Urban Rail Transit最新文献

By integrating land use and transportation systems, transit-oriented development (TOD) focuses on transit and land development, development potential, and the balance between transportation and land use. The TOD level assessment not only helps optimize existing TODs but also guides TOD planning. Based on previous literature, methods for assessing TOD around rail transit stations can be roughly divided into three categories: TOD index, the node-place model, and the data envelopment analysis (DEA) model. The TOD index aggregates indicators from different dimensions into a single value for evaluation. The node-place model emphasizes transportation, land use, and the balance between them, as well as expansion factors. And the DEA model is suitable for situations without input indicator weights or consideration of different units of measurement. TOD indicators are the basis of the evaluation, and in both the TOD index and node-place models, indicator weights are mostly determined by using subjective methods. Furthermore, a limited number of studies to date have pre-evaluated the implementation of TOD at rail stations under construction or in planning. This paper aims to assess the level of TOD areas at the rail transit station level, which can help in constructing the evaluation index system, selecting TOD stations, and optimizing them.

As a means of vibration reduction, the ladder track has seen broad implementation in urban rail transit. However, issues such as increased vibration noise, rail corrugation, and fastener failure have been observed in certain sections of the ladder track during later operation. To investigate the mechanisms behind these phenomena and provide comprehensive insights into the system's response to various operational conditions, this study employed vehicle-track coupled dynamic theory to establish a three-dimensional finite element model of the flexible vehicle-ladder track system. The vibration transmission and dynamic response characteristics of the vehicle-ladder track system were analyzed. The findings revealed that the vehicle-track resonance and anti-resonance phenomena were more prominent in the medium- to low-frequency range. At specific frequencies, the wheelset exhibited various vibration modes, and the fastener force was found to closely correlate with the ladder vibration mode. Furthermore, the influence of speed on diverse components of the vehicle-ladder track system, in terms of maximum vibration and the dominant vibration frequency range, differed considerably. This study provides a more comprehensive and reasonable exploration of the modeling and dynamic behavior of vehicle-ladder track systems.

The monitoring of an IRJ would allow targeted maintenance to be carried out, reducing the problems caused by its potential failures. The authors present the results of a test field investigation, that involved the installation of seven longitudinal displacement sensors that continuously record the gap value of insulated rail joints (IRJs). The monitoring of an IRJ would allow targeted maintenance to be carried out, reducing the problems caused by its potential failures. The studied monitoring system was installed in a station of the suburban railway line within the metropolitan city of Bologna (Italy). Analysis of low-and high-frequency recordings was performed. In particular, low-frequency acquisition was used to fit a statistical predictive model that detects a deviation from a standard behaviour and may evidence anomalies. For the high-frequency acquisitions (registered during train passage) some representative quantities, that can provide macroscopic indicators of loss of joint stiffness, were computed. Although gap measurement alone is not exhaustive for identifying all possible failure scenarios, the data acquired by these monitoring devices can represent a possible immediate monitoring solution, based on already available instrumentation, to provide user-friendly predictive analysis systems aiming at improving the railway maintenance.

Railroad vehicles require the use of disc brakes for safety purposes, however, the brakes are susceptible to thermal stress, which ultimately shortens their lifespan. Hence, to accurately predict the life of railway disc brakes in thermal load simulations, the availability of a model that considers spatial and temporal variations of temperature and thermal stress is essential. A non-axisymmetric moving heat source model was successfully developed to address spatial temperature variations (Deressa and Ambie in Urban Rail Transit 8(3–4):198–216, 2022. 10.1007/s40864-022-00176-9), and this study aims to extend this model to predict thermal stress and fatigue life, and assess its effectiveness. The analysis includes braking time thermal analysis, cooling time thermal analysis, and structural analysis. Spatially varying temperature is incorporated into the structural analysis to calculate thermal stress and strain. A fracture mechanics-based fatigue life estimation method is applied to critical areas of the friction surface. The model is implemented on two braking conditions (service and emergency) and two disc geometries (actual and modified). The model successfully resolves spatial heat considerations by estimating maximum stress variations of up to 46 MPa along the disc circumference. Stress differences of 3 MPa and 6 MPa are observed between the leading and trailing edges of the pad trace during late and mid-braking times, respectively. Fatigue life results identify critical positions and directions for fatigue life initiation. Additionally, these results are in accord with previous observations available in the literature. The proposed model can be easily implemented in various sliding friction applications such as drum brakes, engine pistons/cylinders, and camshafts.

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.

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.