智能城市2025，Vol.11Issue(3)：34-36,3.DOI:10.19301/j.cnki.zncs.2025.03.010
基于智慧交通空间模型数据预测研究
Research on data prediction based on smart transportation spatial model
摘要
Abstract
Smart traffic monitoring systems can monitor road network operations in real-time and respond quickly to sudden road events,but they have limitations in traffic flow prediction.To address this,this study uses the bat algorithm(BA)to optimize the hyperparameters of the long short-term memory network(LSTM),constructing a traffic flow prediction model based on improved LSTM.Experimental results show that the improved model has a lower mean absolute error(MAE)of 22.54 and a lower root mean square error(RMSE)of 35.16 compared to the traditional LSTM model.The application of this model can enhance traffic flow prediction accuracy and provide a technical basis for decision-making support in smart traffic systems.关键词
智慧交通/数据预测/交通流量/长短期记忆网络Key words
intelligent transportation/data prediction/traffic flow/long short term memory network分类
交通工程引用本文复制引用
邵洪清,高剑峰,杜宇..基于智慧交通空间模型数据预测研究[J].智能城市,2025,11(3):34-36,3.基金项目
2024年度茂名市哲学社会科学规划共建项目(2024GJ12) （2024GJ12）
2024年度广东省教育厅普通高校认定类科研项目(2024KTSCX273) （2024KTSCX273）
