· Artificial Intelligence (人工智能)

· Multi-Mode Rail Transit (多制式轨道交通)
· Multi-Network Integration (多网融合)
· Intelligent Dispatching (智能调度)
· Transport Organization (运输组织)
· Operations Optimization (运营优化)
· Network Resilience (网络韧性)
· Disruption Management (干扰管理)

· AI-based collaborative transport organization and timetabling in multi-mode rail networks (基于AI的多制式轨道交通协同运输组织与运行图优化)

· Dynamic resilience quantitative assessment and vulnerability analysis of complex rail networks (复合路网动态韧性量化评估与脆弱性分析)
· Data-driven real-time train dispatching and conflict resolution algorithms (数据驱动的列车实时调度与冲突疏解算法)
· Intelligent emergency response and disruption management for unexpected events (面向突发事件的智能应急响应与干扰管理)
· Proactive resilience enhancement mechanisms and risk prediction in rail transit systems (轨道交通系统主动韧性提升机制与风险预测)
· Cross-mode passenger flow forecasting and dynamic capacity matching technologies (跨制式客流精准预测与运力动态匹配技术)
· Intelligent rolling stock scheduling and efficient resource allocation across networks (智能车底交路计划优化与路网资源高效配置)
· Digital twin and simulation technologies for efficient and resilient operations (面向高效与韧性运营的数字孪生与仿真技术)

This special session investigates how artificial intelligence (AI) can overcome the limitations of traditional dispatching, which often struggles to balance routine operational efficiency with resilience during disruptions, within the context of integrated multi-mode rail transit networks. This session emphasizes the establishment of dynamic resilience assessment frameworks and proactive enhancement mechanisms, which are crucial for improving the comprehensive transport performance and robustness of complex networks. Targeting academic researchers, operations managers, and AI algorithm engineers in the rail transit sector, this session aims to share state-of-the-art algorithms for intelligent dispatching, quantitative resilience assessment, and emergency recovery. The ultimate goal is to offer innovative insights for multi-network coordination and drive the practical application of AI theories into efficient and resilient operational dispatching.

本专题探讨在多制式轨道交通多网融合背景下，AI技术如何破解传统调度难以兼顾常态高效与灾时韧性的瓶颈，构建动态韧性评估体系与主动提升机制，对提升复合路网的综合运输效能与抗干扰能力至关重要。面向轨道交通领域的学术研究者、运营管理者及AI算法工程师，旨在分享前沿的智能调度、韧性量化评估与应急恢复算法，为多网协同提供创新思路，推动AI理论向实际的高效、韧性运营指挥落地转化。