Search

Urban rail transit is an integral part of public transit, and has been extensive built in China. Previous studies have proved that the spatial features are closely related to rail transit ridership, considering a fundamental role of short-term passenger flow forecast in the urban rail operation, it is meaningful to explore how these factors affect the prediction accuracy. This study aims to find a way to improve prediction accuracy by considering spatial features of stations based on deep learning. Therefore, a CNN-LSTM model capturing the spatial and temporal features was applied and Suzhou (China) was choosing as a case study to explore the influence of three spatial features, namely relative position, location, and land use, on the prediction accuracy. The predict model used can extract spatiotemporal features and accurately predict the citywide stations, and the results show that, for the relative position, the inbound and outbound flow prediction errors of transfer stations and middle stations are the lowest, respectively. As for locational features, the more distant the station is from the city center, the more accurate the results are. For stations where land use is dominated by work and living services, the predictions are more accurate. The error rate is higher for stations whose services are mainly tourism, transportation, and leisure services. This study's results can help operators predict the short-term passenger flow of target stations based on different demands and optimize their services on this basis. [ABSTRACT FROM AUTHOR]

Providing effective Through Train Services (TTSs) faces challenges due to complex infrastructure conditions, train performances and passenger demands. To enhance TTSs between two different classes of urban rail transit lines with variations in train speed and capacity, we propose a multi-objective Integer Non-Linear Programming (INLP) model. This model maximizes passenger travel time savings and average train load utilization, and develops an integrated approach to simultaneously optimize the frequencies of through express trains and local trains, as well as the operation zones, stopping patterns and type selection of through trains. Additionally, a Non-Dominated Sorting Genetic Algorithm II is designed to solve the INLP model based on a simple test network and a real-world case from the Nanjing Subway. The unique benefits of our proposed method are demonstrated by a comprehensive compared with the Single Line Operation Mode and the all-stop plans under Through Operation Mode. [ABSTRACT FROM AUTHOR]

The rapid expansion of urban rail transit necessitates accurate short-term passenger flow forecasts (STPFF) to optimize operation plans and enhance service quality. However, the existing STPFF methods do not fully consider the effect of built environment on passenger flow. In this regard, a convolutional long short-term memory neural network (CNN-LSTM) model incorporating built environment indicators has been proposed for accurately short-term passenger flow predictions. First, a system of built environment indicators (including 11 indicators), anchored in the 5Ds framework, is introduced to depict the characteristics of the built environments surrounding rail transit stations. Then, the random forest model (RF) is utilized to measure and rank the indicator importance. Finally, using historical passenger flow and key built environment indicators as input variables, a CNN-LSTM model for short-term passenger flow forecast is built. Taking Beijing city, China as an example for empirical research, the results show that CNN-LSTM model considering built environment can improve the accuracy of STPFF. Utilizing the top four key built environment indicators (the ratio of commercial land area, density of point of interest (POI) categories, and bus station density) as input variables can effectively reduce model computational complexity while concurrently enhancing predictive accuracy. The highest forecasting accuracy of the model is achieved at a time granularity of 5 min. This study can effectively support the operation and management of urban rail transit. [ABSTRACT FROM AUTHOR]

Accurate and rapid short-term passenger flow prediction is the foundation for safe and efficient operation of urban rail transit systems. The urban rail transit passenger flow is related to the surrounding land properties and is accompanied by random interference. However, the passenger flow characteristics of stations and the role played by random disturbances in predicting passenger flow signals are not clear. A hybrid neural network named Urban Rail Transit Short-Term Flow Prediction Neural Network (URTSTFPNN) is proposed to improve the accuracy and efficiency of short-term passenger flow prediction. The network consists of three modules: feature Processing Module, Data Reconstruction Module, and Prediction Module. In this process, the urban rail transit stations are classified by dynamic time warping based on the time series attributes of entry and exit passenger flow. The noise reduction technology of wavelet transform is added to the network to increase the accuracy of the model. The analysis results of the proposed model using data from Metro Line 2 in Xi'an, Shaanxi Province, China, indicate that urban rail transit stations can be divided into commercial and official stations, high-density residential stations, low-density residential stations, and tourist and passenger transport terminal stations. The URTSTFPNN shows higher predictive accuracy in revealing the errors compared to the single Long Short-Term Memory model, the Auto-Regression and Moving Average model, and the BP neural network model. The coefficient of determination increased by 1.91 ~ 3.48%, 6.45 ~ 9.45%, and 2.72 ~ 5.69%, with a reduction of calculation time by 19.57 ~ 33.29%, 0.88 ~ 11.61%, and 27.87 ~ 36.71%, respectively. The model proposed by this research can accurately and quickly predict passenger flow, which can be used to guide various categories of urban rail stations to develop effective passenger flow management measures. [ABSTRACT FROM AUTHOR]

Accurately predicting origin‐destination (OD) passenger flows serves as the basis for implementing efficient plans, including line planning and timetabling. However, due to the complexity and variety of OD passenger flows types, general prediction models have difficulty in capturing the features of different OD passenger flows, which in turn leads to poor prediction performance. To address this issue, we propose an integrated framework that combines clustering and prediction methods. First, an unsupervised deep learning model is devised to automatically cluster OD flow types by capturing shape characteristics. Second, three types of features are created to enhance training efficiency, including static features, time‐dependent observed features, and time‐dependent known features. Based on the clustering of OD passenger flow, a weighted adaptive passenger flow prediction model is developed. The study employs a temporal fusion transformers model to enable multitype OD passenger flow prediction. In the numerical experiments, the model was applied to the urban rail transit in South China, and the model clustered 15,168 OD pairs into 4 types for prediction. The findings show that this approach enhanced the prediction accuracy by 2.0%–9.6% compared to the LSTM model and by 1.6%–4.3% compared to the Graph WaveNet. Moreover, the model can accurately assess the various features for diverse types of OD flows. [ABSTRACT FROM AUTHOR]

Short-term origin–destination (OD) passenger flow forecasting is crucial for urban rail transit enterprises aiming to optimise transportation products and increase operating income. As there are large-scale OD pairs in an urban rail transit system, OD passenger flow cannot be obtained in real time (temporal hysteresis). Additionally, the distribution characteristics are also complex. Previous studies mainly focus on passenger flow prediction at metro stations, while few methods solve the OD passenger flow prediction problems of an urban rail transit system. In view of this, we propose a novel deep learning method fusing high-dimensional features (HDF-DL) with multi-source data. The HDF-DL method is combined with three modules. The temporal module incorporates the time-varying, trend, and cyclic characteristics of OD passenger flow, while the latest OD passenger flow time sequence (within 1 h) is excluded from the time-varying characteristics. In the spatial module, the K-means and K-shape algorithms are used to classify OD pairs from multiple perspectives and capture the spatial features, reducing the difficulty of OD passenger flow predictions with large-scale and complex characteristics. Weather factors are considered in the external feature module. The HDF-DL method is tested on a large-scale metro system in China, in which eight baseline models are designed. The results show that the HDF-DL method achieves high prediction accuracy across multiple time granularities, with a mean absolute percentage error of about 10%. OD passenger flow in every departure time interval can be predicted with high and stable accuracy, effectively capturing temporal characteristics. The modular design of HDF-DL, which fuses high-dimensional features and employs appropriate neural networks for different data types, significantly reduces prediction errors and outperforms baseline models. [ABSTRACT FROM AUTHOR]

Assessing the importance of nodes in urban rail transit systems helps enhance their ability to respond to emergencies and improve reliability in view of the fact that most of the existing methods for evaluating the importance of rail transit nodes ignore the disturbance effect of signaling system failures and are unable to objectively identify critical stations in specific disturbance scenarios. Therefore, this paper proposed a method for evaluating the importance of urban rail transit nodes in signaling system failure scenarios. The method was based on the research background of the signaling system failure that occurs most frequently and analyzed the network failure mechanism after the occurrence of a disturbance. The node importance evaluation indices were selected from the network topology and network operation performance in two aspects. The variation coefficient–VIKOR method was employed to comprehensively assess the significance of urban rail transit stations during signaling system failures. The Nanjing Metro network was also used as an example to evaluate the importance of network stations. The results showed that under the attack method of signaling system failure, most ECC and interlocking stations experienced significantly higher network performance losses compared to the original attack method, and a few interchange stations showed smaller performance losses. The critical stations identified based on the proposed method are mainly distributed in the passenger flow backbone of the Nanjing Metro and were constructed in the early stage; of these, 85% are ECC stations or interlocking stations, which are easily neglected in daily management, in contrast to interchange stations with heavy passenger flow. The results of this study can provide an important reference for the stable operation and sustainable construction of urban rail transit. [ABSTRACT FROM AUTHOR]

The vibration and transmission characteristics of track components during vehicle operation is a complex issue. To address this issue, this paper proposes a flexible wheelset-damped track-tunnel-soil model that considers the interaction between the flexible wheelset and the rail. Using field measurements and the developed model, this study investigates the vibration characteristics of track components and explores the influence of fastener stiffness and damping pad stiffness on track vibration and vibration transfer characteristics between components from an energy perspective, using track spectrum power flow as the evaluation index. The results demonstrate that the model considering wheel-rail coupling can more comprehensively reflect the vibration characteristics of the track during train operation. Compared to fasteners with lower stiffness, damping pads with lower stiffness can reduce track vibration in a wider frequency range. Lower fastener stiffness reduces the transmission of vibration in 50∼2000 Hz, while lower damping pad stiffness reduces the transmission of vibrations in 1∼2000 Hz. [ABSTRACT FROM AUTHOR]

In China, the total energy consumption and greenhouse gas (GHG) emissions will reach considerable levels based on the current speed of urban rail transit system development. Based on the life-cycle assessment theory, this research constructs an urban rail transit system GHG emission assessment method, calculates emission outputs based on resource inputs from actual investigated data and makes a quantitative analysis of GHG emissions. The results show that in recent years, the GHG emission of urban rail transit construction and operation in China is between 2000 × 104 and 4200 × 104 tonnes of carbon dioxide equivalent (tCO2e) per year. The proportions of the construction and operational phases in this emission are 57 and 43%, respectively. In the construction phase, the GHG emission intensity per unit mileage of shield tunnels and per unit area of stations is about 1.3 × 104 tCO2e/km and 3.71 × 104 tCO2e/ha, respectively. In the operational phase, the GHG emission intensity per unit trip is 0.084 kg carbon dioxide equivalent/passenger-km. The entire life-cycle GHG emission per kilometre of urban rail transit systems is 11.69 × 104 tCO2e (with a service life of 50 years) in China. The construction phase and operation phase generated about 18.73 and 81.27% of this emission, respectively. The preliminary conclusions of this study may help shed light on the emission reduction potential of urban rail transit systems and the emission reduction targets in China. [ABSTRACT FROM AUTHOR]

Focusing on the insufficient passenger demand during off-peak periods in urban rail transit, a new train operation mode with flexible train composition considering trains couple/decouple at intermediate stations is proposed. An optimized two-phase nonlinear model is formulated in order to design a periodic train schedule, where train headways remain constant over a specific period. This model takes into account the flexible train composition. The objective function aims to minimize total passenger waiting time and train operating costs while considering constraints such as maximum line passing capacity, passenger flow demand, and the number of train units in the composition. Linearization techniques are applied to convert nonlinear constraints into linear equivalents, and the CPLEX solver is employed to solve the model. An illustrative real-case example is presented to validate the effectiveness of the proposed model. The results demonstrate that the operation mode with flexible train composition during off-peak periods offers significant advantages over traditional train composition modes, including single train routes, long-short train routes, and multiple train compositions. This mode enables better alignment between passenger demand and capacity, reduces operating costs, and enhances passenger service levels. The proposed operation mode is particularly suitable for urban rail transit lines with low passenger demand but requiring high passenger service, such as suburban lines or common lines during off-peak periods. [ABSTRACT FROM AUTHOR]

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The rapid advancements in information technology and intelligent systems within urban rail transit (URT) systems have highlighted the need for more personalized route recommendations that consider passengers' travel habits. This study aims to address this issue by investigating passenger travel routes alongside other passengers who share similar travel preferences, utilizing collaborative filtering (CF) techniques. The approach involves analyzing historical card data to assess passenger travel profiles, including actual travel time under crowded conditions. By considering both individual passenger preferences and the preferences of similar passengers, a CF algorithm is employed to offer personalized route recommendations. The Shenzhen metro is used as a case study to illustrate the proposed method. The results demonstrate that the proposed approach surpasses traditional route recommendation methods by providing tailored suggestions that align more closely with passengers' travel preferences. These findings emphasize the value of incorporating passenger travel preferences into route recommendation models, thereby enhancing the accuracy and effectiveness of personalized route recommendations within URT systems. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The increasing frequency of urban flooding, driven by global climate change, poses significant threats to the safety and resilience of urban rail transit systems. This study systematically examines the cascading failure processes and resilience of these networks under flood conditions, with a specific focus on the Shanghai Metro. A comprehensive resilience evaluation model was developed by integrating geographic information, static network characteristics, and dynamic passenger flow indicators. This study employs an improved Coupled Map Lattice (CML) model to simulate cascading failures by considering the coupling effects of station centrality, geographic elevation, and passenger flow dynamics. The results indicate that stations with higher degrees of centrality are more likely to trigger rapid cascading failures across the network. However, incorporating dynamic passenger flow and geographic elevation data helps mitigate these effects, emphasizing the need for multi-dimensional resilience strategies. The findings provide valuable insights for urban transit management, offering a scientific foundation for developing targeted disaster response strategies to enhance network resilience against floods. This study advances our understanding of the vulnerability of urban rail transit systems and offers practical guidance for improving disaster preparedness in urban transportation infrastructure. [ABSTRACT FROM AUTHOR]

Summary: To explore the complex stress mechanism of prestressed concrete U‐beams in urban rail transit, in order to improve the safety of urban rail transit construction and the economy of beam structures. The study first analyzed the complex stress mechanism of U‐beams and obtained a tension compression rod model through finite element analysis. Then, experimental research was conducted on the vertical three‐dimensional finite element stress of U‐beams, and strain cloud maps were obtained and compared with calculated values. The experimental data show that the beam can still recover to its original state after the second cycle, and the beam will not crack. This recovery mechanism means that U‐beams have high crack resistance and stability under complex stress processes. In the vertical deformation cloud map of the U‐beam, the deflection of the mid span section is the largest, with a maximum displacement of about 20.4 mm, which is very close to the measured value of 20.3 mm. In the measured data of concrete strain measuring points and the results of finite element calculation, the difference rate between measured values and calculated values of some measuring points is within 10%. The results indicate that the U‐shaped beam tension and compression rod model combined with finite element analysis has a high degree of conformity with the actual situation, and can provide technical reference for the construction of urban rail transit. The stress mechanism and design method proposed in the study have high reliability and are suitable for the design and construction of prestressed concrete U‐beams in urban rail transit construction. [ABSTRACT FROM AUTHOR]

To address the symmetry-related resilience issues of stations and lines in urban rail transit networks, we propose a two-stage robust optimization-based approach for urban rail transit network planning. In this context, resilience is conceptualized as the ability of the network to maintain its operational symmetry under normal and disruptive conditions. Firstly, we used passenger flow distributions as decision variables to construct a two-stage symmetry-based urban rail transit network planning model, aiming to simultaneously minimize the total cost and total operating time of the network while preserving its functional symmetry. Secondly, we designed a hybrid evolutionary algorithm with chromosomes having a two-layer encoding structure, where the Niched Pareto Genetic Algorithm served as the main algorithmic framework, and a Large Neighborhood Search mechanism was designed to optimize the connectivity gene layer of individuals, ensuring the symmetry of network connectivity. Finally, we conducted computational verification on randomly generated instances to confirm the effectiveness of the model and algorithm. The experimental results demonstrated that our method could find two sets of Pareto optimal solutions for cost preference and time preference, thereby preserving the operational symmetry of the network under normal and damaged conditions, as well as reducing the total operating time. This effectively improved the overall efficiency and resilience of the network. Our designed hybrid evolutionary algorithm converged to satisfactory objective values in the early iterations, exhibiting strong search and optimization performance and effectively solving the two-stage symmetry-based urban rail transit network planning model. [ABSTRACT FROM AUTHOR]

To solve the problem of passenger travel cost and full load rate balance before and after the implementation of cross-line operation in the peak period of urban rail transit (URT) passenger flow. This paper introduces the concept of section full load rate balance. According to the division of different types of passenger flow in the intersection mode of "X" and "Y" lines, based on the characteristics of passenger flow in peak passenger flow period, a mathematical model with cross-line operation mode turn-back station and operation frequency of train as decision variables is constructed to minimize passenger travel cost, full load rate balance objective function and enterprise operation cost. Taking the Changping Line and Line 8 in the Beijing URT system as examples, the validity of the model is verified. The results show that the operation mode of the cross-line train can alleviate the passenger flow pressure of the transfer station, reduce the overload of the original train on the line, improve the full load rate balance of the train, make the utilization of the transport capacity more balanced, and optimize the passenger ride experience. In addition, considering the operation of cross-line trains may increase the operating costs of enterprises. [ABSTRACT FROM AUTHOR]

With the rapid development of urban rail transit, the issue of stray current due to incomplete insulation between the rail and the earth is attracting increasing attention. Stray current seriously affects the safe operation of urban rail transit, pipelines and power grids, and pose challenges to the development of urban energy security. However, there are significant differences among the various methods for the analysis and verification of stray currents, and existing research works thus lack a mature theoretical framework. To address this, this paper presents an in-depth review on the study of stray current including the mechanism, technical standards, modelling, simulation and mitigation technologies. Firstly, the principle and standards of stray current are introduced from multiple perspectives (i.e. metro side, pipeline side and grid side). Then, the typical modelling and simulation of stray current are summarized and compared in detail. The representative stray current mitigation measures, together with the stray current hardware simulation technologies, are also discussed. In conclusion, this paper provides a comprehensive overview of key technologies involved in the modelling and simulation of stray current, and highlights the valuable research direction in stray current mitigation. [ABSTRACT FROM AUTHOR]

Copyright of Smart Rail Transit is the property of Smart Rail Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Smart Rail Transit is the property of Smart Rail Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Railway Construction Technology is the property of Railway Construction Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Railway Standard Design is the property of Railway Standard Design Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Railway Standard Design is the property of Railway Standard Design Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)