交通信息与安全2023，Vol.41Issue(5)：12-23,12.DOI:10.3963/j.jssn.1674-4861.2023.05.002
基于集合经验模态分解降噪和优化LSTM的道路交通事故预测
A Method for Predicting Traffic Accidents Based on an Ensemble Empirical Mode Decomposition and an Optimized LSTM Model
摘要
Abstract
Accurate prediction of road traffic accidents is essential to improve traffic safety effectively.Due to the frequent non-linear,fluctuating,and nonperiodic characteristics of accident data,existing algorithms have the prob-lem of poor prediction performance.Therefore,a method for traffic prediction that uses a long short-term memory network(LSTM)combined with ensemble empirical mode decomposition(EEMD)and particle swarm optimiza-tion(PSO)is proposed.Based on a single model,the EEMD is first used to break down the noise of accident data and obtain multiple subsequences and a residual.Based on LSTM optimized by PSO,the temporal feature infor-mation extracted from the data is predicted under the optimal network structure of LSTM.Then,the prediction re-sults of each subsequence and residual are summed to obtain the final prediction result.The results show that,compared with the EMD-PSO-LSTM,PSO-LSTM,EEMD-LSTM,and LSTM,the ermse of EEMD-PSO-LSTM is reduced by 8.7%,48.3%,53.1%,and 57.6%,respectively.Meanwhile,the emape is reduced by 12.4%,36.9%,50.6%,and 61.2%,respectively.Compared with the PSO-LSTM,the ermse of the EEMD-PSO-LSTM is reduced by 60.2%,the emape is reduced by 12.4%,and the r2 is increased by 0.616 5.The PSO Introduced to optimize neural networks can help improve prediction performance.Compared with the EEMD-LSTM,the ermse of the EEMD-PSO-LSTM is reduced by 53.1%,the emape is diminished by 50.6%,and the r2 is climbed to 0.807 8.The re-sults can improve the prediction accuracy of traffic accidents and help relevant departments effectively improve road traffic safety.关键词
交通安全/事故预测/长短时记忆神经网络/粒子群算法/集合经验模态分解Key words
traffic safety/accident prediction/long short-term memory neural network/particle swarm algorithm/ensemble empirical mode decomposition分类
交通工程引用本文复制引用
刘清梅,万明,严利鑫,郭军华..基于集合经验模态分解降噪和优化LSTM的道路交通事故预测[J].交通信息与安全,2023,41(5):12-23,12.基金项目
国家自然科学基金项目(52162049)、赣鄱俊才支持计划-主要学科学术和技术带头人培养项目——青年人才(20232BCJ23012)、江西省研究生创新专项(YC2021-S457)资助 （52162049）
