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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]

Rail transit system plays an important role in connecting the urban center, new towns and suburbs in the metropolitan area. Exploring the methods of integrating rail transit and land use in different locations is essential to improve trip rates and promote transit-oriented development (TOD). Taking Wuhan, China, as a case study, this research uses a cellular automaton-based random forest (CA-RF) model to simulate land use change surrounding rail transit by capturing the impact differences in mother city (MC) and new towns (NT). An impact area identification model is proposed to recognize the most sensitive threshold of land use change in both MC and NT stations, which are 1000m and 1200m, respectively. The characteristics of land use change from 2010 to 2020 are analyzed and compared. The CA-RF model calibration shows that transportation and spatial accessibility present wide variations in driving land use change surrounding the MC and NT stations. The high accuracy of CA-RF model, which is 88.1% for MC and 82.1% for NT, indicating its effectiveness in quantifying the non-linear relationship of land use change and spatial attributes. The forecasted land use maps for proposed rail stations provide a reference for governments and planners in policy intervention and decision-making. This research framework can be applied to other metropolitans to explore the regulations of land use development and promote transit-oriented sustainable development. • Explore the driving mechanisms for land use change within rail transit stations area in different metropolitan areas. • Examine the non-linear relationship between land use change and spatial attributes by proposing a CA-RF model. • Develope a simulation framework to visualize future land use maps considering the impacts of 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]

To meet the evolving demands of land use and transport interaction (LUTI) assessment within China's national territory spatial planning (NTSP) system, this paper introduces the level of development (LoD) and the level of matching (LoM) evaluation models, based on the traffic facility and land use factors. The LoD model, founded on the modified node‐place model, provides a comprehensive assessment of the traffic facility and land use development scales. Conversely, the LoM model, grounded in data envelopment analysis methods, evaluates the relative relationship between transport services and the travel demand generated by land use. The integrated use of LoD and LoM can both reflect the development scale and matching status between traffic services and travel activities, which are crucial contents in the planning works, especially within the NTSP framework. The proposed models are tested in the city of Guangzhou, and the LoD values exhibit peaks in central urban zones, suburban towns, and areas adjacent to railway transit, with a decline observed in rural farmland and ecological regions. In contrast, the LoM distribution performs a distinct pattern, highlighting numerous underperforming areas with congestion or idle problems in urban centers, alongside well‐coordinated regions showcasing a balance between traffic facilities and land uses in rural regions. Furthermore, the LoM scores revealed frequent instances of facility crowding in urban regions and intensive occurrences of facility idleness in rural areas. By marking regions with low LoD scores, the LoD model finds suitable application in determining the urban development border, essential for restricting land development and preserving farmland and ecological areas. Meanwhile, LoM aids in improving urban renewal efforts by assessing and optimizing the balance between intensive land uses and limited traffic facilities. Validated against the existing metrics, the combined use of LoD and LoM efficiently captures the most details of the LUTI process at the lowest computational cost. [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]

Development of urban transport facilities via analysing indicators has become one of the new domains explored worldwide, as cities are growing with no control. The present state of sustainability testing practices feels that technical analysis is rather very complex collaboratively and comprehensively met with local statements. As a result, developing indicators assessment techniques has become inevitable to deal with these challenges. In this study, a comprehensive transport assessment index such as trustworthiness, Amenity, Receptivity, Assurance and Affinity for the cities through passenger response can be useful for the researchers in the city. A set of 5 indicators has been formulated among 500 respondents that might influence and shape urban public transport performance. For finding the Key Performance Indicator (KPI) of the Urban transport system, the hybrid Complex Proportional Assessment (COPRAS)—Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied to measure the quantitative weight to analyze the complex process of perceived service quality. This model is validated by different Multi-Criteria Decision Making (MCDM) such as Analytic Hierarchy Process (AHP) and Fuzzy-AHP by comparing performance measures for randomly selected elements, which is new to mixed traffic flow conditions. The performance obtained such as computational complexity and agility in decision making by the hybrid COPRAS-TOPSIS are 132 and 1245. Hence, the proposed approach was more accurate than conventional ones, thus used to support decision-makers for long-term designing and planning of transport networks based on priority. Based on these factors, the present study successfully develops and applies KPI analysis prediction of the general transport approach in the background of India. [ABSTRACT FROM AUTHOR]

The integration of shared bikes and flexible transit services offers significant potential and benefits for urban transit systems. This paper presents a novel robust optimization framework for designing demand responsive connectors (DRC) that are fed by shared bikes. A two-stage model is formulated, with the first stage focusing on prereserved requests and the second stage addressing en-route random requests. The findings demonstrate that incorporating shared bikes can enhance the efficiency of serving en route late requests and reduce the required DRC vehicle fleet size. Additionally, it is observed that e-bikes are particularly suitable for this joint service due to their adaptability. [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]

The rack railway system, driven by the meshing of vehicle gears and ground racks, overcomes the limitations of wheel-rail adhesion and offers advantages such as strong climbing ability, environmental sustainability, and low operational costs. These factors made it a key focus for rail transit construction in mountainous areas. The mountain rack railway system in China is still in its infancy, and technical standards for rack railway vehicles have yet to be fully established. Compared to traditional wheel-rail vehicles, rack railway vehicles exhibit significant differences. In order to clarify the particularity and top-level indicators of mountain rack railway tourism vehicles, this study analyzed the features of mountain tourist rail transportation. Based on the progress of domestic mountain rack railway tourism projects, the study identified specific vehicle requirements in terms of steep-slope operation, transitions between steep and gentle slopes, axle load, and sightseeing performance. The adaptability of meter gauge and standard gauge tracks in mountain tourism railways was studied, particularly how track gauge influences vehicle structure, clearance, and track structure. It was clarified that meter gauge tracks should be prioritized for routes with lower traffic volumes. A technical specification system was proposed, with three standard vehicle widths: 2. 65 m, 2. 5 m, and 2. 3 m. The corresponding vehicle dimensions and passenger capacities were specified. Train formations were proposed with capacities ranging from 486 to 7 320 passengers per hour. Additionally, an analysis of rack railway line characteristics indicated that the traction and braking performance of rack railway tourism vehicles should be between the levels of high-speed railway vehicles and urban rail transit vehicles. [ABSTRACT FROM AUTHOR]

Intercity railways in urban agglomerations serve as critical transportation infrastructure, playing a key role in supporting and guiding the advancement of new urbanization in China. The functional orientation and technical standards of such railways have become focal areas of research within the industry. However, the current national railway technical standard system is insufficient to fully address the entire process of intercity railway construction and operation in urban agglomerations, particularly within the framework of "four network integration and interconnection". This study focused on the intercity railways in Jiangsu Province's urban agglomerations, with the goal of facilitating their integration into city centers and their operation as public transit systems. Based on their functional positioning and technical characteristics, and through comparisons with the national railway and urban rail transit standard systems, two major subsystems-engineering construction and transportation services-were constructed. Moreover, the system encompassed several key business areas, including foundation, surveying, design, construction, inspection, operation, management, and services. A standard framework, covering various specialized and thematic research areas, was established to guide the design, construction, equipment, inspection, and operational management of intercity railways in urban agglomerations. This framework aims to realize the vision of "unified planning, unified standards, coordinated operation, interconnection, seamless transfers, and one-ticket access." [ABSTRACT FROM AUTHOR]

To implement the requirements of "Building a Strong Transportation Nation" and the "Dual-Carbon Goals", and guided by the "Action Plan for Green Urban Rail Transit Development in Chinese Cities", this study explores green and low-carbon analysis and evaluation methods in the rail network planning stage from three aspects: urban space, integrated transportation, and rail transit. A comprehensive theoretical framework was systematically constructed, covering macro to micro levels. The analysis process employed various methods, including model building, spatial econometrics, geographic analysis, and passenger flow prediction, to establish a theoretical structure for the green and low-carbon effects of rail transit through both quantitative and qualitative approaches. The study proposed a tripartite green development framework integrating "City-Transportation-Railway", forming a primarily quantitative evaluation index system that effectively promotes green travel. This system aligned with higher-level planning while considering the unique characteristics of rail transit, guiding the next phase of green urban rail implementation and related research. The research results demonstrated that combining elements of rail network planning with urban and integrated transportation could effectively and quantitatively reflect green development goals. The calculation methods for evaluation indicators were feasible, operational, and comparable. This framework not only meets the requirements and goals of national land spatial planning and integrated transportation planning but also provides valuable guidance for subsequent stages of construction planning, feasibility studies, and preliminary designs in green development. Furthermore, it aligns well with the entire planning and design process of rail transit. The conclusions and recommendations from this study can be effectively incorporated into national land spatial planning and integrated transportation planning to ensure a unified planning blueprint. [ABSTRACT FROM AUTHOR]

In order to improve the control performance of automatic train operation (ATO) in urban rail trains, five typical operating sequences of urban rail trains were studied. Under the condition of meeting the safety and comfort principles of train operation, a train dynamics model was established to achieve the goals of low energy consumption, short running time, and high stopping accuracy in urban rail transit trains. In the process of finding a multi-objective solution to this problem, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was used with an elite retention strategy, and the optimal Pareto multi-objective solution set was sought. In the process of optimal solution weight assignment, the hierarchical analysis Mahalanobis distance method, which combines subjective and objective analysis, was used. Finally, taking the Beijing Yizhuang subway line as the background design example, the simulation verified the effectiveness and feasibility of the algorithm and obtained high-quality automatic train driving curves under various working conditions. This research has important reference significance for the actual operation of automatic driving in urban rail trains. [ABSTRACT FROM AUTHOR]

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]

The U-shaped beam, a novel beam type, is extensively used in urban rail transit due to its ability to block radiation noise from wheel–rail interactions, providing effective noise control at the source. This study aims to investigate the dynamic response and structure-borne noise of a 25m concrete simply supported U-shaped girder at a vehicle speed of 80km/h, and to assess the effectiveness of novel track vibration damping devices. Grounded in stochastic vibration theory, this study employs pseudo excitation methods (PEM) to obtain the frequency domain response of a finite element model of the U-shaped girder. The structure-borne noise is then analyzed using the acoustic indirect boundary element method (BEM), with frequency-domain responses from PEM as input conditions. The eastern extension section of Nanjing Metro Line 2’s simply supported U-shaped beam is used as a case study for practical and theoretical analyses of dynamic characteristics and structural radiation noise. This study reveals that the sound pressure level of the mid-span section’s bottom plate is higher directly below and above it, while it is lower on either side of the web. Considering the ground reflection effects, sound pressure levels are highly closer to ground reflections. The damping device significantly attenuates vibration noise in the frequency range of 50–200Hz, with the bottom plate exhibiting superior vibration reduction effects compared to the web. The predictive method aligns closely with field test results, validating the feasibility of the approach. This study demonstrates that the U-shaped beam effectively controls rail transit noise, and the novel track vibration damping devices significantly reduce vibration noise, particularly in the bottom plate. This research provides valuable insights for noise control in urban rail transit systems. [ABSTRACT FROM AUTHOR]

Urban rail transit is rapidly evolving, with long-span cable-stayed bridges becoming increasingly popular among engineers because of their adaptability. However, as the operation time of bridges with tracks increases, the combined effects of repeated wheel loads and various bridge system excitations, including temperature changes and shrinkage–creep, gradually alter the alignment of the main beam. Similarly, the track structure on the bridge deck also undergoes vertical displacement, leading to corresponding degradation in rail alignment. This study applies singular function theory and verifies its effectiveness in bridge mechanical analysis. For the steel spring floating plate track cable-stayed bridge system, the mapping relationship between the deformation of the bridge’s main beam and the track’s deformation under bridge system excitation is derived, integrating singular function theory with the theory of train–track–bridge dynamic interaction. By analyzing the force transfer mechanism of the track–bridge system under system excitation, this research examines the effects of different excitations on the coupled dynamic response of the train–track–bridge. These findings suggest that system excitations increase the dynamic response of the train–track–bridge system, which is crucial for improving the durability and reliability of urban rail transit infrastructure. [ABSTRACT FROM AUTHOR]

With the rapid urbanization and expansion of rail transit systems, the axle loads of trains, which are a critical aspect of their configuration, have significantly increased. This increase poses substantial potential threats to the safety and service life of existing bridges within urban rail transit networks. Therefore, it is imperative to develop methods for monitoring and identifying train axle loads. In this study, a strain field measurement scheme was devised and implemented for an operational simply supported composite beam bridge in urban rail transit. This involved numerical modeling and validation of the bridge's structural response, followed by the calculation of strain influence lines at specific measurement points. Subsequently, a method for identifying train axle loads, considering the dynamic amplification effect, was established. This method integrates principles from strain influence lines with mathematical optimization techniques. Specifically, the axle loads of locomotives on the forward AC03 type trains of Shanghai Metro Line 3 were found to conform to a logarithmic normal distribution model, while those of middle carriages followed a normal distribution model. Their respective mean axle loads were determined as 9.64 t and 10.77 t, with a shared variance of 0.8. Similarly, the axle loads of locomotives and middle carriages on reverse AC03-type trains also followed normal distribution models, with identical mean values around 10.5 t. The variances for axle loads of locomotives and middle carriages of reverse trains were found to be 1.36 and 0.8, respectively. The developed method effectively enables the monitoring of train axle loads and assessment of their impact on bridge structures, therefore enhancing safety and operational reliability within urban rail transit systems. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization construction, the contradiction between supply and demand of urban public transportation resources is becoming increasingly prominent, resulting in increasingly serious problems such as traffic congestion and environmental pollution, which has a great impact on the sustainable development of cities. The experience of mature markets both domestically and internationally indicates that priority must be given to the development of public transportation networks with rail transit as the backbone. Due to the fact that the operation of the rail transit system should not only focus on market benefits but also meet social benefits as much as possible, evaluating efficiency has become a prerequisite for managing rail transit and an important topic in the performance evaluation of urban public transportation. The urban public transport operations evaluation is a multiple-attribute group decision-making (MAGDM). Then, the TODIM and EDAS method has been constructed to deal with MAGDM issues. The interval neutrosophic sets (INSs) are constructed as an effective tool for representing uncertain information during the urban public transport operations evaluation. In this manuscript, the interval neutrosophic number TODIM-EDAS (INN-TODIM-EDAS) method is constructed to solve the MAGDM under INSs. Finally, a numerical example study for urban public transport operations evaluation is constructed to validate the INN-TODIM-EDAS method. The main research contribution of this paper is constructed: (1) the INN-TODIM-EDAS method is put up for MAGDM with INSs; (2) the INN-TODIM-EDAS method is put up for urban public transport operations evaluation and were compared with existing methods; (3) Through the detailed comparison, it is evident that INN-TODIM-EDAS method for urban public transport operations evaluation proposed in this paper are effective. [ABSTRACT FROM AUTHOR]

Elevated stations are integral components of urban rail transit systems, significantly impacting passengers' travel experience and the operational efficiency of the transportation system. However, current elevated station designs often do not sufficiently consider the structural dynamic response under various operating conditions. This oversight can limit the operational efficiency of the stations and pose potential safety hazards. Addressing this issue, this study establishes a vehicle-bridge-station spatial coupling vibration simulation model utilizing the self-developed software GSAP V1.0, focusing on integrated station-bridge and combined station-bridge elevated station designs. The simulation results are meticulously compared with field data to ensure the fidelity of the model. Analyzing the dynamic response of the station in relation to train parameters reveals significant insights. Notably, under similar travel conditions, integrated stations exhibit lower vertical acceleration in the rail-bearing layer compared to combined stations, while the vertical acceleration patterns at the platform and hall layers demonstrate contrasting behaviors. At lower speeds, the vertical acceleration at the station concourse level is comparable for both station types, yet integrated stations exhibit notably higher platform-level acceleration. Conversely, under high-speed conditions, integrated stations show increased vertical acceleration at the platform and hall levels compared to combined stations, particularly under unloaded double-line working conditions, indicating a superior dynamic performance of combined stations in complex operational scenarios. However, challenges such as increased station height due to bridge box girder maintenance, track layer waterproofing, and track girder support maintenance exist for combined stations, warranting comprehensive evaluation for station selection. Further analysis of integrated station-bridge structures reveals that adjustments in the floor slab thickness at the rail-bearing and platform levels significantly reduce dynamic responses, whereas increasing the rail beam height notably diminishes displacement responses. Conversely, alterations in the waiting hall floor slab thickness and frame column cross-sections exhibit a minimal impact on the station dynamics. Overall, optimizing structural dimensions can effectively mitigate dynamic responses, offering valuable insights for station design and operation. [ABSTRACT FROM AUTHOR]

The real-time dynamic coupling under the virtual-coupling technology guarantees running multi-group trains in different sections based on passenger flow's spatial and temporal distribution characteristics. It enables more flexible transport organization modes such as full-length and short-turn routing and the express/local stop mode. Utilizing the redundant capacity of the airport line to carry out coordinated transportation of passengers and goods is an essential means of relieving urban traffic pressure, saving energy, and protecting the environment. To promote urban rail transit to achieve the goal of "double-carbon," the optimization of passenger and freight cooperative transport schemes based on the virtual coupling technology for the airport line of the rail transit is investigated. Based on the train operation of full-length and short-turn routing under the virtual-coupling technology, according to the unbalanced distribution characteristics of passenger and freight flows in urban rail transit, this paper constructs an optimization model of urban rail transit capacity allocation to minimize passenger travel costs, enterprise operation costs, and carbon emission costs, and considering the constraints of the carbon emission policy, with the constraints such as the train operation safety, the constraints of the balance of the passenger flow and freight flow loading. The multi-objective model to solve the optimization problem has been designed. Given the complexity of the multi-objective model solution, an improved nondominated sorting genetic algorithm is designed, and the algorithm makes use of the screening function of the constraints during the initial population generation process, which effectively balances the relationship between the whole process of screening and time-consumption of the NSGA-II algorithm. Taking Wuxi Metro Line 3 as an example, the results show that: (i) Compared with the traditionally large and small crossing mode, full-length and short-turn routing of virtual coupling Trains proposed in this paper can reduce the average waiting time of passengers by about 2.01min, the cost of carbon emission by 15.7% and the cost of enterprise operation by 20.2%, and at the same time, improve the average total rate by about 50%, which verifies that the virtual formation technology is conducive to improving the efficiency of passenger travel and reducing the operating cost and the carbon cost. It is also beneficial for improving passenger traveling efficiency. (ii)The single-alignment fixed configuration mode, compared with the single-flow mode, can increase the average full-load rate by 26.6% while increasing the passenger travel cost by only 0.98% and reducing the carbon emission cost by a small margin; the virtual configuration technology based-on the large and smallalignment operation and transport scheme increases the passenger travel cost by about 0.3% and further reduces the carbon emission cost by 1.9%. They further reduced the carbon emission cost by 1.9% while increasing the average total occupancy rate by about 5.8%. The proposed models and algorithms provide some decision support for urban rail transit operators to optimize transport schemes under different carbon emission policies and the virtual coupling operation mode. [ABSTRACT FROM AUTHOR]

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The development of urban rail transit systems is progressing rapidly, and an increasing number of cities worldwide are either in the process or planning to construct urban rail transit systems. However, questions arise as to whether all cities truly require such systems and how to prevent these transit systems from becoming decorative rather than essential to urban development. This paper collates various antecedent conditions that may influence ridership in rail transit across 31 cities in China with operating rail transit. To this end, it uses two main methods, including necessary condition analysis and fuzzy-set qualitative comparative analysis methodologies. In this way, it explores the interactive effects of multiple factors such as the level of urban development, trends in urban development, ground-level road network congestion, urban spatial patterns (centrality), coverage of rail transit, and government focus on rail transit. The results show that no single antecedent condition can be a necessary condition for high ridership in rail transit. Rather, the effective combination of various antecedent conditions has elevated postimplementation ridership in a different means, same end manner. Moreover, the government's focus on rail transit development or its effective planning can increase postimplementation ridership in cities lacking some objective resource conditions. Furthermore, ridership in most cities remains stable after the initiation of rail transit. The findings imply that the development of urban rail transit systems requires a comprehensive urban development plan, which covers urban growth trends, congestion levels, and spatial patterns. [ABSTRACT FROM AUTHOR]

A noisy environment can considerably impact drivers' attention and fatigue, endangering driving safety. Consequently, this study designed a simulated driving experimental scenario to analyse the effects of noise generated during urban rail transit train operation on drivers' functional brain networks. The experiment recruited 16 participants, and the simulated driving scenario was conducted at noise levels of 50, 60, 70, and 80 dB. Functional connectivity between all electrode pairs across various frequency bands was evaluated using the weighted phase lag index (WPLI), and a brain network based on this was constructed. Graph theoretic analysis employed network global efficiency, degree, and clustering coefficient as metrics. Significant increases in the WPLI values of theta and alpha frequency bands were observed in high noise environments (70 dB, 80 dB), as well as enhanced brain synchronisation. Furthermore, concerning the topological metrics of brain networks, it was observed that the global efficiency of brain networks in theta and alpha frequency ranges, as well as the node degree and clustering coefficients, experienced substantial growth in high noise environments (70 dB, 80 dB) as opposed to 50 dB and 60 dB. This finding indicates that high-noise environments impact the reorganisation of functional brain networks, leading to a preference for network structures with improved global efficiency. Such findings may improve our understanding of the neural mechanisms of driving under noise exposure, and thus potentially reduce road accidents to some extent. [ABSTRACT FROM AUTHOR]

Prolonged exposure to high-intensity noise environments in urban rail transit systems can negatively impact the health and work efficiency of drivers. However, there is a lack of comprehensive understanding of the noise pattern and, therefore, effective mitigation strategies. To control the noise in urban rail transit systems, this study proposes a comprehensive noise assessment framework, including metrics such as average sound pressure level, peak sound pressure level, percentile sound pressure levels, dynamic range, main frequency component, and cumulative time energy to evaluate the noise characteristics. We also employ a density-based spatial clustering of applications with noise (DBSCAN) method to identify the noise patterns with the evaluation of their hazard to urban rail transit drivers. The results have revealed that: (1) The equivalent continuous sound pressure level (Leq) in the cab of Lanzhou Urban Rail Transit Line 1 averages 87.12 dB, with a standard deviation of 8.52 dB, which reveals a high noise intensity with substantial fluctuations. (2) Ten noise patterns were identified, with frequencies varying from 14.47 Hz to 69.70 Hz and Leq varying from 60 dB to 115 dB. (3) The major noise sources from these patterns are inferred to be the train's mechanical systems, wheel–rail interaction, aerodynamic effects, and braking systems. Combined with the noise patterns and urban rail transit's operation environment, this study proposes tailored mitigation strategies for applications aimed at protecting drivers' hearing health, enhancing work efficiency, and ensuring driving safety. [ABSTRACT FROM AUTHOR]

In urban rail transit construction, the empirical prediction equation is generally used to predict the environmental vibration along the line after its operation. It is used as the basis for whether to adopt a vibration-damping track or which vibration-damping track to use. Due to the superposition of complex factors such as geological conditions, differences in vehicles, and their running foundations on the prediction results, the track implementation should be considered at least with a design margin of 3 dB. However, there are still a large number of environmental vibration complaints. Therefore, an ecological vibration prediction method with higher prediction accuracy is proposed. The construction of the track-foundations, such as tunnel, bridge, and roadbed is completed. Before the track system is constructed, a fixed-point excitation load simulating the passage of trains is applied on the track-foundation, and the vibration-sensitive points around the line are directly measured by the actual measurement—environmental vibration prediction evaluation. The fixed-point vibration source function model for simulating train passing is deduced as the excitation load applied to the track-foundation; the fixed-point vibration source foundation finite element model and vehicle-track-foundation coupled dynamic model with the same track-foundation and foundation model have been established. The vibration response of the track-foundation and foundation surface when the train passes through is calculated separately. The results corresponding to different prediction methods of environmental vibration are compared and analyzed. The comparison between the theoretical prediction value of vibration response, the prediction value of the empirical equation, and the measured value shows that the vibration response waveform of the foundation surface is the same as that of the actual train passing under the action of the fixed-point vibration excitation source function. The vibration prediction error is much smaller than the empirical equation predicts the result. It shows that the prediction accuracy of the fixed-point excitation prediction method for environmental vibration is higher than that of the existing practical equation method. Because the fixed-point vibration source device is simple to implement and the ecological vibration field testing method is mature, it is beneficial to the field application of the fixed-point vibration excitation prediction method for urban rail transit environmental vibration. [ABSTRACT FROM AUTHOR]

The intrusion of objects into track areas is a significant issue affecting the safety of urban rail transit systems. In recent years, obstacle detection technology based on LiDAR has been developed to identify potential issues, in which accurately extracting the track area is critical for segmentation and collision avoidance. However, because of the sparsity limitations inherent in LiDAR data, existing methods can only segment track regions over short distances, which are often insufficient given the speed and braking distance of urban rail trains. As such, a new approach is developed in this study to indirectly extract track areas by detecting references parallel to the rails (e.g., tunnel walls, protective walls, and sound barriers). Reference point selection and curve fitting are then applied to generate a reference curve on either side of the track. A centerline is then extrapolated from the two curves and expanded to produce a 2D track area with the given size specifications. Finally, the 3D track area is acquired by detecting the ground and removing points that are either too high or too low. The proposed technique was evaluated using a variety of scenes, including tunnels, elevated sections, and level urban rail transit lines. The results showed this method could successfully extract track regions from LiDAR data over significantly longer distances than conventional algorithms. [ABSTRACT FROM AUTHOR]

Park-and-ride (P&R) facilities, when combined with the advantages of urban rail transit, have the potential to alleviate congestion problems and promote multimodal transportation. However, some facilities face operational and utilization issues in terms of efficiency and capacity, which may not fully meet users' needs. Therefore, it is essential to conduct research that addresses challenges related to user parking and facility operation. A critical step in this direction is to study relevant indicators that comprehensively measure the operational efficiency and capacity of the facilities, and to find effective methods to tackle problems in P&R operation. This study examined and selected data indicators for P&R facilities around targeted stations, taking those around Nanjing urban rail transit stations as an example. Data envelopment analysis was employed to establish a P&R operational efficiency and capacity evaluation model. This model was enriched with a comprehensive evaluation of multiple factors that affect P&R, with these factors significantly influencing the recorded evaluation results. By identifying operational issues in P&R facilities around Nanjing urban rail transit stations, this study proposed optimization solutions to enhance their operational efficiency and capacity. The findings can serve as a reference for the coordinated development of P&R facilities and urban rail transit. [ABSTRACT FROM AUTHOR]

Many cities and countries have embraced transit-oriented development as an international growth management approach, shifting trips from car-oriented to transit. But in this race to become more sustainable, who is being left out? Using mobility justice as a theoretical framework, this paper presents a qualitative comparison between Malmö, Sweden, and Kitchener, Canada, two mid-sized cities where new rail-based infrastructure was completed in 2019. Using over 40 interviews with local residents, business owners and staff, planners, and private sector developers in each city, we found that transportation improvements have created unequal transport mobilities. In both cities, new transportation infrastructure did not improve travel times or access to transit for existing users; in Kitchener, local changes to make way for the new LRT had negative effects on the neighborhood, and bus transit times increased, while in Malmö bus services remained the same as the new train was barely used. This detachment of transportation needs from infrastructure is necessary for local and state politicians to promote new infrastructure as a branding approach. We call this transit boosterism. [ABSTRACT FROM AUTHOR]

The design of transportation networks is generally performed on the basis of the division of a metropolitan region into communities. With the combination of the scale, population density, and travel characteristics of each community, the transportation routes and stations can be more precisely determined to meet the travel demand of residents within each of the communities as well as the transportation links among communities. To accurately divide urban communities, the original word vector sampling method is improved on the classic Deepwalk model, proposing a Random Walk (RW) algorithm in which the sampling is modified with the generalized travel cost and improved logit model. Urban spatial community detection is realized with the K-means algorithm, building the F-Deepwalk model. Using the basic road network as an example, the experimental results show that the Deepwalk model, which considers the generalized travel cost of residents, has a higher profile coefficient, and the performance of the model improves with the reduction of random walk length. At the same time, taking the Shijiazhuang urban rail transit network as an example, the accuracy of the model is further verified. [ABSTRACT FROM AUTHOR]

The urban (suburban) railway is a fast and convenient rail transit system connecting urban and suburban areas, and a refined analysis of the diversity of public service facilities around its stations can help to promote the intensive use of land around rail stations. However, the differences in the diversity of public service facilities in the railway life circle between urban and suburban railway stations and the factors affecting them are not clear. This paper takes the Beijing Suburban Railway Line Sub-center (Line S1) as a case study, uses the Shannon-Wiener index to measure the spatial diversity characteristics of public service facilities, and utilizes a multi-scale geographically weighted regression model to explore the influencing factors. The findings indicate that: (1) Centered on the stations, all six stations show a "less-more-less" ring or half-ring to the left distribution structure of the comprehensive diversity index of public service facilities within their study areas, with an increase followed by a decrease. (2) The influence of each influencing factor on the diversity of market-featured facilities exhibits significant differences. The most substantial spatial heterogeneity is observed in the distances to the nearest subway stations and bus stops. Distances to subway and urban (suburban) railway stations exhibit different spatial distribution characteristics within urban and suburban areas on Line S1. In urban areas, the closer the distance to the subway station or the further the distance to the railway station, the greater the diversity of public service facilities. Conversely, in suburban areas, the opposite is true. The conclusions of this research provide a scientific methodology and improvement measures to facilitate the construction of railway life circles in suburban regions of megacities. [ABSTRACT FROM AUTHOR]

The Virtual Track Train (VTT) represents an innovative urban public transportation system that combines tire-based running gears with rail transit management. Effective control of such a system necessitates precise state estimation, a task rendered complex by the multi-articulated nature of the vehicles. This study addresses the challenge by focusing on state estimation for the first unit under significant interference, introducing a fusion state estimation strategy utilizing Gaussian Process Regression (GPR) and Interacting Multiple Model (IMM) techniques. First, a joint model for the first unit is established, comprising the dynamics model as the main model and a residual model constructed based on GPR to accommodate the main model's error. The proposed fusion strategy comprises two components: a kinematic model-based method for handling transient and high-acceleration phases, and a joint-model-based method suitable for near-steady-state and low-acceleration conditions. The IMM method is employed to integrate these two approaches. Subsequent units' states are computed from the first unit's state, articulation angles, and yaw rates' filtered data. Validation through hardware-in-the-loop (HIL) simulation demonstrates the strategy's efficacy, achieving high accuracy with an average lateral speed estimation error below 0.02 m/s and a maximum error not exceeding 0.22 m/s. Additionally, the impact on VTT control performance after incorporating state estimation is minimal, with a reduction of only 3–6%. [ABSTRACT FROM AUTHOR]

With the rapid development of urbanization, the scope of the city is expanding. At present, the design speed of subway is mostly 80 ~ 120 km/ h, which is slightly insufficient for long-distance commuting. In order to better achieve urban integration, higher-speed urban rail transit has emerged and has been put into operation in some cities. In order to grasp the environmental vibration impact of 160 km/ h urban rail rapid transit underground line, this paper carried out a systematic environmental vibration test on the 160 km/ h urban rapid rail transit underground line of a city, including rail, ballast bed and tunnel wall vibration test, as well as ground vibration and indoor secondary structure noise test. The structure shows that when the train speed is 124 to 152 km/ h, the rail vibration is 109. 7 to 111.2 dB, the ballast vibration is 75.6 to 77.4 dB, the tunnel wall vibration is 67.5 to 69.4 dB, and the average speed is 138 km/ h, the tunnel wall vibration is 68.7 dB. After the train vibration is transmitted to the ground, the maximum Z vibration level of the ground vibration is 46.4 to 60.2 dB at the distance of 0 to 40m from the track directly above the tunnel. The ground vibration basically conforms to the attenuation law that decreases with the increase of distance, but the vibration response of the measuring point at 10m from the track is larger than that at 0m. In the case of rubber vibration isolation pad monolithic track bed, the maximum secondary structural noise of the first floor of the building above the tunnel is 32.7 dB(A) and 28.4 dB(A) respectively when the near and far rail trains pass through. [ABSTRACT FROM AUTHOR]

The matching between multiple disciplines of urban rail transit is a key link to determine whether the design ability can be fully realized and the investment benefit can be brought into play. And the train speed is one of the crucial aspects and an important symbol for technical equipment, technical standards and operation management level of urban rail transits. For the problem of overlapping protection on speed control, this paper, by analyzing the influences of communication, signal, vehicle, track, environment and other restrictions on the train speed of fully automatic operation (FAO) lines, puts forward a speed matching control scheme under different operation modes such as ATO (automatic train operation) mode, ATP ( automatic train protection) mode, creep automatic mode and wash mode, discusses the method of determining the line structure design speed, vehicle construction speed, vehicle overspeed protection trigger speed, vehicle maximum dynamic envelope calculation speed, maximum ATP ceiling speed, and maximum ATO target speed according to the line grade speed, and defines the key speed recommendations of the matching design of multi disciplines. Meanwhile, by means of simulation and combined with the setting of different station spacing and the calculation of the minimum time speed curve, the design and selection principle of station spacing for lines with different speed grades ( i. e., 80, 100, and 120 km/ h) is investigated, and it is recommended that the distance between the three stations be no less than 1.0, 1.4 km and 2.5 km respectively. The optimization design of multi disciplines speed control and matching is helpful to maximize the performance of the system and equipment and ensure the overall operation level of the line. [ABSTRACT FROM AUTHOR]

With the development of social economy, improvement of living standards and changes in lifestyle concepts, people's yearning for tourism has promoted the vigorous development of China's tourism industry. In order to meet the higher demands of tourists for tourism quality, the combination of tourism and rail transportation has emerged. At present, tourism rail transit is an emerging and relatively popular thing, but there is no corresponding classification, design, construction and acceptance standard. It is urgent to clarify the rail transit system suitable for different tourism resources for the selection and reference of construction units. This article analyzes the classification of tourist attractions, its demand and suitable rail transit system. It focuses on the adaptability of the cross seat monorail system in tourist rail transit, and concludes that the cross seat monorail system has a vehicle appearance that integrates with the environment, a transportation capacity that is suitable for tourist flow characteristics, a flexible vehicle composition, lightweight rail beam bridge structure, strong terrain adaptability, excellent environmental performance and engineering construction costs that meet investor requirements, making it a very suitable system for tourist rail transit. Finally, by analyzing the differences between cross seat monorail as a tourism rail transit and urban rail transit, it is proposed that research on cross seat monorail as a tourism rail transit should be conducted in the development of new vehicles, equipment configuration that matches the operational needs of tourism monorail, system integration, and formulation of tourism rail transit technical standards, providing reference for the promotion and implementation of tourism cross seat monorail transit in the future. [ABSTRACT FROM AUTHOR]

Focusing on the problem of energy-efficient operation of urban rail transit, an energy-efficient control strategy of urban rail transit based on Sarsa reinforcement learning algorithm is proposed. To achieve the driving strategy of reducing energy consumption while taking into account punctuality and comfort when the urban rail transit is in automatic driving mode and facing different road conditions, the running state of the train is discretized and the continuous driving process is divided into several subsections according to the line conditions. Combined with the speed limit, initial state and terminal state of the track, the appropriate reward functions are constructed based on energy consumption and running time. At the same time, the setting of optional speed is limited by the maximum speed and minimum speed that can be reached in the current state, which reduces the exploration space and accelerates the convergence of the algorithm. Finally, the simulation is carried out for the case of Xiaohongmen-Xiaocun Station on the Yizhuang Urban Rail Line in Beijing. The experimental results show that, compared with the traditional dynamic programming method, the Sarsa algorithm saves 9.32% energy while meeting the requirements of comfort and punctuality. Compared with the Q-learning algorithm in reinforcement learning, the number of overspeed also decreases significantly in the process of speed selection. Simulation results show that the Sarsa algorithm has a better energy-saving effect and security. With the algorithm parameters unchanged, the speed limit conditions are adjusted, and compared with the traditional dynamic programming method again, it still saves 4.21% energy, which verifies the robustness of the algorithm. [ABSTRACT FROM AUTHOR]

The different rail transit systems, such as regional railway and urban rail transit, cooperate to form the syncretic railway network (SRN). With the rapid development of SRN, the limited transportation capacity is inadequate to meet the booming passenger flow during peak hours, where cascading failures caused by large passenger flow become a threat to SRN. This paper adopts a state equation to depict the failure‐restoration process and investigates detailed restoration strategies considering the characteristics of recoverable and repeatable failures of stations. Specifically, three different restoration strategies—spontaneous restoration strategy (SRS), active restoration strategy (ARS), and hybrid restoration strategy (HRS)—are proposed, and the varying effects of restoration time, restoration probability, restoration objective, and restoration priority for SRN with ARS are compared. These restoration strategies are applied to the actual SRN in Chengdu, where it is found that HRS has a better effect than other strategies. Furthermore, stations in the metro network with higher passenger flow allocate more restoration resources to improve the robustness of SRN, while the restoration effect of SRN increases noticeably with the restoration coefficient and the reserve coefficient. The restoration strategies presented in this paper can improve the safety management of SRN. [ABSTRACT FROM AUTHOR]

As an efficient, environmentally friendly, and economical urban public transportation tool, the subway has significant advantages compared to other urban public transportation tools and plays an irreplaceable role in the urban rail transit system. While the subway brings many conveniences to people, its operation has also brought a series of problems to urban development and residents’ lives, especially the vibration and noise problems generated by the subway have become unavoidable and urgent problems to be solved. The traditional wave barriers have a certain effect on attenuating and suppressing the vibration generated by the subway, but it has shortcomings in terms of the width and quantity of elastic wave bandgap (BG), so its application in low-frequency vibration reduction and isolation of subway systems is greatly limited. In order to broaden the elastic BG width and quantity of the wave barrier, this paper designs a novel phononic-like crystal subway wave barrier (PLCSWB) for subway vibration isolation based on locally resonant theory. Firstly, the dispersion curves of the novel PLCSWB are calculated using the improved plane wave expansion method and finite element method (FEM), and compared with the dispersion curves of traditional phononic crystal (PC) wave barriers. Secondly, the frequency response function of the novel PLCSWB is calculated using FEM to evaluate its attenuation effect on vibration, and the energy distribution characteristics at its BG boundary are analyzed. Then, the main factors affecting the BG of the novel PLCSWB are analyzed, and a spring–mass system equivalent model of the novel PLCSWB is established to theoretically estimate its BG range. Finally, the vibration data of the tunnel wall monitored on site are used to analyze and verify the vibration isolation effect of the novel PLCSWB. The results show that the novel PLCSWB opens five low-frequency BGs in the 200Hz frequency band, which increased the total width of the opened BGs by 49% compared to traditional PC wave barriers. In the frequency range of the BG, the attenuation value of the novel PLCSWB to vibration is mostly above 30dB, and the energy distribution inside the structure is mainly concentrated in the primitive cell that controls the BG, indicating good attenuation and control effects on vibration. The main factors affecting the BG of the novel PLCSWB are the density of the scatterer material, the elastic modulus, and the thickness of the wrapping layer material. Moreover, during subway operation, the novel PLCSWB has a good attenuation effect on the vibration transmitted from the tunnel wall to the soil. Among them, the maximum vibration acceleration amplitude of the novel PLCSWB with nine rows is reduced by 47%, and the vibration isolation performance is remarkable. The relevant research results of this paper provide a novel approach and method for the study of subway vibration reduction and isolation, and can also provide specific references for the design and development of wave barriers. [ABSTRACT FROM AUTHOR]

Urban rail transit network planning is a complex issue that involves multiple considerations, including traffic flow, passenger demand, geographical environment, economic costs, environmental protection. To address the pressing issues of inadequate station coverage, protracted planning timelines, and suboptimal passenger satisfaction in conventional urban rail transit network planning methodologies, the paper delves into the application of a Deep Dual Q Network in urban rail transit network planning. By initially analyzing the distribution density of Points of Interest (POI), the study employs the K-means clustering algorithm to meticulously select optimal locations for urban rail transit stations. Subsequently, a tailored urban rail transit network planning model is constructed, incorporating the site selection outcomes and delineating its pertinent constraints. Leveraging the prowess of a Deep dual Q Network, the model is efficiently solved, yielding an optimized urban rail transit network planning scheme. Experimental validation reveals remarkable outcomes, with a maximum station coverage rate of 91.9%, a minimized planning duration of 23.6 minutes, and a pinnacle passenger satisfaction rating of 98.1%, underscoring the practical efficacy and significance of the proposed approach. [ABSTRACT FROM AUTHOR]

Regarding alleviating urban traffic pressure and conserving environmental energy, it is essential to utilize the redundant capacity of urban rail transit during non-peak hours for passenger-freight collaborative transportation. In order to achieve the "dual carbon" goals in urban rail transit, a study has been conducted on the coordinated transportation of passengers and goods, which can adapt to the evolving carbon emission policies. A method for calculating the waiting time that allows passengers to queue for a second time has been proposed by studying the queue of passengers. A multiobjective optimization model for urban rail transit passenger-freight collaborative transportation has been constructed, with the objectives of minimizing carbon emissions and passenger waiting time, considering constraints such as carbon emission policy, train operation safety, passenger and freight loading balance, and train formation scheme. An improved nondominated sorting genetic algorithm (NSGA-II) has been designed to deal with the complexity of solving the multiobjective model. The algorithm utilizes the filtering function of the constraint conditions during the initial population generation process, effectively balancing the relationship between the filtering and time consumption of the NSGA-II algorithm. Taking the Wuxi Metro Line 3 as an example for analysis, the results show that: ① Although the average passenger waiting time increases by about 0.53 minutes in this method, the carbon emissions of the trains decrease by approximately 21.3%. Meanwhile, the average seating rate of the optimized solution is 5%~15% higher than the traditional transportation scheme used by Wuxi Metro Line 3. The superiority of urban rail transportation for passenger-freight collaborative transportation has been verified. ② With the mandatory control of carbon emission control, the passengerfreight collaborative optimization scheme is not affected by different carbon emission limits. Under the carbon tax policy, transportation schemes are less affected by different carbon tax levels. Transportation schemes remain consistent under the different limits of carbon trading policies. The proposed model and algorithm provide decision-making support for urban rail transit operators to optimize transportation plans under different carbon emission policies. [ABSTRACT FROM AUTHOR]

Urban rail transit systems constitute a crucial component of modern transportation engineering, where accurate predictions of station-level passenger flow are essential for optimizing the efficiency of rail transit operations. Traditional spatio-temporal models, such as graph-based models and recurrent neural networks, typically use static approaches. These methods often require high-dimensional parameter spaces to capture the complex dynamics of urban rail systems, resulting in high computational costs and low model efficiency. Moreover, it is difficult for traditional graph learning models to dynamically adjust their focus when capturing changes and subtle patterns, finally failing to fully utilize heterogeneous information between nodes. As a result, these limitations prevent models from adapting to the evolving characteristics of urban rail passenger flows and identifying fine-grained patterns with precision. To address these challenges, this study presents the DualGraph Attention-Neural Controlled Differential Equation (DGA-NCDE) model, which combines local geographical associations and global semantic associations through a novel Dual-Graph Attention (DGA) Module. The DGA Module is seamlessly integrated into the Neural Controlled Differential Equation (NCDE) framework, enabling the model to dynamically adapt to urban rail passenger flow patterns. To validate the effectiveness of the DGA-NCDE model, comprehensive experiments were conducted on two largescale public datasets, HZMetro and SHMetro. When compared to the best baseline models, DGA-NCDE reduces MAPE by 3.44% on HZMetro and by 2.27% on SHMetro, while using just 1.12% of the parameters. In addition, the DGA-NCDE model significantly cuts the training time by approximately 51.31%, and the inference time by about 87.23%, demonstrating the significant improvement in both accuracy and efficiency of the DGA-NCDE model. [ABSTRACT FROM AUTHOR]

Accurate short-time passenger volume prediction can guarantee the efficient scheduling command of urban rail transit. However, the short-time passenger flow of rail transit has the characteristics of nonlinearity and high randomness. In order to improve the prediction accuracy of short-time passenger volume, the Seasonal-Trend decomposition using Loess (STL) method and Human Evolutionary Optimization Algorithm (HEOA) is employed to optimize the bi-directional long short-term memory neural network (BiLSTM). Thus, a combined STL-HEOA-BiLSTM prediction model is proposed. Firstly, the inbound passenger volume along the urban rail transit is classified according to the Pearson correlation number. Secondly, the STL algorithm decomposes different types of short-time passenger flow data into Trend component (Tt), Seasonal component (St) and Residual component (Rt). Thirdly, the HEOA optimizes the various types of hyper-parameters of the BiLSTM model. Finally, the optimized BiLSTM model predicts Tt, St and Rt individually, and the final prediction value is obtained based on the combination of the three predictions. Three evaluation metrics are used to assess the results and quantify the effectiveness of the combined model. The example analysis shows that the prediction accuracy of the combined STL-HEOA-BiLSTM model surpasses that of the other six common and combined models in forecasting short-duration passenger flow. This experimental result shows the effectiveness, accuracy and applicability of the STL-HEOA-BiLSTM model proposed in this paper. It is demonstrated that the proposed model has a reference value for urban rail transit operators. [ABSTRACT FROM AUTHOR]

This paper incorporates the shortest passenger travel paths into the first train timetabling problem in urban rail transit networks. We first propose a path-based first train timetabling problem, which is formulated as a mixed-integer nonlinear programming model to reduce the travel time for passengers. To solve this model, we present a tailored branch-and-bound combined with the timetable-based Dijkstra algorithm. To further enhance the efficiency of the exact algorithm, we design a rolling time heuristic algorithm as a substitute for the branch-and-bound algorithm. Then, we proved that the rolling time heuristic combined with the timetable-based Dijkstra algorithm achieves 97.86% and 99.39% reductions in CPU time while sacrificing only 0.007% and 0.057% of optimality gap by two numerical experiments. Finally, the results based on two test networks with different structures demonstrate that the path-based first train timetabling model effectively improves passenger travel efficiency. Specifically, the travel times are reduced by 744 mins and 761 mins, while the transfer waiting times are reduced by 37.87% and 58.91% compared to the existing timetables. Moreover, the results also demonstrate that loop line networks will increase the difficulty of timetabling and lead to many passengers taking detours. To further enhance the efficiency of commuting for passengers who take the first train, URT operators may consider relaxing the limitations on the departure time of the depot stations. • A path-based first train timetabling model to minimize the total travel time in urban rail transit networks. • A tailored branch-and-bound combined with a timetable-based Dijkstra algorithm. • A rolling time heuristic algorithm to solve large-scale networks cases. [ABSTRACT FROM AUTHOR]

Public transportation usage in Delhi has declined, with the Delhi Metro having a significant share. However, due to fare hikes and COVID-19 restrictions, the DM's share has been decreasing further. To improve ridership, a study is being conducted to evaluate the DM's performance and identify areas for improvement in passenger convenience and comfort. The Magenta line is investigated through an on-board survey to collect primary data. The survey covers commuter perceptions of safety & security, financial & economic factors, infrastructure & comfort and functional & operational features. The Relative Importance Index approach is used to analyse the data and evaluate DM performance. An ANN model is also presented to determine the factors influencing the choice to travel on the DM, with the "metro fare per trip" factor being a key consideration. Based on the analysis results, recommendations are made to improve the DM's performance. The study found that safety and security had the highest RII, followed by efficiency and viability, functional and operational features, infrastructure and comfort, and financial and economic factors. The subway fare had the lowest RII. The ANN model is adapted to understand the reasons behind low metro ridership. [ABSTRACT FROM AUTHOR]

The study aims to critically evaluate the Kochi Water Metro as a model for sustainable urban transportation and extract valuable lessons for policymakers and cities seeking similar initiatives. The study employs a qualitative data-driven approach, utilising a longitudinal case study design, open-ended interview questions, direct observations and qualitative content analysis. The Kochi Water Metro has emerged as a transformative urban mobility initiative, enhancing connectivity while simultaneously reducing congestion on roads. The project's sustainable practices have minimised its carbon footprint, contributing to environmental sustainability. Economic benefits have accrued through increased tourism, employment opportunities and improved local livelihoods. The project's adaptability to local conditions and changing needs further underscores its sustainability. Limited data from the project's early years necessitate further longitudinal analysis. The focus on benefits may overlook potential challenges, warranting broader investigation. Reliance on qualitative data may restrict generalisability. The Kochi Water Metro serves as a beacon for sustainable and equitable transportation solutions. It serves as a blueprint for policymakers, providing practical lessons in project implementation, especially its integration with existing modes enhances overall urban mobility. Policymakers should consider replicating elements such as electric boats and integrated networks, while emphasising environmental sustainability, accessibility and community engagement. The comprehensive evaluation provides valuable insights into the project's tangible impacts and replicable elements. The innovative approach blends sustainable practices, operational resilience and community engagement, serving as a model for cities seeking sustainable, equitable and efficient transportation solutions. [ABSTRACT FROM AUTHOR]

Short-term passenger flow prediction is a critical component of urban rail transit operations. However, predictions of passenger flow are mostly focused on one station, and land use, which has a substantial impact on passenger flow variation, has not been taken into account. A model termed the temporal-spatial network long short-term memory model (TNS-LSTM) is developed to solve the forecasting gap for the metro inbound/outbound passenger flow. The model introduces the spatial characteristics of the land use by extracting the point of interest (POI) data instead of merely considering temporal characteristics and network characteristics. The spatial-temporal network matrix is designed through the K-Means clustering model, extraction for temporal characteristics analysis for land use, and establishment of an origin-destination station matrix. Furthermore, the prediction of short-term passenger flow is implemented for multiple stations in the metro network. Finally, a case study based on actual data from the Nanjing metro is carried out, and the results demonstrate that the proposed model can not only avoid the complexity of constructing the numerous models for each station in urban rail transit but also improve the prediction accuracy and save a substantial amount of time. [ABSTRACT FROM AUTHOR]