones to track protective-gear status and passenger posture in real time. The system detects unsecured safety bars and abnormal movement instantly, then escalates the condition to the control room while directly controlling operating speed or triggering emergency braking. That architecture creates a continuous 24/7 active safety environment without waiting for manual intervention.

This intelligent safety-monitoring architecture raised detection of fall-related risk factors and boarding defects to above 99%, allowing operators to block accidents before they occur. Automated equipment intervention also reduced emergency braking and response lead time by more than 70%, helping secure golden time when an anomaly appears. The result is a global-standard model for safety management that combines precise Vision technology with direct machine control.