Rapid urban expansion and the development of urban rail transit networks have led to a deteriorating vibration environment along metro lines. These long-term vibrations pose significant challenges to adjacent buildings, such as opera theatres, and to the well-being of nearby residents. Consequently, there is a critical need for vibration evaluation and the implementation of mitigation solutions. This work provides a numerical investigation into the dynamics of vibrations observed in an opera theatre located above a metro station. A unified coupling method, known as the train-track-station-solum-opera model, is proposed and validated with field experiments. By employing contact theory, deformation coordination criteria, and spring elements, various components are meticulously modeled and coupled. Using this unified coupled approach, metro-induced vibrations at the opera theatre are predicted and evaluated. Additionally, vibration control measures are employed from the perspectives of transfer paths and vibration receivers to mitigate and isolate excessive theatre vibrations. The results, based on a case where the distance between the metro line and the opera theatre is 42 m, demonstrate that metro operations can lead to vibrations exceeding acceptable limits at the opera theatre near the metro station. Therefore, it is imperative that vibrations are assessed before constructing vibration-sensitive buildings along metro lines and that mitigation measures are implemented to meet specifications. In this work, the application of extruded polystyrene (XPS) plates and optimization of building structures effectively reduced excessive theatre vibrations by 1–2.5 dB, offering viable attenuation options without requiring modifications to the existing metro system.

The issues of housing and traffic in China's mega cities have become increasingly pressing problems, particularly for middle/low-income tenant workers. These tenants are from less advantaged socioeconomic backgrounds, which has resulted in a significant geographical separation between their workplace and their residence. Although a large number of studies have confirmed that built environment factors have a solid impact on residents’ commuting distance, few studies have investigated the mechanism underlying the nonlinear influence on middle/low-income tenants. This paper aims to provide an in-depth analysis of the key factors and nonlinear influencing mechanism of the built environment on middle/low-income tenant workers’ commuting distance by establishing a gradient-boosting decision tree model, using Beijing as an empirical case. The paper reveals three primary findings: (1) An important nonlinear relationship between the surrounding built environment and peoples’ jobs–housing spatial proximity can be observed for those middle/low-income tenant workers who use slow and public modes of commuting. Specifically, the density of public transport stations, road networks, and workplaces, and the land use mix play a dominant role. (2) A limited effect of built environment factors can be found for the same group of tenant workers who choose cars as their mode of commuting. (3) The differences in self-selected commuting modes have a significant mediating effect on the relationship between the built environment and jobs–housing situation among middle/low-income tenant workers. Given this, effective policy guidance for residents’ travel modes is necessary to optimize the built environment indicators to achieve the best effect. In addition, we should consider giving priority to the matching indicators such as land use mix and resident population density. Another possibility is to strengthen the connection to the public transport stations, which in turn can optimize the walkability in residential environments.

Urban railways in coastal areas are exposed to the risk of extreme weather conditions. A cost-effective and robust wind monitoring system, as a vital part of the railway infrastructure, is essential for ensuring safety and efficiency. However, insufficient sensors along urban rail lines may result in failure to detect local strong winds, thus impacting urban rail safety and operational efficiency. This paper proposes a hybrid method based on historical wind speed data analysis to optimize wind monitoring system deployment. The proposed methodology integrates warning similarity and trend similarity with a linear combination and develops a constrained quadratic programming model to determine the combined weights. The methodology is demonstrated and verified based on a real-world case of an urban rail line. The results show that the proposed method outperforms the single similarity-based method and spatial interpolation approach in terms of both evaluation accuracy and robustness. This study provides a practical data-driven tool for urban rail operators to optimize their wind sensor networks with limited data and resources. It can contribute significantly to enhancing railway system operational efficiency and reducing the hazards on rail infrastructures and facilities under strong wind conditions. Additionally, the novel methodology and evaluation framework can be efficiently applied to the monitoring of other extreme weather conditions, further enhancing urban rail safety.

Short-term passenger flow prediction (STPFP) helps ease traffic congestion and optimize the allocation of rail transit resources. However, the nonlinear and nonstationary nature of passenger flow time series challenges STPFP. To address this issue, a hybrid model based on time series decomposition and reinforcement learning ensemble strategies is proposed. Firstly, the improved arithmetic optimization algorithm is constructed by adding sine chaotic mapping, a new dynamic boundary strategy, and adaptive T distribution mutations for optimizing variational mode decomposition (VMD) parameters. Then, the original passenger flow data containing nonlinear and nonstationary irregular changes of noise is decomposed into several intrinsic mode functions (IMFs) by using the optimized VMD technology, which reduces the time-varying complexity of passenger flow time series and improves predictability. Meanwhile, the IMFs are divided into different frequency series by fluctuation-based dispersion entropy, and diverse models are utilized to predict different frequency series. Finally, to avoid the cumulative error caused by the direct superposition of each IMF’s prediction result, reinforcement learning is adopted to ensemble the multiple models to acquire the multistep passenger flow prediction result. Experiments on four subway station passenger flow datasets proved that the prediction performance of the proposed method was better than all benchmark models. The excellent prediction effect of the proposed model has important guiding significance for evaluating the operation status of urban rail transit systems and improving the level of passenger service.

To alleviate peak-hour congestion in urban rail transit, this study proposes a new off-peak fare discount strategy to incentivize passengers to shift their departure time from peak to off-peak hours. Firstly, a questionnaire survey of Shanghai metro passengers is conducted to analyze their willingness to change departure time under different fare strategies. Secondly, based on the survey results, a time-differentiated fare discount model is constructed, considering both the company’s revenue and passengers’ travel benefits, and with the optimization objective of achieving balanced peak-hour and off-peak-hour train loads throughout the day. Subsequently, a genetic algorithm with nested fmincon functions is designed and combined with the actual data of Shanghai rail transit line 9 for arithmetic analysis. Finally, the effectiveness of the model is validated using the survey data. The research results show that the off-peak fare discount strategy can incentivize 6.88% of passengers traveling in the morning peak and 6.66% of passengers traveling in the evening peak to shift to off-peak travel. This research provides theoretical support and decision-making guidance for implementing time-differentiated pricing in urban rail transit systems.

The emergence of dockless shared bikes (DSB) has led to their use as an important transfer mode to urban rail transit (URT) stations. However, in highly populated areas such as subway stations in peak hours, there is increasing concern about the imbalance between the demand and supply of shared bikes. To promote smoother subway transfer trips using shared bikes, it is very important to estimate the DSB demand, especially the disparity in the volume of bike pick-up and drop-off demand around subway stations. This research first utilizes the Shenzhen metro usage data and DSB usage data, analyzes data regarding subway and shared bike usage, discusses their potential transfer uses, and finds great disparity in DSB demand between different subway stations. The catchment area method is used to estimate bike usage as a potential transfer mode to the subway, where the catchment area is defined as a radius of 150 m from the subway station center. The DSB trip demand is categorized into two types: pick-up and drop-off. The most recent deep learning method, adaptive graph convolutional recurrent network (AGCRN), is used to predict the DSB demand more accurately because of its ability in enabling the modeling of relationships between entities in a self-adapted graph, and the prediction is compared with long short-term memory (LSTM), spatiotemporal neural network (STNN), diffusion convolutional recurrent neural network (DCRNN), and Graph WaveNet. Results show that methods with graphs (STNN, DCRNN, Graph WaveNet, and AGCRN) perform better than LSTM, and methods with adaptive graphs (Graph WaveNet and AGCRN) outperform methods with static graphs in terms of mean absolute error (MAE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE). DSB prediction results show that AGCRN performs the best in this study. More data, particularly land use data and URT station volume data, are expected to improve the predictive accuracy of the method due to potentially improved graph representation of station characteristics and subway station volume correlations. And with more accurate prediction results, it will be possible to achieve a better balancing strategy for bike operation optimization for better bike usage, and thus for a higher transfer rate of DSB to subway.