Metro Networks Research Articles

Research on the Performance Recovery Strategy Model of Hangzhou Metro Network Based on Complex Network and Tenacity Theory

Based on complex networks and resilience theory, the structural characteristics and post-disaster performance recovery process of the urban metro network are studied to determine the best repair strategy for metro network performance under different scenarios. Specifically: (1) The space-L method is used to model the Hangzhou metro network, and MATLAB software is used to calculate the characteristic parameter values of the Hangzhou metro network structure; (2) A model of the post-disaster resilience of the Hangzhou metro network was constructed, and network efficiency was used as the evaluation index of the resilience level and resilience of the metro network; (3) The performance recovery process of the metro network under different scenarios was simulated and the optimal recovery strategy of the post-disaster metro network was obtained. The results show that the degree values of the Hangzhou metro network nodes are all generally low; the average passage path between nodes is long and the nodes are scattered, which makes the convenience of residents’ travel low. In addition, the degree index and the betweenness have some influence on the recovery order of the failed nodes. Finally, the genetic algorithm solves the post-disaster optimal recovery strategy of the metro network with good results.

Syncretic K-shell algorithm for node importance identification and invulnerability evaluation of urban rail transit network

In this paper, the Syncretic K-shell algorithm is developed and applied to identify the node importance and evaluate the invulnerability in an urban rail transit network, which is based on the idea that the nodes removed later are more important than those removed former, the nodes with the same core value also have different importance. It has a very important strategic significance for optimizing network structure, improving operational efficiency, and providing targeted protection and designated accident prevention measures for stations with different importance. Syncretic Core Value, as the node importance recognition criterion of Syncretic K-shell algorithm, considers the influence of node degree, cumulative core value and syncretic weight of adjacent nodes on node importance. Firstly, we use space L to establish the indirect and non-weighted urban rail transit network. Then, the node importance recognition steps of Syncretic K-shell algorithm are introduced. Then, a numerical study of the proposed Syncretic K-shell algorithm is conducted, and the Degree Centrality, Betweenness Centrality, Closeness Centrality, Mixed Degree Decomposition and Classical K-shell are used as comparison approaches. The node anti-destruction under node failure (including intentional attack and random attack) is evaluated, and finally, an example study is conducted with the background of Chengdu Metro Network. The results show that the important nodes identified by Syncretic K-shell have a greater impact on anti-destruction performance. The research found that Chengdu Metro Network is more vulnerable to attack and easy to be destroyed under intentional attack. Finally, two coping strategies are provided. According to the research results, we can conclude that compared with the previous node importance recognition methods, Syncretic K-shell algorithm has five advantages: (i) it can distinguish the importance between nodes with the same core value; (ii) considering the global and local centrality of nodes, the Syncretic Core Value of nodes can better reflect the importance of nodes; (iii) it can recognize the importance of nodes in networks with the same degree value or without obvious aggregation characteristics; (iv) the time complexity of Syncretic K-shell algorithm can be used for the recognition of node importance in large-scale networks; (v) it can better distinguish and amplify the differences between nodes, and quickly and effectively rank the importance of nodes.

Performance assessment of a communication infrastructure with redundant topology: A complex network approach

The physical assets within a critical infrastructure system are pivotal to its efficient performance and protection and that of other dependent systems. This is particularly the case for communication systems where network protection strategies usually involve asset redundancy. Although such redundancy is well-modelled in the literature, there is a gap in knowledge from a network science perspective in terms of its implications for network modelling and performance assessment. This paper presents a multilayer complex network framework that takes into account the heterogeneity of the redundant infrastructure for realistic network modelling and further analysis, a step change from using a single network model. Key performance indicators (KPIs) for communication networks (i.e., latency and jitter, bandwidth and throughput, queue depth and packet drops) are redefined to evaluate key important features of a long-haul backbone network such as network capacity and average use. In addition, these KPIs are adapted to deal with the aforementioned redundancy and so inform network managers with values defined over a model closer to the real system. The paper analyses the use case of a nationwide core and metro network infrastructure of one of the main UK internet service providers. The results of the analysis of KPIs showcase the advantage of the proposed multilayer complex network framework over the traditional single network model. Critical network elements within different dimensions of a communication network are identified based on their performance for prioritising network management measures.

Resilience-based optimization model for emergency bus bridging and dispatching in response to metro operational disruptions.

Bus-bridging evacuation services can significantly enhance metro resilience during operational disruptions. A resilience-based optimization model was proposed to generate a bus bridging and dispatching plan. The objective of the model is to maximize the resilience index of evacuated passengers while meeting pre-established restrictions on operational indicators and resources. The proposed approach consists of three steps: representing an integrated network based on a hyper-network, generating candidate bus-bridging routes using the K-shortest paths algorithm, and solving the optimization model using a genetic algorithm to determine the optimal vehicle allocation among the candidate routes. The Nanjing metro network was used to demonstrate the proposed model. The results show that the average waiting time is the main reason for travel delays, especially in short-distance travel. Furthermore, the cycling strategy is beneficial for reducing the average travel delay and improving evacuation efficiency with limited vehicles. In particular, when resources are very limited, the vehicle cycling strategy may have significant advantages over fixed vehicles for servicing fixed lines. The proposed model could be widely used in emergency response to quickly and efficiently evacuate passengers.

Suppression of Nonlinear Optical Effects in DWDM-PON by Frequency Modulation Non-Coherent Detection

We propose a simple and cost-effective method, using a direct frequency modulation (FM) and noncoherent detection (NCD) scheme, to suppress the nonlinear optical effects in dense wavelength division multiplexed (DWDM) optical communication. The FM transmitter comprises a directly modulated distributed feedback laser and a saturable semiconductor optical amplifier. In the NCD receiver, an optical slope filter as the FM to intensity modulation (IM) signal convertor is placed before a conventional photodetector. Because the FM signal has more evenly distributed optical power, bit-pattern-dependent nonlinear effects are consequently suppressed. After analyzing the nonlinear effects in the FM-NCD system and traditional IM direct detection (IM-DD) system, we found that the minimum achievable BER of the proposed FM-NCD scheme is 40 dB smaller. Moreover, a 2 Tbps (10 Gb/s × 200 channels) capacity was achieved by the FM-NCD system in 100 km DWDM passive optical networks (PONs), which is twice the capacity of the IM-DD system (10 Gb/s × 100 channels) under the same condition. These results indicate that WDM-PONs with the cost-effective FM-NCD scheme are strong candidates for future broad access networks and show great potential for the combination of optical access and metro networks for future generations of PONs.

Discovering periodic frequent travel patterns of individual metro passengers considering different time granularities and station attributes

Periodic frequent pattern discovery is a non-trivial task to discover frequent patterns based on user interests using a periodicity measure. Although conventional algorithms for periodic frequent pattern detection have numerous applications, there is still little research on periodic frequent pattern detection of individual passengers in the metro. The travel behavior of individual passengers has complex spatio-temporal characteristics in the metro network, which may pose new challenges in discovering periodic frequent patterns of individual metro passengers and developing mining algorithms based on real-world smart card data. This study addresses these issues by proposing a novel pattern for metro passenger travel pattern called periodic frequent passenger traffic patterns with time granularities and station attributes (PFPTS). This discovered pattern can automatically capture the features of the temporal dimension (morning and evening peak hours, week) and the spatial dimension (entering and leaving stations). The corresponding complete mining algorithm with the PFPTS-tree structure has been developed. To evaluate the performance of PFPTS-tree, several experiments are conducted on one-year real-world smart card data collected by an automatic fare collection system in a certain large metro network. The results show that PFPTS-Tree is efficient and can discover numerous interesting periodic frequent patterns of metro passengers in the real-world dataset.

Maximizing synchronous transfer between metro and buses considering connection level

Transfer between the metro (underground railway system) and buses is standard in many cities, and the simultaneous arrival of these two modes can provide passengers with a smoother transfer experience. However, previous synchronous transfer studies rarely consider different public transport modes simultaneously. This paper presents an optimization model that maximizes the number of metro and bus network synchronizations considering the connection level. An improved algorithm that combines simulated annealing (SA) and artificial bee colony (ABC) is proposed. The best nectar source position in history is combined with the SA operation. The ABC and simulated annealing and improved artificial bee colony (SA–IABC) algorithms are used to solve the model using the Nanmendou metro station and the bus stations within 1.2 km around it in Fuzhou City, China. The results show that ABC and improved SA–IABC algorithms increase the synchronization times of the metro and bus network by 17% and 24% on average, respectively, without expanding the departure frequency.

Enabling hierarchical control of coherent pluggable transceivers in SONiC packet–optical nodes

Traditional metro networks are composed of packet switching nodes (i.e., routers) interconnected by optical transport links. In this scenario, the packet and optical domains are clearly separated, using dedicated controllers. Standalone muxponders/transponders will be replaced in optical metro and transport networks by the utilization of hybrid packet–optical nodes equipped with coherent pluggable transceivers. Thus, the traditional packet control plane is unable to manage and fully support the specific optical parameters required to configure such pluggable modules. Moreover, the coordination between the optical and packet layers within this hybrid node has not been standardized yet and requires careful design to enable effective management of connectivity services. This paper proposes two software-defined networking (SDN)-based hierarchical solutions to coordinate and control coherent pluggable transceivers in a multi-layer network exploiting hybrid packet–optical nodes. The two solutions have been designed and validated focusing on the pluggable module configuration and the communication within the SDN hierarchical architecture. An experimental testbed including two packet–optical nodes, running an extended version of the open-source Software for Open Networking in the Cloud (SONiC) operating system, is deployed to show the effectiveness of the two solutions, with a deep analysis of the time required to set up end-to-end connections spanning the packet and optical domains.

Ultra-dense, 1152-core, broadband multicore fiber link deployed in a metro network.

We have designed, developed, and deployed the world's first ultra-dense space division-multiplexing multicore fiber link in a conduit of a metro network. In a 10-mm-diameter fiber optic-cable, 288 4-core multicore fibers are arranged in 24 200-µm spiderweb collapsible ribbons. The multicore fibers are fusion-spliced to 576 fanout devices which provide conventional single-core interfaces at patch panels at both ends of the link.

Robustness assessments of urban rail transit networks based on user equilibrium with time compensation mechanism

Urban rail transit has become an important mode of urban public transportation and has been paid more attention in the past two decades due to the advantages of low pollution and high efficiency. However, traffic incidents frequently occur in urban rail transit, therefore the robustness must be investigated to improve operation safety. In this paper, the user equilibrium with time compensation mechanism is adopted to study the robustness of urban rail transit networks (URTNs) subjected to failures, and Shanghai metro network is considered as an example to prove the feasibility and effectiveness of the proposed scheme. Meanwhile, the average travel time, the average link-flow and the demand–supply ratio are used to assess the characteristic changes of URTNs subjected to three attack modes. Moreover, the critical nodes and links can be discovered based on the proposed model, and the attacks to these critical nodes and links can greatly decrease the robustness of URTNs. The simulation results show that URTNs have better robustness when suffering the node attack and the link-demand attack and possess worse robustness when suffering the overall-demand attack, and we find that the uneven distribution of passenger flow results in the low network utilization.

Assessment of the Noise Reduction Impact from Application Restrictions on Rail Dampers

In the last decades, rail dampers had become a commonly used rolling noise abatement measure at several mainline and metro networks. Rolling noise reductions outdoor/indoor up to 4/8 dB had been found. While the beginning of damper application was started with multiple test sections, infrastructure managers had raised installation restrictions from other related technology departments like signaling and communication. This is leading to defined sections where one rail has to remain partially free from dampers or the track has to remain completely free from dampers. These sections become relevant when the damper application is caused by noise tackling action plans. Therefore, the acoustic impact was assessed by a) laboratory test track analysis (partially free) and b) environmental noise prognosis software (completely free). It is shown, that also for a partially free rail, a cancellation of the noise reduction can be expected for single 1/3 octave frequencies. This can be relevant for the overall noise emission. The effect from the completely free rail is much larger due to noise increases at several immission points. Railway noise mitigation has to consider these results or, if possible, the rail damper technology has to be improved to avoid these restricted sections.

Hybrid Amplification Approach Towards Wideband Optical Communications

Optical amplification techniques using fiber nonlinearities have the potential to cover new bands beyond the C and L-bands. Among nonlinear amplifiers, the phase-sensitive amplifier has a noise figure that can ideally be 0 dB, and it can compensate for the additional noise resulting from the higher losses of new bands compared to C and L-bands in transmission fibers. In this paper, a hybrid amplifier that combines three nonlinear phenomena in highly nonlinear fibers, namely parametric wavelength conversion, Raman amplification, and phase-sensitive amplification, is proposed as a discrete amplifier to achieve sufficient gain in the new band while simultaneously minimizing amplified spontaneous emission (ASE). The combination of these phenomena helped to simplify the overall amplifier by eliminating the need for electrical phased lock loop and aligning the powers of the signal and idler and maximizing the pump power without signal/pump path separation. In addition, the fiber Bragg grating-based phase shifter reduced the total phase of the signals, idlers, and pump close to zero by temperature tuning. The prototype of the hybrid amplifier achieved >25 nm gain bandwidth, approximately 20 dB net gain, and the ASE level was suppressed. The proposed amplifier is suitable for applications in large-scale high-capacity networks, such as metro and long-haul networks.

Network-Scale Passenger Flow Forecasting Methods in URT Based on Similarity Measurement

Accurate passenger flow forecasting in urban rail transit (URT) could provide a vital reference for operators’ timely operation management. However, due to the enormous scale of the metro network, it is unwise to forecast passenger flows at the station scale individually. In this paper, a forecasting framework is proposed for network-scale forecasting tasks considering both accuracy and efficiency. There are mainly three stages in the forecasting framework. Firstly, three kinds of similarity measurements are presented regarding the adjacent similarity, geographic location similarity, and trend similarity. Secondly, three similarity graphs are formed by combining the three kinds of similarity measurements and flow time series. Thirdly, the multigraph network is applied to perform passenger flow forecasting. The experimental results indicated that the proposed method performs relatively accurately for the network-scale prediction with economic time costs. Specifically, the proposed model could acquire the feasible forecasts compared with the best disaggregate model using less than half of calculation costs, and above 10% reduction in root-mean squared error (RMSE) compared with the best aggregate model in the benchmark trials. Extensive contrast experiments were conducted to investigate the sensitivity and interpretability of the proposed model.

Probabilistic Evaluation Method of Transformer Neutral Direct Current Distribution in Urban Power Grid Caused by DC Metro Stray Current

With the coupling of the DC metro network and urban power grid, the DC metro stray currents flowing into transformers have caused serious DC bias. There is, therefore, a need to evaluate transformer neutral direct currents caused by the DC metro stray current. To achieve this, an evaluation model considering the coupling of DC metro network and urban power grid is proposed. In the modeling principle, metro lines are coupled with grounding grids of transfer stations. The DC metro system and the urban power grid are coupled with the soil and grounding systems. The train operations on DC metro lines are simulated using the Monte Carlo method. And, considering the conditions of one-way conduction devices (OWCDs) and over-voltage protection devices (OVPDs), transformer neutral direct currents are calculated. Moreover, two probability indexes are proposed to evaluate distributions of transformer neutral direct currents. The applicability of the proposed model is verified by comparing the calculation results with test data of the transformer neutral direct current. Using the evaluation model, transformer neutral direct currents are evaluated. The result indicates that substation supplying power to the DC metro network has a large probability of a high-level neutral direct current.

Designing Metro Network Expansion: Deterministic and Robust Optimization Models

Designing Metro Network Expansion: Deterministic and Robust Optimization Models

Machine learning enhanced next-generation optical access networks—challenges and emerging solutions [Invited Tutorial

Optical access networks are envisioned to become increasingly complex as they support more and more diverse and immersive services, each with a different capacity, latency, and reliability need. While machine learning has been touted as a silver bullet that will intelligently manage network operations and resources to meet these demands, as it had been anticipated for core and metro networks, there exist various challenges that need to be addressed to progress machine learning models from research to production. In this tutorial, we first aim to motivate the continued push to advance optical access networks and rationalize the use of machine learning in these networks. We then highlight the challenges that are especially amplified due to the traffic dynamicity and heterogeneity, data scarcity, and computation-resource constraints of optical access networks. We discuss emerging machine learning approaches that are being explored to address these challenges. Finally, we consider a fast and self-adaptive machine learning enhanced dynamic bandwidth allocation scheme in an illustrative future use case of supporting immersive human-to-machine communications over the mobile fronthaul of next-generation mobile networks.

Multi-View Multi-Attention Graph Neural Network for Traffic Flow Forecasting

The key to intelligent traffic control and guidance lies in accurate prediction of traffic flow. Since traffic flow data is nonlinear, complex, and dynamic, in order to overcome these issues, graph neural network techniques are employed to address these challenges. For this reason, we propose a deep-learning architecture called AMGC-AT and apply it to a real passenger flow dataset of the Hangzhou metro for evaluation. Based on a priori knowledge, we set up multi-view graphs to express the static feature similarity of each station in the metro network, such as geographic location and zone function, which are then input to the multi-graph neural network with the goal of extracting and aggregating features in order to realize the complex spatial dependence of each station’s passenger flow. Furthermore, based on periodic features of historical traffic flows, we categorize the flow data into three time patterns. Specifically, we propose two different self-attention mechanisms to fuse high-order spatiotemporal features of traffic flow. The final step is to integrate the two modules and obtain the output results using a gated convolution and a fully connected neural network. The experimental results show that the proposed model has better performance than eight other baseline models at 10 min, 15 min and 30 min time intervals.

Passenger flow analysis and emergency response simulation in a metro network using virus transmission model

ObjectivesThe potential virus in transportation facilities poses a serious risk to travelers. This research focus on the commuting by metro on the risk of the coronavirus disease 2019 (COVID-19). The main purpose is to explore the trajectory of virus transmission and the effectiveness of various control measures. MethodsA transmission model was established on the basis of the susceptible-infected-recovered (SIR) model, combined with the spatial and temporal characteristics of the metro passenger flow. The implementation effects of the emergency strategies were analyzed through a series of simulation experiments. The changes in passenger flow affected by the virus transmission were analyzed both under the single intervention condition of the disinfection or off-peak travel policy and their double interventions. ResultsThe results of the experiments show that disinfection and off-peak travel can effectively reduce the number of the infected people. To promote the disinfection is better than the off-peak travel strategy. The optimal solution is the combination of these two strategies, thereby reducing the infection rate in the stations effectively. In particular, it can reduce the number of potential infected people in high-traffic stations by 50%. ConclusionsThis study provides a scientific basis for the prevention of COVID-19 in the urban transportation system and the formulation of public emergency strategies. It can also be applied to other epidemic diseases such as the seasonal flu, for public health prevention.

Bipartite networks to enable management of internet core and metro infrastructure

In Telecommunications, core and metro network infrastructure provide high-speed, low-latency internet service to national-level areas. Their optimal operation and management are essential for today's increasing internet service demand. The usual representation of such infrastructure as a complex network considers router stations and other main internet sites as network nodes, connected by links that represent fibre-optic cables. However, this representation comes with important simplifications. One of them is that two nodes are connected by one single link when in reality there are involved multiple links in that connection. This paper explores bipartite networks to model the detail of multiple links connecting two network nodes, zooming-in process. To avoid dimensional complexities, the cable disentangles will only be in place at local sub-networks where the analysis may have a particular focus of interest. For instance, this process will support the decision-making associated to re-routing operations after a traffic anomaly has been detected. To show the efficiency of this multi-resolution network approach, the paper uses the case study of the core and metro infrastructure of one of the major UK internet service providers.

Shuttle Bus Line Planning in Response to Unexpected Disruption in Urban Rail Transit Networks

When metro systems need to be temporarily closed due to an unexpected disruption, the metro operators have to arrange shuttle buses as alternative transport for trains, while passengers have to find new routes to complete their journeys. However, the additional shuttle bus lines might be redundant if the well-known phenomenon of the “Braesss Paradox” exists. To address this issue, this paper proposes a bi-level programming model in which the objective of the upper-level model is to minimize the number of shuttle bus lines, while the lower-level model is a UE (User Equilibrium) model that simulates the route choice of passengers. To solve this bi-level programming model, the Overload Arc-oriented Heuristics (OAH) algorithm is developed to find an initial solution, and then we develop a BS (Bi-direction Search) algorithm to obtain the optimal solution to our model. Numerical cases of the partial Beijing Metro network have proved the efficiency and effectiveness of our method, and the result shows that: (1) compared with the standard bus bridging routes, the additional shuttle bus lines can significantly reduce the number of stranded passengers; (2) the number of stranded passengers at each station can be controlled by two strategies: adjust the departure frequency of each shuttle bus line and control the passenger flow at each station.