## 1 Introduction  
In China, the rate of urbanization has increased, which has sped up the movement of people, information and resources across metropolises. Long-distance multimodal travel inside urban agglomerations or between them has increased significantly; this has increased requirements for multimodal transportation systems' operating effectiveness. The study of multimodal transport modes [1] and the sharing rate of each mode in long-distance travel [2] related to transportation networks is a priority, and the study of long-distance travel mode choice, transfer choices and travel costs related to individual choices is another [3, 4, 5]. A major source of concern for policymakers is the forecast of passenger flow between significant cities [6] as well as the demand for travel by air [7] and high-speed railway [8] in megacities. Numerous techniques based on conventional statistics (such as ARIMA [9], gray prediction models [10], etc. ), primarily developed from neural networks (such as ANN [11], CNN [12], and DNN [13]), have been extensively used in past studies to solve prediction issues in the field of traffic. The ability of prediction algorithms to handle vast amounts of data is becoming more and more important as data collecting capabilities advance. More forms of data, such as cab trajectory data, cell phone signaling data, travel application data, etc., can be employed for traffic forecast analysis. Researchers have attempted to use deep learning techniques to promote the application of neural network-type models in massive amounts of traffic data. These techniques include recurrent neural networks [14], long short-term memory networks [15] and gated recurrent units [16] and various hybrid models [17; 18; 19]. These researchers have achieved rich results, including long, medium and short term prediction of traffic station flow [20], transportation mode flow [18; 21] and traffic networks [19] based on massive amounts of traffic data, based on the widely used LSTM, GRU and other algorithms. Lately, the transformer algorithm has been better used in traffic timeseries prediction [22]; it can train the model by removing the spatiotemporal characteristics of traffic data [23], and also helps with the dependence issue within long series data processing [24]. Transfer between different modes of transportation is one of the critical links in the long-distance multimodal travel chain of urban agglomeration, which is frequently impacted by exceptional events, such as severe weather, holiday festival gatherings and unexpected line interruptions. Interchange difficulties may result in travelers missing the next leg of their trip or trip suspension for multimodal travelers. Urban agglomerations' multimodal transportation management and operation organizations must accurately estimate and track real-time transfer passenger volumes inside key transfer routes. The accompanying huge passenger flow response plan and the transfer passenger forecast results used together prior to the occurrence of extreme weather or mega-events can improve the operational effectiveness of the urban cluster multimodal transportation system. For the purpose of developing short-term projections of transfer passengers between integrated hubs within urban agglomerations, the Transformer's classical encoder-decoder framework is taken into consideration in this study. Additionally, LSTM is employed to pre-process passenger flow statistics, addressing the issues with long time series data dependency and extracting passenger flow features for simple encoder recognition. The decoder maps the features to the prediction sequence using the attention method to obtain the future value. The analyzed model's overall structure is shown in Figure 1, which calls for historical data for practical training purposes. It forecasts future traffic passenger tendencies by entering real-time data on transfer passengers between particular hubs.