Railway track is a linearly inhomogeneous object that consists of geometrical and elastic discontinuities such as bridges, transition zones, rail joints and crossings. The zones are subjected to the development of local instabilities due to quicker deterioration than the other tracks. Until now, there have been no efficient approaches that could fully exclude the problem of accelerated differential settlements in the problem zones. Many structural countermeasures are directed at controlling the sleeper/ballast loading with the help of fastenings/under-sleeper pad elasticities, sleeper forms and additional bending stiffness reinforcements. However, the efficiency of the methods is difficult to compare. The current paper presents a systematic approach in which the loading distribution effect in the rail support by application of two bending reinforcement methods is compared: auxiliary rail and under-sleeper beam. The study considers only the static effects to reach a clear understanding the influence of the main factors. The track equivalent bending stiffness criterion is proposed for comparing reinforcement solutions. The analysis shows that the activation of the bending stiffness of the reinforcement beams depends on the relative ratio of the rail fastenings stiffness and track support stiffness under sleepers (or under the under-sleeper beam). The comparison demonstrates that conventional auxiliary rail reinforcement solutions are ineffective due to their weak bending because of the high elasticity of fastening clips and the main rail fastenings. The share of an auxiliary rail is maximally 20% in the track bending stiffness and cannot be significantly improved by additional rails. The under-sleeper beam-based reinforcement solutions show noticeably higher efficiency. The highest effect can be achieved by the activation of the horizontal shear interaction between the under-sleeper beam and the rail. The additional track bending stiffness of the under-sleeper-based solutions is about 3.5 times more of the rail one and could be potentially increased to 6–10 times.

With development of the heavy-haul railway, the increased axle load and traction weight bring a significant challenge for the service performance and safety maintenance of the railway track. Conducting defect recognition on concrete sleepers and ballast using big data is vital. This paper focused on the detection of absent sleeper support in a ballasted track with an emphasis on the integration of model-based and data-driven methods. To this end, a mathematical model consisting of the wagon, track and wheel–rail contact subsystems was first established to acquire the necessary raw data for the data-driven method, in which the wagon was regarded as a 47-degree-of-freedom multi-body subsystem, and the track was treated as a multi-layer discrete-elastic support beam subsystem with absent sleeper support. Then, an architectural hierarchy of a three-layer  convolutional neural network (TLCNN) was developed, which includes three convolutional layers and two pooling layers, and a method for reconstructing one-dimensional sleeper vertical displacement to a two-dimensional time–space matrix was also proposed. Thirdly, verification was carried out by comparing the simulation and experimental results to illustrate the accuracy and reliability of the mathematical model, and the dynamic behaviour of the track with absent sleeper support was investigated. Lastly, the established TLCNN was used to train the raw data of the sleeper vertical displacement and detect the existence of absent sleeper support. Results show that the integration of model-based and data-driven methods was a reliable and effective approach for the detection of absent sleeper support. The proposed TLCNN can acquire and extract robust characteristics in a noisy environment. To handle more complex recognition tasks and further improve performance, deeper CNN models and larger sample sizes should be preferentially considered in practical applications.

In the extensive urban rail transit network, interruptions will lead to service delays on the current line and spread to other lines, forcing many passengers to wait, detour, or even give up their trips. This paper proposes an event-driven simulation method to evaluate the impact of interruptions on passenger flow distribution. With this method, passengers are regarded as individual agents who can obtain complete information about the current traffic situation, and the impact of the occurrence, duration, and recovery of interruption events on passengers’ travel decisions is analyzed in detail. Then, two modes are used to assign passenger paths: experience-based pre-trip mode and response-based entrap mode. In the simulation process, the train is regarded as an individual agent with a fixed capacity. With the advance of the simulation clock, the network loading is completed through the interaction of the three agents of passengers, platforms, and trains. Interruption events are considered triggers, affecting other agents by affecting network topology and train schedules. Finally, taking Chongqing Metro as an example, the accuracy and effectiveness of the model are analyzed and verified. And the impact of interruption on passenger flow distribution indicators such as inbound volume, outbound volume, and transfer volume is studied from both the individual and overall dimensions. The results show that this study provides an effective method for calculating the passenger flow distribution of an extensive urban rail transit network in the case of interruption.

After the rapid expansion of the subway system over the past two decades, some cities are preparing to build more suburban railways. The emergence of suburban railways is bound to change the choice of suburban passenger transportation. This paper studies the factors that affect the choice of travel mode at the construction stage of suburban railways, aiming to design a more rational suburban railway network and urban public transport service system. Taking Shanghai as an example, this study first surveyed revealed preference (RP) and stated preference (SP) among urban-suburban travelers. Then, we used discrete choice models (DCM) and machine learning algorithms to build a travel mode choice model based on data collection and analysis. Furthermore, the importance of each factor was analyzed, and the effects were predicted under several traffic demand management schemes. Finally, this study proposed some strategies for increasing the share of public transport. On the one hand, it is suggested that Shanghai should continue to develop suburban railways and maintain low pricing of public transport services. Considering the construction and operation costs, the government needs to provide certain subsidies to stabilize prices. On the other hand, as passengers are very sensitive to the “last mile” trips in their suburban railway travel, transport planners should strengthen the connection from and to the suburban railway stations by developing services such as shared bikes and shuttle buses. In addition, the results indicated that some traffic demand management measures can also contribute to a larger share of public transport.

With the promotion of the major strategy of national transportation power, the super-large-scale metro operation network has become an inevitable trend, and operation safety has become increasingly prominent. Metro operation dispatch logs and accident reports were taken as the research object, the hazard sources were efficiently and accurately identified, the risk chains of hazard sources were mined, and the risk evolution mechanism was revealed. Firstly, the transportation lexicon was constructed to improve the accuracy of word segmentation, the text features were extracted based on the term frequency-inverse document frequency (TF-IDF) algorithm, and the key eigenvalues were mined to identify the key hazard sources. Secondly, pattern matching was used to extract explicit causality, and the Self-Attention BiLSTM extracted implicit causality and integrated event trigger word position identification to enhance the effectiveness of implicit causality extraction. Finally, an example was given to verify the efficiency and accuracy of the model, the experiments showed that the F value of extraction effect was increased by nearly 10%, the extraction accuracy was 87.53%, the identification of operational hazard sources was more accurate, and the visualization of risk evolution law was realized.

We numerically investigate the train-induced transient pressure on the platform screen doors (PSDs) of an island-type platform in a subway station according to real scenarios and full-scale simulation to enhance understanding of the design and safe operation of subway trains. Two typical cases, namely, the non-stop and chasing cases, were examined, with the train’s speed variation well simulated throughout both cases. In the non-stop case, a train passes through the station without stopping; in the chasing case, a train stops at the station while another subsequent train approaches through the tunnel, which inevitably happens during rush hours. It is observed that the train generates a compression wave when it passes the tunnel’s ventilation shaft, similar to what occurs when entering a tunnel. In the non-stop case, the PSDs experience two positive and one negative pressure extremes in the whole process. The first positive peak results from an oncoming compression wave through the tunnel, while the second positive and negative peaks arise due to the passing of the train head and tail, respectively. In the chasing case, the stopped train at the platform leads to a blockage effect that significantly increases the pressure on the PSDs via the oncoming compression wave. This lateral pressure may result in the failure of proper operation of the PSDs, particularly during rush hours, with pressure lasting for roughly 5.7 seconds. It is also found that, compared to the fully sealed PSD, both the half-height and partial porous PSDs can significantly reduce the pressure load caused by the passing of the train head, with its maximum pressure load reduced by 38.7% and 38.2%, respectively. Therefore, if the pressure load on a fully sealed PSD is too high for structural design, one may consider the use of half-height and partial porous PSDs as an alternative. Our study offers crucial insights into the train-induced transient pressure on PSDs in a subway station island-type platform. It provides guidance on optimizing the pressure/operation problem on PSDs and offers valuable information for the safe design and operation of subway trains.