Special Session Ⅲ

AI-Driven Modeling, Optimization, and Resilient Management of Multimodal Transportation Systems 人工智能驱动的多模式交通系统建模、优化与韧性管理
Submission Deadline: September 20, 2026

Chair:	Co-chair:

Guangnian Xiao Shanghai Maritime University, China	Xinqiang Chen Shanghai Maritime University, China

Keywords:
· Artificial Intelligence (人工智能) · Multimodal Transportation Systems (多模式交通系统) · Transportation System Modeling (交通系统建模) · Intelligent Optimization (智能优化) · Collaborative Scheduling (协同调度) · Transportation Resilience (交通韧性) · Low-Carbon Transportation (低碳交通)

Topics:
· AI-Driven Multimodal Travel Demand Forecasting and Behavior Analysis (人工智能驱动的多模式交通需求预测与行为分析) · Modeling, Simulation, and Intelligent Optimization of Multimodal Transportation Systems (多模式交通系统建模、仿真与智能优化) · Coordinated Scheduling of Rail Transit and Integrated Urban Transportation (轨道交通与城市综合交通协同调度) · Large Transportation Models and Generative Artificial Intelligence Applications (交通大模型与生成式人工智能应用) · Digital Twins and Intelligent Operations Management for Transportation Systems (交通系统数字孪生与智慧运营管理) · Intermodal Transportation, Port Collection and Distribution, and Smart Logistics Optimization (多式联运、港口集疏运与智慧物流优化) · Demand-Responsive Transport, Shared Mobility, and Intelligent Mobility Services (需求响应式交通、共享出行与智能移动服务) · Risk Assessment, Emergency Management, and Resilience Enhancement of Transportation Systems (交通系统风险评估、应急管理与韧性提升) · Low-Carbon Transportation, Electrification, and Coordinated Energy Management (低碳交通、电动化与能源协同管理) · Transportation Safety, Vehicle–Infrastructure Cooperation, and Autonomous Driving System Optimization (交通安全、车路协同与自动驾驶系统优化)

Summary:
· Artificial intelligence is rapidly reshaping the perception, prediction, scheduling, and governance of multimodal transportation systems. This topic is important for improving transport efficiency, safety, low-carbon development, and system resilience. The forum is intended for researchers, engineers, and policymakers in transportation engineering, rail transit, logistics, urban planning, and intelligent optimization. It aims to facilitate the exchange of frontier methods and practical cases, promote interdisciplinary collaboration, and develop transferable approaches to transportation modeling, optimization, and resilient management.

· 人工智能正加速重塑多模式交通系统的感知、预测、调度与治理方式，该主题对于提升交通效率、安全性、低碳化与系统韧性具有重要意义。本专题面向交通工程、轨道交通、物流运输、城市规划及智能优化领域的科研人员、工程技术人员和管理决策者，旨在交流前沿方法与实践案例，促进跨学科合作，并形成可推广的建模、优化与韧性管理思路。