T/GBAS 59-2023 Intelligent Operation and Maintenance Data of Qiaodian Tunnel based on Radar Group Road Full Domain Traffic Digital Twin and Risk Early Warning
T/GBAS 59-2023 Intelligent Operation and Maintenance Data of Qiaodian Tunnel based on Radar Group Road Full Domain Traffic Digital Twin and Risk Early Warning
Basic Information
Scope
Scope: This document specifies the basic requirements, system architecture and functions, software systems, hardware equipment, data formats, and transmission requirements for radar-based traffic digital twins and risk early warning systems in operation and maintenance management. This document is applicable to the implementation of traffic digital twins and risk early warning services for roads in bridge-island-tunnel cross-sea channels projects. Main technical content: This document specifies the basic requirements, system architecture and functions, software systems, hardware equipment, data formats, and transmission requirements for radar-based traffic digital twins and risk early warning systems in operation and maintenance management. The main body of this document includes eight parts: scope, normative references, terms and definitions, basic requirements, system architecture and functions, software systems, hardware equipment, and data formats and transmission requirements. This document clarifies the technical requirements of the system architecture of radar clusters and the related functional modules for implementing risk early warning; clarifies the software and hardware components of radar clusters, specifies the deployment methods of software systems and the capability requirements including data access and sharing capabilities, gives the installation and deployment methods of hardware equipment, and clarifies the technical parameter requirements of various hardware equipment; constructs a metadata model for implementing traffic risk early warning based on radar clusters, and uses "unique coding" to associate different types of metadata; and gives the rules and coding tables for the classification and coding of traffic digital twins and risk early warning based on radar clusters.