ResiliFlow：面向基础设施感知与灾害韧性的开放交通世界模型
ResiliFlow: An Open Transport World Model for Infrastructure Perception and Disaster Resilience
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中文总结 AI 辅助
ResiliFlow是面向基础设施感知与灾害韧性的开放交通世界模型平台，整合两类工作空间功能，实现交通模型的可检查复用，支撑研究协作与公众监督。
中文摘要 AI 辅助
交通韧性相关工作通常分散在独立的数据准备脚本、网络模型、仿真工具、图像检测系统及报告中，这种碎片化导致从观测到可测试、可评审的决策的推进十分困难。我们提出ResiliFlow，这是一个面向基础设施韧性、响应与恢复的开放交通世界模型概念及已实现平台。该平台连接两个工作空间：灾害交通韧性分析提供六项以地图为中心的功能，用于识别关键道路与关键区域、恢复优先级排序、中断路径规划、韧性测试及场景仿真；基于AI的交通基础设施感知与决策支持则整理街景与卫星数据，检测可见的道路、人行道及路缘状况，并为人工评审的干预规划准备这些观测结果。两个工作空间共享一个包含感知、预测、模型开发、验证、执行、决策、反馈及记忆的八步循环。研究验证记录假设与检查，而本地助手及可选的多提供商大语言模型Copilot可将用户问题转换为对可执行工具的受限调用。我们记录了平台架构、代表性数学模型、界面证据及计算机视觉学习结果。示例显示对8种可见状况类别的准确识别，而紧凑的误差分析表明困难案例如何指导持续学习。ResiliFlow展示了交通模型如何成为可检查、可复用且以问题为导向的系统，而非零散分析的集合。伴随的版本旨在支持机构评审下的研究协作、公众监督及扩展。
英文摘要
Transport resilience work is often split across separate data preparation scripts, network models, simulation tools, image inspection systems and reports. This fragmentation makes it difficult to move from an observation to a tested and reviewable decision. We introduce ResiliFlow, an open transport world model concept and an implemented platform for infrastructure resilience, response and recovery. The platform connects two workspaces. Disaster Transport Resilience Analysis provides six map-centred functions for critical-road and critical-area identification, recovery prioritisation, disruption routing, resilience testing and scenario simulation. AI-based Transport Infrastructure Perception and Decision Support organises street-level and satellite evidence, detects visible road, footpath and kerb conditions, and prepares these observations for human-reviewed intervention planning. Both workspaces share an eight-step cycle of perception, prediction, model development, verification, execution, decision, feedback and memory. Research Validation records assumptions and checks, while a local Assistant and an optional multi-provider large language model Copilot translate user questions into bounded calls to executable tools. We document the platform architecture, representative mathematical models, interface evidence and computer-vision learning results. Examples show accurate recognition across eight visible-condition classes, while compact error analysis demonstrates how difficult cases guide continued learning. ResiliFlow shows how transport models can become an inspectable, reusable and question-led system rather than a collection of disconnected analyses. The accompanying release is intended to support research collaboration, public scrutiny and extension under institutional review.
