摘要
随着铁路行业快速发展,客运任务持续增加,客站面临日益严峻的客流安全问题。为实时监测客流动态、精细化解析客流多粒度特征,提出1种基于视频分析技术的客站多粒度动态客流评估(MYDC)模型。首先,构建铁路客运站客流数据集;其次,设计基于YOLO和判别式相关滤波(DCF)跟踪算法的旅客客流细粒度特征感知网络,并改进面向客站的自适应人群定位Transformer(CLTR)模型,以捕捉客流整体分布的粗粒度特征;最后,基于客流的物理属性及其微观与宏观特征,构建多注意力时空图卷积网络(MASTGCN),挖掘客流的时空动态趋势,评估站内客流安全风险等级。结果表明:细粒度特征的提取累计误差为6.9%,粗粒度特征的识别精确率为89.1%,客流安全评估模型的召回率为87.5%。该模型可为客流管理提供精准的数据支撑,具有较强的工程应用价值。
With the rapid development of the railway industry and the continuous increase of passenger transport tasks,railway passenger stations are facing increasingly severe passenger flow safety issues.To realize realtime monitoring of passenger flow dynamics and finely analyze the multi-granularity characteristics of passenger flow,a Multi-granularity Yardstick for Dynamic Crowds(MYDC)model for railway passenger stations based on video analysis technology is proposed.Firstly,a passenger flow dataset for railway passenger stations is constructed.Secondly,a fine-grained feature perception network for passenger flow is designed based on YOLO and Discriminative Correlation Filter(DCF)tracking algorithm,and the adaptive crowd localization Transformer(CLTR)model for railway passenger stations is improved to capture the coarse-grained features of the overall passenger flow distribution.Finally,based on the physical attributes of passenger flow as well as its micro and macro characteristics,a Multi-Attention Spatio-Temporal Graph Convolutional Network(MASTGCN)is constructed to mine the spatio-temporal dynamic trends of passenger flow and assess the safety risk level of passenger flow in the station.The results show that the cumulative error of fine-grained feature extraction is 6.9%,the recognition accuracy of coarse-grained features is 89.1%,and the recall rate of the passenger flow safety assessment model is 87.5%.The proposed model can provide accurate data support for passenger flow management and has strong engineering application value.
作者
刘玉鑫
LIU Yuxin(Institute of Computing Technology,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China)
出处
《中国铁道科学》
2026年第2期244-255,共12页
China Railway Science
基金
中国国家铁路集团有限公司科技研究开发计划课题(N2025X026)。
关键词
客流感知
视频分析
铁路客站
安全评估
Transformer模型
时空图卷积网络
Crowd perception
Video analysis
Railway passenger station
Security assessment
Transformer model
Spatio-Temporal Graph Convolutional Network(STGCN)
