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The purpose of this paper is to optimize the accuracy of airport express passenger flow prediction so as to meet the need for the optimal allocation of traffic resources against the background of accelerated urbanization and the rapid development of airport express services. A fusion intelligence network model (FINM) is proposed, which integrates the advantages of convolutional neural networks, bidirectional long short-term memory networks, and gated recurrent units. Firstly, by using the powerful feature extraction ability of convolutional neural networks, local features and detail information are captured from the input data to improve the data representation ability. Secondly, bidirectional long short-term memory networks are used to process the sequence data, capture the global information and its context relationship, and enhance the model’s understanding of the dependence of time series data. Finally, gated recurrent units are introduced to simplify the computational complexity while maintaining high prediction accuracy and training efficiency. Based on the actual passenger flow data for Tianjin Metro Line 2 on a 30 min time scale, the proposed FINM is verified. The experimental results show that the model achieves an excellent performance, with 0.0160, 0.0947, 0.0160, 0.1255, 18.40, and 0.7788 in key indicators such as loss value (Loss Value), mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R-Squared). Compared with the comparison algorithm, this model shows significant advantages in all indicators, which proves its effectiveness in dealing with complex time series data.

To meet the preference demands of the crew members of high-speed railway trains while forming a crew team, and to automate the compilation of adaptable crew member schemes, a bilateral matching decision method for crew members is proposed based on complete preference order information. This method first describes the mutual selection process between the chief stewards and stewards of a high-speed railway train as a one-to-many bilateral matching decision process between the chief stewards and stewards Subsequently, by constructing a virtual train chief stewards, the original one-to-many bilateral matching relationship between the chief stewards and stewards is transformed into a one-to-one bilateral matching relationship between the virtual chief stewards for modeling. Then, a dual-objective integer programming model is established with the minimum sum of preference order values as the objective. Finally, an optimization solver is used to calculate the problem under different scales, and a genetic algorithm is designed for large-scale scenarios. The analysis results of numerical examples show that the model and algorithm of train crew members based on bilateral matching decisions can meet the actual requirements of the crew department and have good application value.

This study aims to elucidate the implications of carbon peak and carbon neutrality targets on corridor logistics, with a focus on the burgeoning high-speed rail express sector. Acknowledging carbon emissions taxation as an integral component of transportation costs, it examines the competitive dynamics among four cargo transport modes: high-speed rail (HSR), conventional rail, aviation, and road transport. Within a strategic game framework, carriers are analyzed based on freight rates, speed, safety, reliability, and convenience to assess their utility. A dynamic game model and a solution approach are developed, aiming to optimize freight-related variables, maximize carriers’ generalized profits, and enhance shippers’ utility. Empirical validation is provided through case studies in Xi’an and Lanzhou, northwest China, affirming the model’s efficacy. The findings reveal the strong competitive edge of the high-speed rail express in the corridor, offering valuable insights for carrier pricing strategies, emission tax rate setting, and macro-policy adjustments in the transport sector.