| Intelligent Operations of Rail Transportation |
| 轨道交通智能运营 |
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| Chair: |
Co-chairs: |
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| Ping Huang |
Xinlei Hu |
Jinlei Zhang |
| Southwest Jiaotong University, China |
Central South University, China |
Beijing Jiaotong University, China |
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| Keywords: |
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- Rail Transportation (轨道交通)
- Intelligent Operations (智能运营)
- Big Data and Artificial Intelligence (大数据与人工智能)
- Intelligent Optimization (智能优化)
- Train Operations (列车运行)
- Intelligent Dispatching (智能调度)
- Robustness (鲁棒性)
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| Summary: |
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- With the continuous expansion of the road network, the rail transit system is facing practical challenges such as tight transportation capacity, frequent operational disturbances, and low robustness. Traditional transportation organization and scheduling methods are difficult to fully utilize real-time data and cannot quickly respond to complex and changing operating environments. The rapid development of big data, artificial intelligence, and intelligent optimization technologies has provided new ideas for the compilation of train operation diagrams, real-time scheduling decisions, and emergency response. The purpose of this topic is to explore intelligent operation methods that combine data-driven and model driven approaches, with a focus on improving the feasibility of operational plans, real-time scheduling decisions, and robustness of system operation, providing support for achieving safer, more efficient, and robust rail transit operations.
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- 随着路网规模不断扩张，轨道交通系统正面临运能紧张、运营扰动频发、鲁棒性低等现实挑战。传统运输组织与调度方法难以充分利用实时数据，也无法快速响应复杂多变的运行环境。大数据、人工智能与智能优化技术的快速发展，为列车运行图编制、实时调度决策与应急处置提供了全新思路。本专题旨在探究数据驱动与模型驱动相结合的智能运营方法，重点提升运行计划可执行性、调度决策的实时性、系统运行的鲁棒性，为实现更安全、高效、鲁棒的轨道交通运营提供支撑。
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