计算机应用与软件2025，Vol.42Issue(6)：136-140,177,6.DOI:10.3969/j.issn.1000-386x.2025.06.018
基于改进CNN-LSTM的高速公路交通流量预测研究
RESEARCH ON EXPRESSWAY TRAFFIC FLOW FORECASTING BASED ON IMPROVED CNN-LSTM
何仲祥 1吴明礼1
作者信息
- 1. 宁夏回族自治区公路联网收费清分结算中心 宁夏银川 750011
- 折叠
摘要
Abstract
This paper addresses the issues of complex and diverse spatiotemporal characteristics,and the insufficiencies in robustness and adaptability of traffic flow.We propose an improved model based on the convolutional neural network(CNN)and long short-term memory(LSTM)network for highway traffic flow prediction.The model aimed to resolve the correlations in time series and spatial network by extracting relevant features and conducting perturbation analysis during the model training process,introducing an error compensation mechanism to enhance the performance of traffic flow prediction.Experimental results indicate that the model can effectively predict traffic flow in highway networks,demonstrating good accuracy and robustness,which holds significant implications for the construction of intelligent transportation systems.关键词
卷积神经网络/长短期记忆网络/交通流量预测/智能交通Key words
CNN/LSTM/Traffic flow forecasting/Intelligent transportation分类
信息技术与安全科学引用本文复制引用
何仲祥,吴明礼..基于改进CNN-LSTM的高速公路交通流量预测研究[J].计算机应用与软件,2025,42(6):136-140,177,6.
