摘要
随着城市化进程的加快,地铁客流量的动态变化及不确定性带来的扰动会影响我国城市轨道交通运营服务质量。本研究面向轨道交通网络化运营提出一种基于生成对抗网络(GAN)的客流数据增强方法,通过利用少量的原始客流数据生成大量特征相同的可用数据,进行数据增强。在客流数据增强基础上,进一步研究基于时空多维的轨道交通运营态势精准预测方法,提出基于长短期记忆网络((LSTM))、卷积神经网络(CNN)和图神经网络(GCN)的客流数据预测方法,分别从时间维度和时空维度实现对轨道交通的客流量数据进行精准预测。短时客流数据的生成和预测能够为列车运行调整提供坚实基础,为提升轨道交通运营服务质量保驾护航,为未来城市发展规划提供理论支撑。
With the acceleration of urbanization,the dynamic change of subway passenger flow and the perturbation caused by uncertainty will affect the quality of urban rail transit operation service in China.This study proposes a passenger flow data enhancement method based on generative adversarial network for networked rail transit operation,which generates a large amount of usable data with the same characteristics by using a small amount of original passenger flow data for data enhancement.On the basis of passenger flow data enhancement,we further study the accurate prediction method of rail transit operation posture based on spatio-temporal multidimensionality,and propose a passenger flow data prediction method based on long-short-term memory network,convolutional neural network,and graphical neural network,which can realize the accurate prediction of the passenger flow data of the rail transit in the temporal dimension and spatio-temporal dimension,respectively.The generation and prediction of short-time passenger flow data can effectively alleviate the pressure of passenger flow.Additionally,accurate passenger flow prediction provides a solid foundation for adjusting train operations,improves the quality of rail transit services,and offers theoretical support for future urban development planning.
作者
郜新军
张梅欣
朱力
GAO Xinjun;ZHANG Meixin;ZHU Li(Signal&Communication Research Institute,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China;National Key Laboratory of Autonomous Operation of Advanced Railway Transportation,Beijing Jiaotong University,Beijing 100044,China)
出处
《计算机科学》
北大核心
2025年第S1期704-708,共5页
Computer Science
基金
北京市自然科学基金(L221016)。
关键词
城市轨道交通
数据增强
生成对抗网络
客流预测
神经网络
Urban rail transit
Data augmentation
Generative adversarial networks
Passenger flow prediction
Neural networks
