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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. [ABSTRACT FROM AUTHOR]

Short-time traffic flow prediction can not only help urban traffic management to complete the control and induce but also reduce the degree of urban congestion and improve the efficiency of urban operation. At the same time, improving the effect of short-time traffic flow prediction is one of the key points of traffic control and guidance development. Currently, there are many different models for short-term traffic flow prediction, and different prediction models have their different advantages and disadvantages. In this paper, we review the current status of research on short-time traffic flow prediction based on Long Short-Term Memory (LSTM) neural network: first, we analyze the research method in this paper; second, we briefly review the research and application effects of common prediction methods in traffic flow prediction; finally, the optimization method based on LSTM and its application effect are summarized and analyzed from the aspect of LSTM network and its combination model. In addition, short-time traffic flow prediction still faces challenges due to the problems of the data itself and the complexity of real-world traffic flow impact factors. [ABSTRACT FROM AUTHOR]

As an important part of modern comprehensive transportation system, high-speed railway (HSR), whether it is a transregional trunk line or an intercity line, always shapes the industrial layout and changes the living environment at different levels. Based on the analysis of the railway network topology and organization process of passenger train, this paper clarifies the connotation of passenger train operation scheme of HSR considering network survivability, and further explains the connotation of railway network system and its relationship with passenger train operation scheme. In this paper, a comprehensive quantitative index of the importance of key nodes and key edges is established, and an evaluation index of the survivability of railway service network based on the operating mileage of passenger trains is constructed, which is applied to the real local HSR network in China. With the aim of improving the survivability of HSR system, an alternative operation route of passenger trains between some OD stations is given according to the specific railway interruption situation., Author(s): Yong Xian [1]; Yinzhen Li (corresponding author) [1]; Haijun Li [1]; Changxi Ma [1]; Tianxiang Zhang [2]; Yongpeng Zhao [1] 1. Introduction Railway plays an important role in economic [...]

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Predicting rail transit passenger flow is crucial for modifying the metro schedule. To increase prediction accuracy, a model is proposed that combines long short-term memory (LSTM) with single spectrum analysis (SSA). Firstly, a stepwise decomposition sampling (SDS) strategy based on SSA progressive decomposition is proposed as a solution to the data leaking issue in traditional sequence decomposition. Then, based on this strategy, the passenger flow time series with complex features is decomposed into a relatively single trend and fluctuation component. Finally, the LSTM network is employed to perform short-term predictions on each component separately. The predicted value of each component is accumulated to obtain the original passenger flow' predicted result. The example shows that, compared with the single LSTM and other hybrid models, the proposed method offers a greater overall prediction accuracy in the experimental days, and the method has specific applicability., Author(s): Haijun Li (corresponding author) [1]; Yongpeng Zhao [1]; Changxi Ma [1]; Ke Wang [1]; Xiaoting Huang [1]; Wentao Zhang [2] 1. Introduction Subway system has the advantages of large [...]

Tourist-dedicated train is the product of the combination of railway transportation and tourism, and its tour line selection includes the selection of nodes and lines. Based on the principle of decision-making, taking technical factors, tourism factors, regional economic factors, and passenger flow factors as criteria, this paper analyzes the decision-making indices affecting the tour line and establishes the decision-making index system. Subsequently, the prospect theory considering the bounded rationality and psychological factors of decision makers is combined with vague set fuzzy decision theory, and a comprehensive decision method based on vague set and prospect theory is proposed. Finally, the feasibility of the proposed decision method is verified by an example. The research shows that the established decision-making index system is representative, and the proposed decision-making method is scientific and effective for the decision making of tourist lines of tourist-dedicated train. The decision-making results can be used as a reference for the formulation of tour lines and line plans of tourist-dedicated train., Author(s): Yuanding Cui (corresponding author) [1]; Changxi Ma [1] 1. Introduction Tourist-dedicated train is a new tourism product developed by railway transport enterprises for the tourism market. It is the [...]

A new macroscopic traffic flow lattice model is established which considered the taillight effect and velocity deviation. By combining the concept of critical density, the critical condition of taillight triggering is established. The situation of taillight appearing is fully considered, and the influence of taillight effect is analyzed. Through the stability analysis of the model, the stability conditions of the model are obtained. By nonlinear analysis, the mKdV equation is derived which can describe the evolution mechanism of the density wave. From the phase diagram, we can find that the two factors have a positive impact on the stability of traffic flow. Finally, spatiotemporal evolution of the density wave and cross-section view of the density wave are obtained by numerical simulation to verify the theoretical derivation of this paper. The simulation results show that the stability of traffic flow can be improved by considering taillight effect and speed deviation. In addition, by comparing the time-space evolution maps of different parameter values, it can be found that the stability of traffic flow considering the taillight effect and speed deviation is better than that considering one factor alone. However, the stability of traffic flow will be negatively affected by low speed estimate and high critical density., Author(s): Zhicheng Li [1]; Changxi Ma (corresponding author) [2] 1. Introduction With the development of urbanization and modern science and technology, traffic problems have become the most serious social problem [...]