摘要
交通流预测作为智能交通系统的核心技术,其核心挑战在于如何有效建模交通数据中复杂的时空依赖性。当前主流模型(基于图神经网络和注意力机制)存在两大局限:①节点相似度计算受交通波动的时间错位影响,导致具有延迟传播特性的相似节点被误判;②空间特征提取未能协同捕获交通流的宏观规律(如周期性出行模式)与微观动态(如突发拥堵、交通事故等)。基于此,提出了LDFormer模型,引入动态时间规整(DTW)算法重构节点相似性度量,消除了传播延迟导致的时空偏差;同时设计了双通道空间建模机制,通过M_(glo)、M_(mic)可学习掩码矩阵分别对注意力生成的空间依赖关系进行宏观-微观特征的捕捉。通过3个基准数据集上的实验表明:该模型显著优于现有的时空预测方法。
Traffic flow prediction,as a core technology of intelligent transportation systems(ITS),faces the fundamental challenge of effectively modeling complex spatio-temporal dependencies in traffic data.Current mainstream models(based on graph neural networks and attention mechanisms)have two key limitations.Firstly,node similarity computation is affected by temporal misalignment in traffic fluctuations,causing misjudgment of similar nodes with delayed propagation characteristics.Secondly,spatial feature extraction fails to jointly capture macro-level patterns(e.g.,periodic travel patterns)and micro-level dynamics(e.g.,sudden congestion,traffic accidents)in traffic flows.To address these issues,LDFormer model was proposed,which introduced dynamic time warping(DTW)algorithm to reconstruct node similarity measurement,eliminating spatio-temporal deviations caused by propagation delays.Meanwhile,a dual-path spatial modeling mechanism was designed.Macro-micro characteristics of the attention-generated spatial dependencies were respectively captured by learnable mask matrices(M_(glo) and M_(mic)).Experiments on three benchmark datasets demonstrate that LDFormer model significantly outperforms existing spatio-temporal prediction methods.
作者
张建华
温政龙
ZHANG Jianhua;WEN Zhenglong(School of Civil Engineering&Traffic,Northeast Forestry University,Harbin 150040,Heilongjiang,China)
出处
《重庆交通大学学报(自然科学版)》
北大核心
2025年第8期99-107,共9页
Journal of Chongqing Jiaotong University(Natural Science)
基金
黑龙江省重点研发计划项目(JD22A014)
黑龙江省自然科学基金项目(YQ2022E003)。
关键词
交通工程
交通流预测
双尺度
注意力机制
时间序列聚类
traffic engineering
traffic flow prediction
dual-scale
attention mechanism
time series clustering
