交通信息与安全2023，Vol.41Issue(5)：148-157,10.DOI:10.3963/j.jssn.1674-4861.2023.05.015
基于超参数优化WOA-Bi-LSTM模型的客运枢纽抵站客流预测方法
A Forecasting Method for Arrival Passenger Flow Based on Hyperparametric Optimization WOA-Bi-LSTM Model for Passenger Hubs
摘要
Abstract
Accurate prediction of arrival passenger flows at external passenger transportation hubs is an important prerequisite for enhancing the scientific scheduling of the transferring transport capacity of hubs.In order to im-prove the prediction accuracy of arrival passenger flows,a combination model of the whale optimization algorithm and bi-directional long short-term memory(WOA-Bi-LSTM)is proposed.Integration of historical arrival passenger flow data with multi-source information such as weather,date,and time of day,the time-varying characteristics of arrival passenger flows are analyzed,and correlation analysis is conducted between different influencing factors and arrival passenger flows at the hub.The parameter setting of the traditional bi-directional long short-term memory(Bi-LSTM)model is modified with the whale optimization algorithm(WOA)optimization algorithm.Learning rate(η)and the number of hidden neurons(H)are significant hyperparameters on model prediction accuracy and are de-termined by searching optimal values.The search procedure is performed to achieve adaptive parameter optimiza-tion by calculating their fitness functions through iterative logic.Through continuous optimization,set the η as 0.060 3 and H as 120.The performance of the proposed model is evaluated using three indicators:R2 value,mean ab-solute error(MAE),and root mean square error(RMSE).Simultaneously,the WOA-Bi-LSTM model is compared with several baseline models across multiple dimensions based on the same dataset,including three Bi-LSTM mod-els modified by different hyperparameter optimization algorithms,two other combination models based on the WOA algorithm and two unmodified neural network models.The results show that the WOA-Bi-LSTM model shows better performance of predicting arrival passenger flows in different scenarios involving holiday,workday and non-workday.Compared to other models,the WOA-Bi-LSTM model achieves the highest R2 of 0.951 4,indicat-ing that the proposed model has the best fit.The RMSE and MAE are both the lowest,at 762.96 and 556.25,respec-tively,with errors reduced by at least 5.6%and 3.2%compared to other models.关键词
综合运输/对外客运枢纽/抵站客流预测/双向长短时记忆神经网络/超参数优化Key words
integrated transportation/external passenger hub/arrival passenger flow prediction/bi-directional long-short term memory neural network/hyperparameter optimization分类
交通工程引用本文复制引用
翁剑成,陈旭蕊,潘晓芳,孙宇星,柴娇龙..基于超参数优化WOA-Bi-LSTM模型的客运枢纽抵站客流预测方法[J].交通信息与安全,2023,41(5):148-157,10.基金项目
国家自然科学基金项目(52072011)、北京市博士后工作经费资助项目(2022-ZZ-087)资助 （52072011）
