AI Empowering Rail Transit: Technology Integration, Application Practice and Future Trends

Chen Dewang, Chen Pengqiao, Xiong Gang, Zhao Junhui, Wang Yihui, Zhou Qiang

Abstract: With the acceleration of urbanization and the continuous expansion of rail transit networks, rail transit systems face the demand for more efficient, refined, and intelligent solutions in operation scheduling, facility maintenance, and train control. Artificial Intelligence (AI), with its advantages in data perception, pattern recognition, and intelligent decision-making, is profoundly reshaping the technical system and management mode of rail transit. From the core perspective of AI-enabled rail transit, this paper systematically reviews the current research progress and engineering practices around the three key links of operation optimization, equipment maintenance, and intelligent driving, and summarizes the main challenges of existing methods in terms of high-dimensional data, insufficient model interpretability, and limited system integration. Furthermore, the coping paths of AI technologies such as deep learning, graph neural networks, collaborative multi-agent reinforcement learning, self-supervised learning, and digital twins are discussed. Finally, the trends of future rail transit in the directions of computing power support, standard system construction, and human-machine collaborative agent development are prospected, aiming to provide theoretical reference and practical enlightenment for the deep integration of AI and rail transit.

Key words: artificial intelligence; rail transit; intelligent transportation; digital twin; parallel intelligence; federated learning

人工智能赋能轨道交通：技术融合、应用实践与未来趋势

陈德旺，陈鹏桥，熊刚，赵军辉，王艺辉，周强

摘要：随着城市化进程加速推进与轨道交通网络持续扩容，轨道交通系统在运营调度、设施维护及列车控制等环节亟需更高效、精细化、智能化的解决方案。人工智能（AI）凭借数据感知、模式识别与智能决策等技术优势，正深刻重塑轨道交通的技术体系与管理模式。本文从人工智能赋能轨道交通的核心视角出发，围绕运营优化、设备维护、智能驾驶三大核心环节，系统梳理相关领域的当前研究进展与工程实践成果；同时总结现有技术方法在高维数据处理、模型可解释性不足、系统集成度有限等方面面临的核心挑战，并探讨深度学习、图神经网络、协同多智能体强化学习、自监督学习及数字孪生等人工智能技术的应对路径。最后，从算力支撑、标准体系构建、人机协同体发展等方向展望轨道交通的未来趋势，旨在为人工智能与轨道交通的深度融合提供理论参考与实践启示。

关键词：人工智能；轨道交通；智能交通；数字孪生；平行智能；联邦学习

来源：中国知网

Source: https://kns.cnki.net/reader/flowpdf?invoice=cV1AmtsSE7FxRCO5rXnLYKuKQ3n0E0kyB%2FV%2B9HJb1ES8BALTb6EwDrM4fIgVfdM78SSrJN%2BNxTqcdGE1dG2esuQ6%2FGHT3J5hcwLiwg7Pe0xpcMZ%2FuyMzsRj5m%2BqUEQzmzctjvyufHFidtDTJiIl3rCv7V9JwQ2BkDaJ3tfa%2BjGA%3D&platform=NZKPT&sourcetype=nxgp&product=CAPJ&filename=HDJT20260305003&tablename=capjlast&type=JOURNAL&scope=trial&cflag=overlay&dflag=pdf&pages=&language=CHS&trial=&nonce=1E5B9173F91C45A6A20AABA47556DCA1

发表时间：2026年3月6日

Date: March 6, 2026

检索：范薇

翻译：王烨

一审：王建秀

二审：彭莉

三审：罗玲娟

上传发布：姜浩