A Forecasting Method for Arrival Passenger Flow Based on Hyperparametric Optimization WOA-Bi-LSTM Model for Passenger Hubs

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 improve 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 determined by searching optimal values. The search procedure is performed to achieve adaptive parameter optimization 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 absolute 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 models 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, indicating that the proposed model has the best fit. The RMSE and MAE are both the lowest, at 762.96 and 556.25, respectively, with errors reduced by at least 5.6% and 3.2% compared to other models.