华南理工大学学报（自然科学版）2025，Vol.53Issue(5)：82-93,12.DOI:10.12141/j.issn.1000-565X.240480
用于交通流预测的时空异质化两阶段融合网络
A Spatiotemporal Heterogeneous Two-Stage Fusion Network for Traffic Flow Prediction
摘要
Abstract
In response to the existing traffic flow prediction studies that fail to fully integrate complex spatiotempo-ral correlations and heterogeneities,this paper designs a traffic flow prediction network based on grid data,namely the spatiotemporal heterogeneous two-stage fusion neural network marked as ST_HTFNN.This network employs a phased and hierarchical spatiotemporal feature extraction architecture,and adopts a new model where the static and dynamic feature extraction stages are serialized.In the static feature extraction stage,a novel Mamba-like linear at-tention(MLLA)block is introduced as a static heterogeneous fusion unit to achieve spatial correlation and heteroge-neity fusion mining.In the dynamic feature extraction stage,a simple and efficient dynamic heterogeneous fusion unit is designed,and dilated convolution is combined with gating mechanisms to adaptively fuse and capture global and local spatiotemporal correlations and heterogeneities.Furthermore,to address the smoothing of road features during the deep convolution process for road-level traffic flow characteristics,a road feature enhancement module is designed to reconstruct and enhance road information.Finally,an external disturbance feature fusion module is de-signed to integrate the impact of external disturbance features on traffic flow prediction results.Experimental re-sults on three real-world traffic datasets,namely BikeNYC,TaxiCQ and TaxiBJ,demonstrate that the ST_HTFNN model outperforms the existing benchmark methods,respectively with a decrease of 6.13%,0.8%and 7.01%in the mean absolute error of prediction accuracy.关键词
交通流预测/栅格数据/时空异质化/膨胀卷积/门控机制Key words
traffic flow prediction/grid data/spatiotemporal heterogeneity/dilated convolution/gating mechanism分类
信息技术与安全科学引用本文复制引用
侯越,尹杰,张志豪,卢可可..用于交通流预测的时空异质化两阶段融合网络[J].华南理工大学学报（自然科学版）,2025,53(5):82-93,12.基金项目
国家自然科学基金项目(62063014,62363020) （62063014,62363020）
甘肃省自然科学基金项目(22JR5RA365) Supported by the National Natural Science Foundation of China(62063014,62363020)and the Natural Sci-ence Foundation of Gansu Province(22JR5RA365) （22JR5RA365）
