Metro Networks Research Articles

Disaggregation and virtualization for future access and metro networks [Invited Tutorial

Future access and metro networks are expected to provide advanced broadband services and the evolution of mobile x-haul in a flexible manner. This paper first reviews the progress and challenges of disaggregation and virtualization technologies to meet this expectation with a focus on their application to optical access networks. Next, it describes future access and metro integrated networking in which disaggregation and virtualization technologies will play important roles.

Node Importance Evaluation of Urban Rail Transit Based on Signaling System Failure: A Case Study of the Nanjing Metro

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.

Flexible and Efficient Network Slicing for Integrated Optical Metro Networks With Diverse Access Applications

Flexible and Efficient Network Slicing for Integrated Optical Metro Networks With Diverse Access Applications

Spatially Varying Effect Mechanism of Intermodal Connection on Metro Ridership: Evidence from a Polycentric Megacity with Multilevel Ring Roads

Understanding the spatially varying effect mechanism of intermodal connection on metro ridership helps policymakers develop differentiated interventions to promote metro usage, especially for megacities with multiple city sub-centers and ring roads. Using multiple datasets in Shanghai, this study combines Light Gradient Boosting Machine (LightGBM) with Shapley additive explanations (SHAP) to explore these effects with the consideration of the built environment and metro network topology. Results show that the collective impacts of intermodal connection are positive, not only within the main city but also alongside the main commuting corridors, while negative effects occur in the peripheral area. Specifically, bike sharing trips increase metro ridership within the inner ring of the city, while bus services lower metro usage at stations alongside the elevated ring roads. Parking facilities enable metro usage at city sub-centers, and the small pedestrian catchment area increases metro riders alongside the main commuting corridors. Empirical findings help policymakers understand the effect mechanism of intermodal connection for stations in different regions and prioritize customized planning strategies.

A geographic-semantic context-aware urban commuting flow prediction model using graph neural network

Urban commuting flow prediction is crucial for urban planning, transportation optimization, and supply chain management. Traditional models and machine learning solutions often fall short in capturing the complex dynamics of urban mobility, overlooking factors like spatial correlation and human mobility patterns. Recent deep learning advancements have shown promise by integrating spatial correlation through incorporating geographical adjacency. However, this approach may not fully capture spatial correlation, particularly the semantic adjacency effect of zones connected via public transportation lines, such as metro networks—a novel dimension overlooked in prior research. The metro station serves as a major transit hub, attracting passengers from various origins and destinations and correlating the flow between the connected zones and other zones within the metro network. Devising an optimized model to encapsulate these spatial correlations is another crucial area of research where scientists are actively seeking the most effective solutions. We propose a novel sequential hybrid approach leveraging graph-based learning, focusing on the underexplored impact of metro networks on commuting flow prediction. Our model integrates two types of adjacency—geographic and semantic—using Graph Convolutional Networks (GCNs) to embed geographic correlations among proximate zones and Graph Attention Networks (GATs) to incorporate semantic adjacency defined by metro line connectivity. Furthermore, our approach innovatively incorporates features directly from online maps, eliminating reliance on third-party resources and enhancing scalability and efficiency. We validate our model using real-world datasets from Montreal, Canada, comprising travel surveys and GPS trajectory types, totaling nearly 900,000 trip records. Through evaluation using established performance metrics such as MAE, RMSE, MAPE, and CPC, we demonstrate significant improvement in predictive accuracy compared to existing state-of-the-art models. Our research has practical implications for urban planning and infrastructure development, aiding policymakers and urban planners in analyzing the impact of new urban infrastructure on commuting flow dynamics and facilitating informed decisions regarding transportation and infrastructure planning.

Last-train timetable synchronization for service compatibility maximization in urban rail transit networks with arrival uncertainties

The last train services of urban rail transit offer all passengers the final chance to reach their destinations. In the context of multimodal transport, considering the random delayed arrivals of late-night vehicles of other transport modes at urban hub stations and their adverse effects on multimodal passengers transferring to urban rail transit, a probabilistic scenario set is established to determine the arrival uncertainties of multimodal passengers. In each scenario, the timetable synchronization problem is formulated as a mixed-integer nonlinear programming model that requires a performance trade-off between destination reachability and the remaining path distance of both non-multimodal and multimodal passengers, integrally termed last train service compatibility. Through linearization techniques, the model is transformed into an equivalent mixed-integer linear programming form, which can be efficiently solved using the Gurobi solver to obtain the optimal last train timetable for each scenario and form an alternative scheme set. An improved probabilistic scenario set-based regret value theory is then developed, in which a novel regret value calculation method is proposed. The scheme with the minimum total weighted opportunity loss in all scenarios is selected as the optimal robust. Real case experiments based on the Chengdu–Chongqing high-speed railway line and Chengdu metro network are conducted to test the performance of our model. The results show that compared with the original timetable, the optimized timetable reduces the number of unreachable passengers by 34.29% and the sum of the average remaining path distance of non-multimodal and multimodal passengers by 57.43% with the help of path planning for all passengers. The proposed approach is proven not only to balance the demands of both reachable and unreachable passengers, but also to significantly improve the robustness of the last train timetable to arrival uncertainties of multimodal passengers and reduce their service inequity.

Collaborative train timetabling and passenger flow control in oversaturated metro lines considering state-dependence

Congestion downgrades the moving speed of passengers in trains and platforms. This in turn affects congestion. That is, the moving speed of passengers interacts with congestion. It is state-dependent and obeys the fundamental diagram. This interaction makes the system unstable and can result in substantial passenger delays and heightened security risks in oversaturated metro lines. This study aims to rectify this by developing a more realistic collaborative optimization approach. Firstly, we formulate a deterministic fluid queuing network model to accurately capture the dynamic propagation of congestion in oversaturated metro lines. This model incorporates the fundamental diagram, thereby effectively considering the interaction between passenger movement speeds and congestion. A recursive algorithm is devised to solve the fluid queuing network model. The complexity of the algorithm is independent of the capacities of the stations and trains. This provides the possibility of analyzing large-scale metro networks. Based on the queuing network model, a mixed-integer nonlinear programming model aimed at minimizing the total passenger waiting time is built. To solve the model effectively, we employ an alternating direction method of multipliers framework to decompose the model into nonlinear programming and integer nonlinear programming problems, which are respectively solved by improved simultaneous perturbation stochastic approximation and adaptive large neighborhood search algorithms. Finally, a small-scale case and a real-world case with data from Chengdu metro lines are implemented to demonstrate the performance and effectiveness of the proposed approach. The experimental results provide some interesting insights. (1) Without consideration of state-dependence, the queuing model underestimates the passenger waiting time and overestimates the service levels. (2) As demand increases, excluding extreme oversaturation scenarios, the optimized results of the state-independent model progressively deteriorate compared to our state-dependent model, with the largest percentage decline being 50.23%. This is especially noticeable when the train capacity is limited. (3) The contribution ratio of passenger flow control in mitigating congestion gradually surpasses that of train timetabling with increasing demand. In the small-scale case, the former contribution ratio to optimization rises from negligible to 98.19%.

Optimized design of horseshoe-and-spur filterless networks leveraging point-to-multipoint coherent pluggable transceivers

Cutting-edge network architectures and solutions are needed to empower operators to address capacity demands in metro and access networks efficiently. The horseshoe topology, along with similar topologies, is commonly employed in metro-aggregation segments due to its compatibility with the hub-and-spoke traffic pattern present in these networks and the survivability that they can provide. A filterless architecture can enhance cost-effectiveness by replacing active elements with passive components. Moreover, supporting coherent-based point-to-multipoint transceivers—enabled by digital subcarrier multiplexing (DSCM)—can yield additional cost savings. It is noteworthy that telecommunication network topologies often evolve to accommodate more end (leaf) nodes, extending the original horseshoe with spurs or small and short trees. This paper targets these types of networks and proposes combining the utilization of coherent pluggable transceivers leveraging DSCM to guarantee transparent communication between the hub and leaf nodes while adopting different filterless node architectures with selective amplifier deployment. Moreover, it discusses the potential advantages of the architecture using an exact optimization framework tailored to various network sizes and scenarios, which accounts for the amplifiers’ placement and the available types of power splitters/combiners/couplers. The results demonstrate that strategically deploying add/drop and express amplifiers, along with optimizing coupler ratios, can effectively meet design requirements while minimizing the number of optical amplifiers required.

Collaborative rescheduling of train timetables to relieve passenger congestions in an urban rail transit network: A rolling horizon approach

At some urban rail transit (URT) stations, large activities, emergencies, or holidays usually lead to a significant surge in passenger flow demand quickly, known as Large Passenger Flow (LPF) events. The passenger congestion will spread quickly via transfer stations and affect other stations and lines in the URT network. This study develops a timetable rescheduling and coordinating method for the URT network under LPF events. Firstly, a collaborative adjustment model of train timetables with an implement backup vehicle strategy is formulated to simultaneously consider rescheduling and coordinating problems, to reduce the congestion influence for an URT network. Then, a rolling horizon approach is developed to divide the whole adjustment problem into several decision-making stages to ensure solution efficiency. In each decision-making stage, the influence of LPF propagation within the URT network is firstly evaluated. Based on the congestion evaluation results, the proposed method determines whether it’s necessary to adjust timetables of the LPF Line or other lines. The proposed method is applied to the Xi’an Metro network in China. The results indicate that the proposed method can effectively evaluate and adjust the train timetables for large URT networks under the LPF events.

Resilience analysis of metro stations integrating infrastructures and passengers

Metro stations are important infrastructures ensuring people's daily commuting with tremendous travels every day, but susceptible to disruptions posing challenges in the resilience of the metro system. Previous studies mainly contributed to the resilience of the whole metro network, neglecting the resilience methodology and analysis of important stations. Integrating the infrastructure characteristics and the evolvement of passengers, a metro station resilience analysis methodology is developed. The approach is demonstrated with the stations of the metro system of Xi'an, China. Results show that station resilience varies differently across stations dominated by infrastructure types and queuing delay. Based on the comparison analysis under different infrastructure failures, failures of auto-ticket gates would have the highest impact on station resilience. When incidents on one infrastructure last for a period of time over 15 min, the resilience of station would be reduced by 9.8%, and dramatic resilience reductions could be observed if there are short-time incidents within 5 min on more than five infrastructures of a station. The findings would have practical significance for the resilience improvement of metro stations in infrastructure planning and passenger flow control against metro incidents.

State-dependent multi-agent discrete event simulation for urban rail transit passenger flow

Urban rail transit passenger flow modeling is the foundation of urban rail transit planning, design, and operation. The motivation of this paper is to accurately and efficiently simulate passenger flow dynamics in urban rail transit systems. To this end, we propose a State-dependent Multi-agent Discrete Event Simulation (SdMaDES). The state-dependence (or congestion-dependence) means that the service abilities (or travel times) of bottleneck facilities (e.g. platform screen doors and transfer corridors) are not constant and they interact with the congestion dynamics. Specifically, we first establish a modular Multi-agent Discrete Event Simulation (MaDES), which includes four types of modules (passenger, train, station, and network modules) and three types of agents (passenger, train, and station agents). The logical connections and event-triggering modes between the modules are analyzed and defined, and thirty types of events are designed. The state-dependence is then captured by an Improved Social Force Model (ISFM), which adds an autonomous obstacle avoidance mechanism. The ISFM reproduces passenger movement behavior within bottleneck facilities of urban rail transit systems at the microscopic level. These state-dependent functions or general rules are explicitly formulated by fitting the results of ISFM and are subsequently applied to the proposed MaDES model, resulting in the SdMaDES. This integration aims to enhance the accuracy of the simulation. We conducted a real case from the Chengdu Metro network. Some interesting results are found. (a) The maximum number of boarding passengers in a train carriage is a complex nonlinear function that is dependent on the state (density inside the train carriage). This challenges the linear function commonly utilized in most studies. (b) Compared to the actual data, the proposed SdMaDES model shows a cumulative error of 9.85% after data smoothing, while the conventional MaDES model exhibits a much higher cumulative error of 21.9% after data smoothing. (c) As the overall traffic demand level increases, the gap between the two simulation models’ results is getting wider and wider due to the amplified nonlinear impact of congestion.

The Spatiotemporal Matching Relationship between Metro Networks and Urban Population from an Evolutionary Perspective: Passive Adaptation or Active Guidance?

With the operation of the first route in Xi’an City, the matching relationship between the metro networks and the urban population is a root factor affecting the utilization of rail transit facilities. The mismatch between the metro networks and the urban population has led to an imbalance between the supply and demand for rail transport, resulting in wasted urban infrastructure. Based on this issue, the research objective is to focus on the spatiotemporal variations of the matching relationship. Firstly, the topological network model abstractly extracted metro spatial distribution features, and the spatial autocorrelation model was adopted to identify the evolution characteristics of the metro networks and urban population. Secondly, this paper adopted a time-lagged regression model to demonstrate the action relationship from 2011 to 2021. Then, the compositive coordination index was utilized to assess the variation of the global matching relationship. Finally, the paper explored spatial heterogeneity through the coupling coherence degree attached to grid cells. The research results indicate that the Moran’s I value of metro elements decreased from 0.782 to 0.510 with the further complexity of topological networks, while the population was consistently high in spatial dependence with a Moran’s I value of around 0.75 during the decade. Based on the regression coefficients and significance, this paper verified the hypothesis that the metro networks and urban population had a positive time-lagged feedback effect in urban development. From 2011 to 2021, the compositive coordination index symbolizing the global matching relationship increased from 0.29 to 0.90, but the coupling coherence degree shows significant spatial heterogeneity in different grid units. Differentiated spatial planning strategies were proposed for varied areas to efficiently utilize rail transit, which may provide a reference for other cities with the same reality problem.

Transformer-Based Spatio-Temporal Traffic Prediction for Access and Metro Networks

Transformer-Based Spatio-Temporal Traffic Prediction for Access and Metro Networks

Measurement and Optimization of Metro Network Service Efficacy

Measurement and Optimization of Metro Network Service Efficacy

Research on relay safety output control method based on BP neural network

With the continuous improvement and expansion of the metro network, the issues of train safety capacity, transportation effi-ciency and maintenance cost will become increasingly prominent. As an indispensable switching and controlling device of train safety control system, the health status of relay is the foundation of low-cost, high safety and efficient development of subway. This paper designs and proposes a BP neural network based relay safety output control method, this method utilizes BP neural net-work on the basis of improved two-take-two hardware structure to do fault prediction and safety state management with real-time retrieved detection signals, finally uploads operation and mainte-nance logs to the platform to guide subsequent fault trouble-shooting and maintenance.

Commuter Experience: An Assessment of Metro-Train Comfort Amidst Operational Vibroacoustic Conditions

This study investigates the impact of different passenger rolling stock structures and train configurations on vibrations and noise generated during operation. Vibroacoustic measurements were performed during acceleration, constant speed, and braking phases to analyze the effects using statistical analysis according to the relevant standards, revealing a statistically significant relationship between the equivalent noise level and vibration dose. In the context of the expanding Warsaw metro network, which is adding new lines and modern rolling stock, trends in the development of metro rolling stock were analyzed using five different types of metro vehicles, from the oldest to the newest designs. Vehicle performance was ranked in the context of ride comfort using standards and combined vibration and noise measures. The research results allowed for a collective comparative assessment of the construction of individual types of passenger rolling stock in terms of vibroacoustic phenomena, thanks to which it is possible to assess the impact of modern solutions and the justification for investing in modern rolling stock. Newer trains generally record significantly lower vibration and noise levels. The difference between the oldest and the most modern vehicle types is 57% for the vibration acceleration level (0.08 ms−2 RMS) and 66% for the noise level (2.2 dB LAeq).

TBM excavation of São Paulo Metro Line 6 South in heterogeneous ground in urban area

AbstractThe São Paulo Metro is expanding its network in the São Paulo Metropolitan Area inhabited by 21 million people. Metro Line 6 connects the north of the city to the existing metro network further South. The construction of Line 6 currently represents the largest infrastructure project in Latin America, combining the excavation of tunnel boring machine (TBM) and conventional tunnels, cut‐and‐cover stations and a number of deep emergency exit shafts. This paper presents the main features of the TBM tunnel excavated in the South stretch through the Tertiary soils of the São Paulo Basin and through the challenging Embu Complex, which consists of a mixture of fractured rock, saprolite and residual soils. The alignment runs under the dense urban grid of São Paulo under high‐rise buildings and other infrastructure, such as existing Metro lines.

Robustness Assessment of the Metro System: A Case Study of Bucharest, Romania

Metro network systems play an important role in urban transportation but they are vulnerable to various risks and disruptions. Having a comprehensive understanding of these risks is essential for their operation. In this paper, we aimed to assess the operational robustness of the Bucharest metro system. We employed graph theory and concepts. Using available data specific to the Bucharest metro system operation, we analyzed the type of disruptions across metro lines and stations. As expected, the two subway lines that sub-cross the city center experienced the greatest number of disruptions. Several stations on Line M1, that sub-cross the central urban area, were found to be particularly vulnerable. In our analysis, the results are as follows: the robustness indicator for the metro network, rT, is 0.1538, and the effective graph conductance (CG) is 0.1. These reveal that the Bucharest metro network can withstand up to 45% of node failures before collapsing. This indicates moderate robustness, suggesting that while the network can handle specific disruptions, targeted improvements are necessary to enhance overall resilience.

‘An iron, pyjamas, an apron, a Pandora bracelet’: The social shadow of sexism in Italian adverts

This is an exploratory article engaging with an advertisement that appeared on the billboards of Milan’s metro network in December 2017. Investigating discourse as the prominent site for identifying the ideological assumptions embedded in language use and the existing social conventions emerging from them, the author argues that the use or frequency of sexist stereotypes does not necessarily mark a brand’s alignment with sexism. The methodology adopted is a form of Bakhtinian stylistics, i.e., an analysis of texts with a view to evaluate the addressivity and answerability of utterances and investigate their heteroglossic and interindividual nature as well as their positioning within a specific community, time and place. Based on a constructionist and critical sociolinguistics matrix, this study focuses on style meant as the particular ways an individual uses language as social practice through which ideological assumptions emerge.

Robust dynamic train regulation integrated with stop-skipping strategy in urban rail networks: An outer approximation based solution method

In dense urban rail networks with high passenger demands, uncertain disturbances occur frequently, and the resulting train delays will likely spread over the whole network rapidly, hence degrading the service quality offered to passengers. To cope with the uncertainties of frequent disturbances in urban rail networks, this paper proposes a robust train regulation strategy based on the information gap decision theory, which allows the operators to adjust the conservativeness of adjustment schemes flexibly by varying system performances but without the need for prior knowledge of uncertain disturbances. Specifically, considering the coupling relationship between train dynamic flows and passenger dynamic flows, a mixed integer quadratically constrained programming (MIQCP) model is constructed for the robust train regulation problem to generate solutions with immunity against disturbance uncertainties, in which the envelope-bound model is used to characterizing the uncertain sets of disturbances. To meet the real-time requirements of train operation adjustment, a tailored outer approximation algorithm incorporating a two-phase heuristics method is devised to effectively solve the developed robust train regulation model, thereby quickly generating high-quality solutions. Moreover, the warm start technique and domain reduction technique are carefully developed to accelerate the solving procedure. Numerical experiments based on the Beijing metro network illustrate the robustness of the proposed train regulation strategies and the effectiveness of the designed solution approach.