基于VMD-BiLSTM-BLS模型的短时交通流预测  

Short-term Traffic Flow Prediction Based on VMD-BiLSTM-BLS Model

作　　者：卢海鹏 韩莹[1] 张凯[1] 张龄允 丁昱杰 LU Hai-Peng;HAN Ying;ZHANG Kai;ZHANG Ling-Yun;DING Yu-Jie(School of Automation,Nanjing University of Information Science and Technology,Nanjing 210044,China)

机构地区：[1]南京信息工程大学自动化学院,南京210044

出　　处：《计算机系统应用》2022年第5期238-245,共8页Computer Systems & Applications

基　　金：国家自然科学基金(62076136)。

摘　　要：准确的短时交通流预测在智慧交通系统中至关重要.近年来,双向长短时记忆网络(BiLSTM)被广泛地应用于短时交通流预测中,但由其结构特点,易产生过拟合现象,从而影响预测精度.鉴于宽度学习系统(BLS)能够解决过拟合的问题,本文将深度学习与宽度学习相结合.进一步地,为减少噪声对车流量数据的干扰,引入变分模态分解(VMD)进行降噪处理,提出VMD-BiLSTM-BLS短时车流量预测模型.本文以PeMS交通流数据为例,进行预测分析.结果表明:与基线模型、消融模型和现有模型进行对比,本文模型预测精度均表现最佳,能够更好的反应路口短时交通流的状况.Accurate short-term traffic flow forecasting is very important in smart transportation systems.In recent years,bi-directional long-short term memory(BiLSTM)has been widely used in short-term traffic flow prediction,but due to its structural characteristics,it is prone to overfitting,affecting the prediction accuracy.Given that the broad learning system(BLS)can solve the problem of overfitting,this study combines deep learning with broad learning.Furthermore,the variational mode decomposition(VMD)is introduced for noise reduction so as to minimize the interference of noise on the traffic data.By doing this,the VMD-BiLSTM-BLS short-term traffic flow prediction model is proposed in this study.The PeMS traffic flow data is used as an example for predictive analysis,and the results show that compared with the baseline model,the ablation model,and the existing model,the proposed model has the best prediction accuracy and can better reflect the short-term traffic flow at the intersection.

关 键 词：短时交通流预测 双向长短时记忆网络 过拟合 宽度学习系统 变分模态分解 深度学习 

分 类 号：U491.1[交通运输工程—交通运输规划与管理]