交通运输研究2024，Vol.10Issue(1)：18-27,10.DOI:10.16503/j.cnki.2095-9931.2024.01.003
基于SSA-CNN-BiLSTM组合模型的短时交通流量预测
Short-Term Traffic Flow Prediction Based on SSA-CNN-BiLSTM Combination Model
摘要
Abstract
In order to solve the problem of urban road traffic congestion and provide auxiliary means for decision-making of intelligent transportation systems,a combination model of SSA-CNN-BiLSTM was constructed to predict short-term traffic flow,taking into account the nonlinear and temporal characteristics of short-term traffic flow.Firstly,the original traffic flow data was subjected to outlier cleaning,wavelet threshold denoising,and normalization processing.Secondly,the SSA algorithm was used to iteratively optimize the three hyperparameters of the number of hidden layer units,initial learn-ing rate,and L2 regularization coefficient in the CNN-BiLSTM composite network.Finally,the opti-mal hyperparameter combination obtained from the search was input into the constructed combination network for training and prediction.The experimental results showed that compared with PSO and GWO algorithm,SSA algorithm had a faster convergence speed and stronger global optimization abili-ty in the process of network hyperparameter optimization.Compared with the three comparison models of CNN-BiLSTM,BiLSTM,and LSTM,the SSA-CNN-BiLSTM combination model showed a reduction of 5.46%,12.78%,and 20.38%in RMSE,and a decrease of 0.49%,2.24%,and 3.11%in MAPE respectively at a 5-minute time scale;and under the 15-minute time scale division the RMSE of SSA-CNN-BiLSTM model was decreased by 9.70,28.42,and 41.18,while the MAPE was decreased by 0.50%,1.98%,and 2.59%,respectively.Research has shown that the accuracy and stability of the short-term traffic flow prediction model of SSA-CNN-BiLSTM have been improved compared to existing algorithms,and road traffic conditions can be improved by providing more accurate short-term traffic travel information.关键词
智能交通/交通流预测/卷积神经网络/城市道路/麻雀搜索算法/双向长短时记忆神经网络Key words
ITS(Intelligent Transportation Systems)/traffic flow prediction/CNN(Convolutional Neural Network)/urban road/SSA(Sparrow Search Algorithm)/BiLSTM(Bidirectional Long Short-Term Memory Neural Network)分类
交通工程引用本文复制引用
陆由付,孔维麟,田垚,王庆斌,牟振华..基于SSA-CNN-BiLSTM组合模型的短时交通流量预测[J].交通运输研究,2024,10(1):18-27,10.基金项目
交通运输部交通运输行业重点科技项目(2021-ZD2-047) （2021-ZD2-047）
山东省交通运输科技计划项目(2021B49) （2021B49）
山东省高等学校青创科技支持计划项目(2021KJ058) （2021KJ058）
