To solve the problems of low prediction accuracy,insufficient generalization and incomplete hy-perparameters tuning of deep learning model existed in traffic flow forecasting task,an improved ant colony algorithm based Bi-LSTM traffic flow prediction model is put forward,which uses global optimization capa-bility of the improved ant colony algorithm to optimize hyperparameters tuning towards layers of Bi-LSTM network,number of neurons,batch size,and the number of training.Experiments are carried out on two public data sets of daily traffic flow in British Motorway and Bao'an District published by Shenzhen Govern-ment Open Platform,with RMSE and MAE being as evaluation indexes.The results show that DACO-Bi-LSTM model has strong optimization ability and better prediction performance,and shows better prediction performance.
关键词
交通流量预测/蚁群算法优化/双向长短时记忆网络/模型调参
Key words
traffic flow prediction/improved ant colony algorithm/Bidirectional Long and Short Time Memory network/model hyperparameters tuning
维普
万方数据