Mapmost

Rail Transit Digital Twin Safety Control Platform

Guided by the “perceive, analyze, warn, assess and respond” five-in-one philosophy, the platform deeply integrates urban multi-source data and IoT technology to build an all-element, all-scenario, full-life-cycle rail transit safety supervision system. Through high-precision 3D modeling, real-time data mapping and intelligent algorithm deduction, fused with a large-scale rail-transit passenger-flow prediction model, it creates a digital mirror of rail transit operations — significantly improving risk prediction, emergency response and resource dispatch efficiency, and helping rail operations shift toward smarter, more precise and proactive management.

Support

sales@mapmost.com

+86 512 6763 2003

Platform Construction Results

Risk Control

Passenger-flow prediction and warning (e.g. yellow/red large-crowd alerts), abnormal loitering monitoring and key-person identification strengthen source-level prevention.

Efficiency Gains

Digital means reduce manual intervention, improving rail safety supervision efficiency and precision.

Platform Construction Approach

Multi-dimensional Data Fusion & Real-time Monitoring

By integrating station 2D/3D geographic info, IoT device data, video surveillance, security screening and ticketing data, a unified data hub breaks down traditional data silos. Digital twin technology dynamically maps physical and virtual space; with real-time data collection and analysis it forms a “one-map safety situation”. For example, passenger-flow prediction models and video fusion monitor large-crowd risk points in real time, while historical data and AI algorithms anticipate peak hours and crowded areas to inform emergency dispatch. It also supports intelligent recognition of key personnel and abnormal loitering to speed up security response.

“Five-in-one” Risk Warning & Linked Response

Built on full-process closed-loop management of “perceive, analyze, warn, assess and respond”, a multi-level risk warning system uses smart sensing (infrared sensors, face-recognition cameras) to collect environment and personnel data in real time and dynamically analyze with multi-dimensional risk models (e.g. passing items over barriers, ticket anomalies). Once warning thresholds trigger, the platform auto-generates response plans and links relevant departments (police, fire) for coordinated response — e.g. a yellow large-crowd warning auto-launches broadcast diversion and police reinforcement, while a visual assessment interface shows risk spread paths to support command decisions, shifting from passive response to proactive prevention.

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