China Railway Express (CR Express) is an emerging freight transport mode between China and Europe, which provides a new option in addition to air transport and ocean shipping. At the initial stage of development, due to the higher freight costs compared to ocean shipping, the government subsidy provided for the China Railway Express operator plays an important role in the process of market cultivation. The original intention of the government subsidy was to promote the rapid and sustainable development of China Railway Express and the further subsidy scheme should encourage China Railway Express operators to act towards the expected goal of the local government. This research is devoted to modeling and solving the problem of optimal subsidy amount in the operation of China Railway Express. Firstly, a subsidy model is established with the consideration of different objectives of local government and China Railway Express operator, which are affected by the operational effort level of the China Railway Express operator, actual freight rate and the highest freight rate that the shipper is willing to pay. Then, the validity of the proposed model is analyzed in the case study of the China Railway Express line (Wuhan–Hamburg). The effects of different subsidy amounts on the social benefits of local government and profits of the China Railway Express operator are analyzed and the sensitivity analysis is carried on the operational effort level of China Railway Express operator and the highest freight rate that shipper is willing to pay. The analysis results show that the optimal subsidy amount is between 2000 and 2500 USD per Forty-Foot Equivalent Unit (FEU). Finally, suggestions are proposed according to the analysis results. These findings can provide useful references for promoting the sustainable development of China Railway Express.

The construction of high-speed rail lines in China has drastically improved the freight capacity of conventional railways. However, due to recent national energy policy adjustments, rail freight volumes, consisting mostly of coal, ore, and other minerals, have declined. As a result, the corresponding changes in the supply and demand of goods and transportation have led to a gradual transformation of the railway freight market from a seller’s market to a buyer’s market. It is important to carry out a systematic analysis and a precise forecast of the demand for rail freight transport. However, traditional time series forecasting models often lack precision during drastic fluctuations in demand, while deep learning-based forecasting models may lack interpretability. This study combines grey relational analysis (GRA) and deep neural networks (DNN) to offer a more interpretable approach to predicting rail freight demand. GRA is used to obtain explanatory variables associated with railway freight demand, which improves the intelligibility of the DNN prediction. However, the high-dimension predictor variable can make training on DNN challenging. Inspired by deep autoencoders (DAE), we add a layer of an encoder to the GRA-DNN model to compress and aggregate the high-dimension input. Case studies conducted on Chinese railway freight from 2000 to 2018 show that the proven GRA-DAE-NN model is precise and easy to interpret. Comparative experiments with conventional prediction models ARIMA, SVR, FC-LSTM, DNN, FNN, and GRNN further validate the performance of the GRA-DAE-NN model. The prediction accuracy of the GRA-DAE-NN model is 97.79%, higher than that of other models. Among the main explanatory variables, coal, oil, grain production, railway locomotives, and vehicles have a significant impact on the railway freight demand trend. The ablation experiment verified that GRA has a significant effect on the selection of explanatory variables and on improving the accuracy of predictions. The method proposed in this study not only accurately predicts railway freight demand but also helps railway transportation companies to better understand the key factors influencing demand changes.

Accurately predicting railway passenger demand is conducive for managers to quickly adjust strategies. It is time‐consuming and expensive to collect large‐scale traffic data. With the digitization of railway tickets, a large amount of user data has been accumulated. We propose a method to predict railway passenger demand using web search terms data. In order to improve the prediction accuracy, we improved Wasserstein Generative Adversarial Nets (WGAN), which were good at generating and identifying data, by adding a predictor and supervised learning adversarial training to predict railway passenger demand. The improved WGAN could generate virtual data to expand real data, and use parallel data to predict railway passenger demand. We used search times of web search terms on different devices as training data to predict railway passenger demand in Beijing. The results show that the change in demand for railway passenger lags behind the change in the data of web search terms by one month. It is suitable for forecasting in advance. Compared with other forecasting methods, the improved WGAN performance is better, and the mean absolute percentage error is 1.98%. Because it can use mixed data for training and prediction, it has stronger adaptability when data scale decreases.

With the refinement of the urban transportation network, more and more passengers choose the combined mode. To provide better inter-trip services, it is necessary to integrate and forecast the passenger flow of multi-level rail transit network to improve the connectivity of different transport modes. The difficulty of multi-level rail transit passenger flow prediction lies in the complexity of the spatiotemporal characteristics of the data, the different characteristics of passenger flow composition, and the difficulty of research. At present, most of the research focuses on one mode of transportation or the passenger flow within the city, while the comprehensive analysis of passenger flow under various modes of transportation is less. This study takes the key nodes of the multi-level rail transit railway hub as the research object, establishes a multi-task learning model, and forecasts the short-term passenger flow of rail transit by combining the trunk railway, intercity rail transit and subway. Different from the existing research, the model introduces convolution layer and multi-head attention mechanism to improve and optimize the Transformer multi-task learning framework, trains and processes the data of trunk railway, intercity railway, and subway as different tasks, and considers the correlation of passenger flow of trunk railway, intercity railway, and subway in the prediction. At the same time, a new residual network structure is introduced to solve the problems of over-fitting, gradient disappearance, and gradient explosion in the training process. Taking the large comprehensive transportation hub in Guangzhou metropolitan area as an example, the proposed multi-task learning model is evaluated. The improved Transformer has the highest prediction accuracy (Average prediction accuracy of passenger flow of three traffic modes) 88.569%, and others methods HA, FC-LSTM and STGCN are 81.579%, 82.230% and 81.761%, respectively. The results show that the proposed multi-task learning model has better prediction performance than the existing models.

In the context of the Belt and Road Initiative, China Railway Express achieved rapid development. Determining how to achieve effective marking and determining how to adopt the optimal competitive strategy are the main challenges for CR Express operators. By choosing long-distance transportation as the research object, this research established a competitive model between CR Express and maritime transportation based on game theory. First, we determined the participants of this competition. By dividing common goods into the categories of high-value and high-time-sensitiveness, high-value and low-time-sensitiveness, low-value and high-time-sensitiveness, and low-value and low-time-sensitiveness, the model was used to simulate four situations and to obtain optimal strategies for operators. For CR Express, it was always effective to adopt a service optimizing strategy to improve the service level and earn market share. For maritime transportation, this strategy was effective only for high-value and low-time-sensitiveness goods as well as low value and low-time-sensitiveness goods. Therefore, transportation service suppliers should make competitive strategies based on different good types, and it is effective to use differentiation strategies to earn market share and improve competitiveness only for suitable types of goods.