## 4 Experiments and Results  
### Experiments Environment and Data Description  
The training method is facilitated by an RTX3080 station running CUDA 10.1. The computer's i9-10900x station CPU has tensor core GPUs covering a 128 GB GPU memory. Python coding was performed on the open-source PyTorch machine learning toolkit. For the experimental study, the passenger transfer data for two weeks (passengers every 30 min, 672 data items (48 times/day \(\times\) 14 days) in one direction for transfer corridors) between Capital Airport and Beijing South Railway Station inside the Beijing-Tianjin-Hebei urban agglomeration are chosen. The core megacity, Beijing, is part of the Beijing-Tianjin-Hebei urban agglomeration, which also consists of two sizable cities (Shijiazhuang and Tianjin) and 110 million permanent residents. It's one of China's most significant urban agglomerations. It is crucial in reducing Beijing's non-capital functions, developing the ties between Beijing and Tianjin and improving the province of Hebei's all-around capacity. Within the Beijing-Tianjin-Hebei urban agglomeration, the transfer passenger flow between Beijing Capital International Airport and Beijing South Railway Station is chosen for analysis in the study. The Beijing Capital International Airport's flight passenger throughput exceeded 100 million, placing it at the top of the country's list in 2018 and 2019.The Beijing South Railway Station is a significant integrated transportation hub that connects high-speed railroads, regular railroads, urban railroads, urban public transportation and other modes of transportation. The locations of Beijing Capital Airport and Beijing South Railway Station on the urban agglomeration are shown in detail in Figure 3, with the former being in the northeast of Beijing and the latter being on the southern side. A transfer corridor between air-rail intermodal hubs is represented in Figure 3 by a blue line with double arrows on it. The original balance of intra-city transportation has been upset by the expansion of air-rail intermodal passenger traffic, which has led to a new need for transit between numerous hubs within the urban agglomeration. In addition to the composite travel chain of various transportation mode combinations that are available for users to pass between hubs, the air-rail intermodal passengers between the Capital Airport and Beijing South Railway Station displaya variety of complex travel behaviors, using the bus, subway and taxis. According to Figure 4, which shows the various access options available to cross-hub passengers in urban multimodal transportation networks, one significant factor is the growth in the number of passengers between hubs and the heterogeneity of individual passenger characteristics and travel attributes. As a result, it is challenging to determine the transfer passenger flow data between the two hubs using operational data from the airport or high-speed railway station. Instead, we may estimate inter-hub transfer passenger traffic utilizing data from cell phone signaling by employing new data sources. Cell phone users can be identified as inter-hub transfer passengers by users who pass through both Beijing Capital Airport and Beijing South Railway Station at the chosen time, because they passively connect to the base station network at least once every 30 min without actively using the network. The Unicom cell phone signaling data from the National Key Research and Development Program 2018 YFB1601300 were chosen for this study, and following data cleaning and sample enlargement, we obtained the data of the interchange passenger flow between Beijing Capital Airport and Beijing South Railway Station from 10-23 May 2021. Every 30 minwas selected as a statistical period to count the number of inter-hub transfer passengers from 10-23 May 2021, taking into account the base station's capacity to capture the users' cell phone signaling data.