CapsNet融合D-BiLSTM的区域复杂路网交通速度预测  

Traffic speed prediction of regional complex road networks integrating CapsNet with D-BiLSTM

作　　者：曹洁[1,2] 苏广 张红 李鹏辉[1] CAO Jie;SU Guang;ZHANG Hong;LI Peng-hui(School of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,China;School of Information Engineening,Lanzhou City University,Lanzhou 730070,China)

机构地区：[1]兰州理工大学计算机与通信学院,兰州730050 [2]兰州城市学院信息工程学院,兰州730070

出　　处：《吉林大学学报（工学版）》2024年第9期2531-2539,共9页Journal of Jilin University:Engineering and Technology Edition

基　　金：甘肃省重点研发计划项目(23YFGA0063);国家自然科学研究基金项目(62363022,61663021)。

摘　　要：针对交通模式复杂和动态的时空相关性导致现有预测方法在结构深度和预测尺度方面不足以学习交通演变的问题,提出了一种结合胶囊网络(CapsNet)和深层双向LSTM(DBiLSTM)的深度学习模型。该模型采用CapsNet识别路网的空间拓扑结构并提取空间特征,融合D-BiLSTM网络,同时考虑交通状态的前向和后向依赖关系,捕获不同历史时期的双向时间相关性,对目标区域内大规模复杂路网的交通进行预测。在真实交通路网速度数据集上进行的实验表明:提出模型的预测精度平均提高了10%以上,优于其他方法,在区域复杂路网的交通预测中具有较高的预测精度和良好的鲁棒性。Due to the complex and dynamic spatio-temporal correlation of traffic patterns leads to the inadequacy of existing methods to learn traffic evolution in terms of structural depth and prediction scale.a Deep learning model combining CapsNet and deep bi-directional LSTM(D-BiLSTM) was proposed.This model was used to identify the spatial topology of road networks and extract spatial features using CapsNet,was fused with the D-BiLSTM network,taking into account both the forward and backward dependencies of traffic states,and capturing the bi-directional temporal correlations of different historical periods,to forecast traffic on large-scale complex road networks in the target region.Experiments conducted on real traffic road network speed datasets show that the prediction accuracy of the proposed model is improved by more than 10% on average,outperforming other methods,with high prediction accuracy and good robustness in traffic prediction of regional complex road networks.

关 键 词：胶囊网络 深层双向LSTM 复杂路网 后向依赖 交通速度预测 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]