现代信息科技2023，Vol.7Issue(23)：151-155,161,6.DOI:10.19850/j.cnki.2096-4706.2023.23.031
基于改进ICSO-LSTM方法的城市交通预测研究
Research on Urban Traffic Prediction Based on Improved ICSO-LSTM Method
摘要
Abstract
In order to further improve traffic flow prediction on urban roads and reduce urban congestion,an urban traffic prediction method based on an Improved Chicken Swarm Optimization Long Short-Term Memory(ICSO-LSTM).Firstly,the Chicken Swarm Optimization is optimized in three aspects,namely,nonlinear decreasing rooster position update,weighted hen individual and adaptive following coefficient optimization,to address the problem that the Chicken Swarm Optimization has a fast convergence rate and easily falls into local optimum;secondly,the prediction model of ICSO optimized LSTM network parameters is established;finally,ICSO-LSTM is used in traffic flow prediction,and simulation experiments show that the method has good predictive performance in urban traffic flow prediction.关键词
鸡群算法/长短期记忆神经网络/城市交通预测Key words
Chicken Swarm Optimization/Long Short-term Memory Network/urban transportation forecast分类
信息技术与安全科学引用本文复制引用
陈暄..基于改进ICSO-LSTM方法的城市交通预测研究[J].现代信息科技,2023,7(23):151-155,161,6.基金项目
绍兴市哲学社会科学研究"十四五"规划2023年度重点课题——基地智库专项课题(145J069) （145J069）
