ASTNN:一种用于道路级稀疏交通流预测的注意力时空神经网络模型  

ASTNN:An Attention Spatio-Temporal Neural Network for Road-Level Sparse Traffic Flow Prediction

作　　者：赵今朝 魏志成[1,2,3] ZHAO Jinzhao;WEI Zhicheng(College of Computer and Cyber Security,Hebei Normal University,Shijiazhuang 050024,China;Key Lab of Network and Information Security of Hebei Province,Shijiazhuang 050024,China;Hebei Engineering Research Center for Supply Chain Big Data Analysis and Data Security,Shijiazhuang 050024,China)

机构地区：[1]河北师范大学计算机与网络空间安全学院,石家庄050024 [2]河北省网络与信息安全重点实验室,石家庄050024 [3]供应链大数据分析与数据安全河北省工程研究中心,石家庄050024

出　　处：《地球信息科学学报》2025年第3期682-697,共16页Journal of Geo-information Science

基　　金：中央引导地方科技发展基金项目(236Z0104G);河北师范大学2024年研究生创新资助项目(XCXZZSS202442)。

摘　　要：【目的】城市交通流量预测对智能交通系统至关重要。传统方法常将路网划分为栅格进行区域预测,但忽略了道路间的关联,导致预测结果无法准确反映交通流变化。基于道路段数据的方法虽能捕捉道路间的空间联系,却面临轨迹映射时数据冗余、轨迹错配和数据稀疏等问题。【方法】为了解决这些问题,本文提出了一种用于道路级稀疏交通流预测的注意力时空神经网络(ASTNN)模型。模型首先对轨迹数据做预处理和基于隐马尔可夫模型(HMM)的地图匹配,得到各道路段的交通流数据;并采用面向路网的自适应紧凑二维图像表示方法,将路网表示为以道路段为像素点的二维图像。在分析交通流时空特征的基础上,提出了两种新的注意力时空模块:注意力时空记忆块(ASTM block)和注意力时空聚焦块(ASTF block),分别用于时间相关性挖掘和空间稀疏特征提取。基于这2个模块,并融合外部信息,构造了ASTNN模型实现道路级交通流预测。【结果】本研究以成都出租车轨迹数据为研究对象,在成都市三环区域内的五级路网中,完成了轨迹数据的预处理和流量的映射,并对流量预测模型进行了验证。结果显示,本文所提出的数据处理方法可使轨迹与路网匹配时间降低73.6%。在与CNN、ConvLSTM、GRU、STNN等现有方法的对比实验中,本文方法在RMSE、MAE以及R^(2)等指标上均取得了最优的预测准确度。在此基础上,进一步验证了在ASTNN中引入温度信息对提高预测准确度的显著作用,为模型性能优化提供了新的思路。【结论】本研究提出的ASTNN模型为城市道路级稀疏交通流预测提供了可行的实施思路和技术路线。[Objectives]City-wide traffic flow prediction plays a crucial role in intelligent transportation systems.Traditional studies partition road networks into grids,represent them as graph structures with grids as nodes,and use graph neural networks for region-level prediction.However,this region-based approach overlooks the relationships between individual roads,making it difficult to reflect traffic flow changes of roads.Methods based on road segment data can better capture spatial connections between roads and enable more accurate traffic flow predictions.However,mapping trajectory data to roads presents challenges such as redundant data and trajectory mismatches,and traffic flow data after mapping is sparse.Existing methods struggle to effectively capture the spatial correlation in sparse traffic conditions.[Methods]To address these issues,this study proposes an Attention Spatio-Temporal Neural Network(ASTNN)model for road-level sparse traffic flow prediction.The model first preprocesses trajectory data and applies Hidden Markov Model(HMM)-based map matching to obtain road-level traffic flow data.It then introduces an adaptive compact 2D image representation method to model the road network as a 2D image,where road segments are represented as pixel points.Based on an analysis of the spatial and temporal characteristics of traffic flow,two new attentional spatiotemporal blocks are proposed:Attentional Spatio-Temporal Memory Block(ASTM block)for mining temporal correlations and attentional spatial-temporal focusing block(ASTF block)for extracting spatial sparse features.By integrating these two blocks with external information,ASTNN is constructed to achieve road-level traffic flow prediction.[Results]This study uses Chengdu taxi trajectory data as a case study.After preprocessing trajectory data and mapping traffic flow,the proposed model is validated on a five-level road network within Chengdu’s third ring area.Results indicate that the proposed data processing method reduces trajectory-to-road network matching

关 键 词：道路级交通流预测 轨迹匹配 路网图像 稀疏数据 时空注意力 多尺度卷积 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]