基于动态反投影网络的细粒度交通流推断模型  

Fine-grained Traffic Flow Inference Model Based on Dynamic Back Projection Network

作　　者：许明 齐光尧 奇格奇 Xu Ming;Qi Guangyao;Qi Geqi(College of Software,Liaoning Technical University,Huludao 125105,China;School of Traffic and Transportation,Beijing Jiaotong University,Beijing 100044,China)

机构地区：[1]辽宁工程技术大学软件学院,辽宁葫芦岛125105 [2]北京交通大学交通运输学院,北京100044

出　　处：《系统仿真学报》2025年第3期657-666,共10页Journal of System Simulation

基　　金：国家自然科学基金(72371021)。

摘　　要：为解决现有细粒度城市流推断模型在复杂交通区域中的推断结果存在较大误差的问题,提出一种基于动态反投影网络的细粒度交通流推断模型。计算输入粗粒度交通流与外部因素之间的多维交互,将交互结果与粗粒度交通流进行动态自适应融合,使其特征之间能够相互影响和调整,以协助模型推理。结合深度卷积和自注意力机制来学习局部信息和全局信息,提高后续模块对输入数据的理解能力。通过反投影算法和门控交叉注意力机制,实现在细粒度层次中学习复杂区域的交通流特征。在流量归一化机制的基础上引入了非线性变换通路,旨在利用不同层次信息实施空间结构约束,进一步提升模型推断结果的准确性。实验结果表明:所提算法在主观评价和客观度量上均优于同类模型,特别是在市中心入口、桥梁区域等复杂交通区域下的表现尤为出色。To solve the problem of large errors in the inference results of existing fine-grained urban flow inference models in complex traffic areas,a fine-grained traffic flow inference model based on dynamic back-projection network is proposed.The multi-dimensional interaction between the input coarse-grained traffic flow and external factors is calculated,and the interaction results are dynamically and adaptively fused with the coarse-grained trafic flow,so that the features can interact and adjust each other to assist model reasoning.Combining deep convolution and self-attention mechanism to learn local information and global information,and improve the understanding of input data by subsequent block.Through the back projection algorithm and gated cross attention mechanism,the traffic flow characteristics of complex regions are learned at a fine-grained level.Finally,a nonlinear transformation path is introduced based on flow normalization mechanism to enforce spatial structure constraints using information at diferent levels,thereby improving the inference accuracy of the model.Experimental results demonstrate that the proposed model outperforms similar methods in both subjective evaluation and objective metrics,particularly excelling in complex traffic areas such as city center entrances and bridge zones,where its performance is notably superior.

关 键 词：细粒度交通流推断 动态自适应融合 反投影算法 门控交叉注意力 自注意力 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术] U491.2[自动化与计算机技术—计算机科学与技术]