## 5 Conclusions  
Making forecasts of transfer passenger flow between significant passenger transportation hubs in urban agglomerations is essential to better serve urban agglomerations' integrated multimodal transportation operation. The data obtained are crucial for ensuring an efficient multimodal transportation system. Therefore, based on the conceptual framework and model structure depicted in Figures 1 and 2, this study suggests a transformer model based on LSTM. The suggested approach uses a self-attention mechanism to model the time series data and focuses more on capturing temporal aspects in traffic flow. By contrasting it with other time series forecasting techniques, the capacity of this deep learning system to learn complicated dependencies of different lengths from traffic time series data is increased (such as statistics and traditional neural networks). The consistent length of the input sequence is no longer required, making it easier to extend the application of the prediction method. To test the model performance, we chose the passenger transfer between two significant integrated passenger hubs (the Beijing Capital Airport and Beijing South Railway Station) in the Beijing-Tianjin-Hebei urban agglomeration as the experimental object, and we used cell phone signaling from 10-23 May 2021, as the base data. The time series forecasting analysis uses the extracted and extended data: data from 10-20 May 2021, model training and transfer flow projection from 21-23 May 2021, and actual date comparison. A powerful method for forecasting traffic flow data with temporal features is demonstrated by the LSTM-based transformer model, which obtains advanced results on the actual transfer flow dataset in both directions. In addition to the short-term prediction of inter-hub transfer passenger flow, the method proposed in this study can also be used to predict more multimodal traffic network data in urban agglomerations, such as the prediction of transport volume in important passenger hubs and the prediction of different modes of passenger flow between significant cities. It can also be used to predict passenger flow in inter-hub transfer corridors in different states, especially when there are significant travel periods, such as peak travel seasons, significant holidays or when significant cultural or sporting events are happening in a location. Both multimodal transportation managers in urban agglomerations and users of multimodal transportation networks might benefit from the findings of projecting passenger flows along transfer corridors at particular times. The forecasted outcomes of inter-hub transfer passenger flow, from the manager's perspective, can assist management in planning for high passenger flows, considering expanding the capacity of specific transit modes based on the forecast data or adjusting the frequency of inter-hub rail operations to ensure that the hub can easily handle the influx of passengers during special occasions. Once the management combines the forecast results to optimize the capacity adjustment of the transfer corridors, it will be very beneficial for traveling passengers to ensure that long-distance multimodal travel chain users are in the process of travel mode switching smoothly and quickly to avoid travel disruption, cancellation or long delays caused by extraordinary events. Last but not least, the simplified format required for the input sequence and the applicability of the approach to the prediction of nodes or channels within the traffic network demonstrate the progressive nature of the work of the transformer model proposed in the study. However, the outcomes of the transformer run in the experimental instance are not indisputably better than those of the LSTM and GRU models. In [PERSON]'s research [24] about the traffic flow forecasting method, the MAPE of the LSTM model achieved 12.37%, the MAPE of the GRU model reached 12.66% and the best forecast outcome's MAPE in this study was 21.33%.