Metro Networks Research Articles

Analysis of the Complex Characteristics of the New York City Subway Network

Complex networks are important to the real world. Complex networks are instructive for the study of real networks such as metro networks, social networks and information networks. This paper study the New York City subway network and use complex network theory to research the static properties of subway complex network. Conclude that 79% of the nodes in the New York City subway network have degree 2, the network diameter is 59, more than 90% of the network nodes have a clustering coefficient of 0, the standard robustness of the network is 0.326, and the average shortest path length of the network is 18.38. This paper use deliberate attacks and random attacks on the network to study the dynamic Properties. The New York City subway network sites have good robustness against random attacks. In contrast, the network exhibits vulnerability when the maximum mesonumber nodes are targeted for deliberate attacks, and the entire network will be nearly paralyzed when more mesonumber nodes fail.

Dynamic Metro Stations Importance Evaluation Based on Network Topology and Real-Time Passenger Flows

Identifying the importance of metro stations is crucial for enhancing the efficiency and safety of the urban rail transit system. This study proposes an integrated approach to evaluate the importance of metro stations based on jointly the static metro network topology and the dynamic real-time passenger flows. The static characteristics of metro stations are reflected through node degree and clustering coefficient of metro network topology based on the complex network theory. The dynamic characteristics of metro stations are assessed using the passenger flow time series, which were modeled using the autoregressive integrated moving average plus autoregressive conditional heteroscedasticity model (i.e., ARIMA+GARCH model). By combining the static and dynamic characteristics of metro stations, a comprehensive index is proposed to quantify the importance of metro stations. The integrated approach is tested using metro network and passenger flow data collected from Suzhou, China. The results indicate that compared with the topology analysis, the passenger flows could strengthen the importance of transfer stations and their neighboring stations. Especially for the loop lines that formed by the intersection of two or more lines within the network, the importance index of metro stations within these loop lines is inversely proportional to the number of stations in the loop. It indicates that as a metro network continues to expand with the addition of new lines and stations, this is a high likelihood of increased concentration of importance with the network. The concentration of importance in the metro network can be attributed to the emergence of highly connected loop lines that facilitate efficient movement of passengers.

Short-Term Inbound and Outbound Passenger Flow Prediction for New Metro Stations Based on Clustering and Deep Learning

The rapid expansion of metro networks, e.g., in many cities of China, continuously introduces the operation of new stations every year. Due to the lack of historical data and complicate variations of short-term passenger flow in the early stage of operation, it is difficult to accurately predict inbound and outbound passenger flows of new metro stations in the short term, which would be the database for train scheduling for new stations before operation, dynamic capacity optimization for new stations under operation, short-term prediction of cycle sharing demands near new stations, and so on. Traditional methods usually failed to exactly reflect the complicate rules or were unusable without the new station’s historical data. In order to solve the above problems, this paper proposes a short-term inbound and outbound passenger flow prediction model for new metro stations at the early stage of operation by combining the K-means clustering algorithm, an improved spatiotemporal long short-term memory model (Sp-LSTM), and a real-time feedback error model (mean absolute error, MAE), where passenger flows’ spatial-temporal characteristics and land-use relevance are considered. The application in Guangzhou Metro, China, where Line 21 is regarded as a new line, shows that the proposed K-Sp-LSTM model has the best prediction accuracy compared with traditional methods.

Cost- and energy-efficient filterless architectures for metropolitan networks

Network operators are forced to find cost- and energy-efficient solutions for networks supporting new and emerging services with strict latency and ultra-high-capacity requirements. A disruptive approach for delivering network agility in a cost- and energy-efficient manner is employing filterless optical networking based on broadcast-and-select nodes and coherent transceivers. The filterless network concept has been widely studied for terrestrial and submarine applications. In this paper, we investigate the performance of filterless optical networks in metropolitan core and aggregation networks where agility is required due to service dynamics, customer changes, and service flexibility requirements. We compare our results with a conventional metro network based on active switching. The results show that the filterless metro network based on a hierarchical structure similar to its active switching counterpart has comparable installed first cost and spectrum usage at 11 Tb/s of total traffic, as well as cost and wavelength consumption advantages of 19.5% and 16%, respectively, at 107 Tb/s of total traffic. These results confirm that the filterless architecture is an attractive alternative for metro network deployments.

Last train timetabling with transfer accessibility in metro networks: integer linear programing model and schedule-based transfer network

Last train timetable plays an important role in actual operation of metro systems, especially in big cities. Appreciable transfer passengers rely on last train connections to reach their destinations, which requires a coordinated last train timetable. This study is devoted to dealing with the last train timetabling problem in metro networks, and proposes an integer linear programing model to coordinate last train connections at transfer stations to improve the transfer accessibility in terms of served transfer passengers and effective last train connections. Several measures at the planning level are used to guarantee feasibility of the optimized last train timetable when applied in practice. Then, the schedule-based transfer network and pre-process method are presented to shrink the scale of binary variables, which can expedite the solution process when solving problems in large-scale networks. Finally, some experiments based on the Qingdao metro network are carried out to verify the effectiveness of the timetabling method, and results show that dwell time and service ending time have direct influence on network accessibility. Comparison analyses show that the proposed model is able to generate a coordinated enough last train timetable with higher transfer accessibility and better practicality, which can provide support for operators to improve the last train service in a metro network.

A Network Fault Prediction-Based Service Migration Approach for Unstable Mobile Edge Environment

How to perform efficient service migration in a mobile edge environment has become one of the research hotspots in the field of service computing. Most service migration approaches assume that the mobile edge network on which the migration depends is stable. However, in practice, these networks often fluctuate greatly due to the fault of edge devices, resulting in unexpected service interruptions during the migration process. Besides, most of the existing solutions do not consider the migration cost and path selection in the event of edge network fault. Aiming at the above problems, we propose a service migration approach based on network fault prediction (SMNFP) for mobile edge environment. The SMNFP method first introduces the software-defined network as a global controller, which is used to monitor and collect the changing of the edge network and schedule the migration tasks. Second, a network fault prediction model based on Wide&Deep model is proposed to predict the upcoming faults in the network according to the alarm information of network equipment. Finally, the service migration problem is constructed as a Markov decision process, and a fault penalty function is introduced to avoid faulty nodes, together with the deep Q-learning method to solve the migration strategy. Simulation experiments are conducted on the public metro network fault dataset, and results show the proposed method can effectively predict network faults and complete service migration.

Megalopolitan-scale ground deformation along metro lines in the Guangdong-Hong Kong-Macao Greater Bay Area, China, revealed by MT-InSAR

As one of the most densely populated and rapidly growing metropolitan areas worldwide, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China has been developing extensive metro networks to relieve the escalating traffic congestion inner cities and to shorten public transport time inter cities. Metro construction possibly triggers ground deformation, which may result in damage to the tunnel, pipeline, and ground structures. Fast and efficient monitoring ground deformation along metro lines is vital not only to the metro itself but also to these structures. This study provides a comprehensive investigation of ground deformation along 67 metro lines within the GBA, based on multi-temporal synthetic aperture radar interferometry (MT-InSAR) by using Sentinel-1 from March 2017 to February 2022. The results reveal that deformation velocity in this area ranges from –39.4 mm/year to 14.2 mm/year, which is mainly caused by the tunnel excavation, adjacent excavation and construction, and unfavorable geological conditions. Furthermore, risk levels have been evaluated based on the InSAR-derived results with the proposed median absolute deviation heatmap clustering method. The risky area accounts for 16.31% (∼205.24 km2) of the whole study area in the GBA, among which 13.76%, 2.16%, and 0.39% are classified as three risk levels, with mean deformation velocities of –2.2 mm/year, –3.4 mm/year, and –6.5 mm/year, respectively. This study presents a comprehensive megalopolitan-scale ground deformation monitoring, for the first time, along metro lines in six cities of the GBA, which provides important insights into future metro line constructing, operating and planning.

Study on the robustness of Washington Metro Network

The Washington subway networks is taken as the research object. The space-L network topology model of the Washington subway network is constructed, and the complex network characteristics and robustness of the Washington subway network are studied by complex network theory. Firstly, by calculating different topological network statistics, the complex network characteristics of the Washington subway network are analyzed. Secondly, through the two simulations of random attack and deliberate attack on the Washington subway network, the index values of the two attack modes are counted and their changing trends are analyzed, and the robustness of the Washington subway network under attack is analyzed. The results show that the Washington subway network has strong robustness. Deliberate attacks are more destructive to the Washington subway network, while random attacks have less impact.

Spatially disaggregated model for self-channel interference in mixed fiber optical network segments

Coherent transmission technology is widely diffused in backbone optical uncompensated transmission (UT) network segments. In metro and access network segments instead, intensity-modulated direct detected channels at 10 Gbps requiring inline dispersion compensation units (DCUs) are still largely used. While the transition to coherent technology is foreseen in the dispersion managed (DM) network segment, in the meantime, it would be handy to route coherent channels through DM network segments to fully exploit deployed hardware and available spectrum and improve network flexibility. In this scenario, self-channel interference (SCI) exhibits strongly coherent accumulation due to the presence of DCUs, which is not well modeled by traditional analytical models for non-linear interference estimation used for path feasibility assessment. In this paper, we propose a semi-analytical model able to fill this gap. We show that our proposed model is able to provide a quick, conservative estimation of the SCI noise on both UT and DM systems, including optical line system configurations with mixed fiber types.

Trip planning for a mobility-as-a-service system: Integrating metros and shared autonomous vehicles

Mobility as a service (MaaS) integrates various transport modes into an on-demand and real-time platform, providing door-to-door service, and has received extensive attention. For MaaS, personalized trip planning is important but intractable. In this paper, we present a two-phase decision-support optimization framework for the problem of a MaaS system incorporating metros and shared autonomous vehicles (SAVs). First, a mixed integer programming model is proposed to optimize the routes of heterogeneous travelers considering five transport mode combinations, in which SAVs are regarded as not only a first- and last-mile connector to the metro but also a competitor. Next, the scheduling of SAVs and departure time of each traveler is determined with the purpose of minimizing the SAV operation cost. To apply the proposed framework to scenarios with real-time requests, we adopt the rolling horizon solution method, which includes four sub-modules. The method is evaluated on the Sioux Falls network, and the experimental results show that travelers become more sensitive to the mode choice as the additional time of the metro increases. In addition, the connectivity of the metro network has a considerable influence on the relationship between the metro and SAVs. The methodology can be useful for the trip planning of other transport mode combinations.

Studying the Joint Effects of Perceived Service Quality, Perceived Benefits, and Environmental Concerns in Sustainable Travel Behavior: Extending the TPB

The importance of the sustainability concept has gained increasing attention from scholars and practitioners. In this context, the aim of this study was to examine the determinants of intention to continue to use the new Metro in Qatar, which is considered a sustainable mode of travel. Therefore, in this paper, a special attention was paid to certain variables, such as environmental concern, perceived quality, and perceived benefits. A quantitative method approach was employed, in which data were collected based on self-administered questionnaires. A total of 1334 Qatar residents responded to the survey questionnaire, and Structural Equation Modeling (SEM) was used for hypothesis testing. The results confirmed the direct effects of perceived behavioral control, social influence, and attitudes on the intention of residents to continue to use the Metro network in their travel. Moreover, perceived behavioral control was found to play a mediating role, enhancing the indirect relationship between perceived benefits and intention to continue to use the Metro network. More importantly, environmental concerns significantly and positively affected the attitudes and perceived behavioral control of travelers; furthermore, these variables presented a mediated influence—through service quality—on the intention to continue to use the Metro network. This study enriched the sustainability behavior literature by examining the influences of crucial factors. Thus, this study is considered of great value for policy-makers who hope to maximize the awareness level of environmental sustainability among the population, in order to encourage them to embrace changes in their lifestyle towards becoming more environmentally responsible.

A Simulation-Based Model for Evacuation Demand Estimation under Unconventional Metro Emergencies

When metro networks are shut down due to an unconventional emergency, numerous passengers will get stranded and wait to be evacuated. A clear understanding of the stranded passengers in the network is the basis for designing an efficient evacuation strategy. Previous studies focused more on disruption management and assumed a known evacuation demand. Few studies considered the distribution of stranded passengers, especially when the metro network is shut down due to unconventional emergencies. This motivates us to develop a discrete event-based simulation model to estimate the number and distribution of stranded passengers. The proposed model takes the numbers of stranded passengers at stations and trains as the state variables and develops the state evolution rule of the metro system. The numbers and destinations of stranded passengers at trains and on stations at each time step, including the transfer and non-transfer stations, are calculated to describe the state evolution of the metro system. Chongqing Rail Transit (CRT) network is taken as an example. The result shows that the stations with the most stranded passengers are located downtown. Origin-Destinations (ODs) with more stranded passengers have a relatively shorter travel distance, while ODs with fewer stranded passengers have a relatively long travel distance. The average directional disequilibrium factor of stranded passengers of ODs in the whole network is high, especially for those ODs with many stranded passengers. These findings reveal the distribution characteristics of stranded passengers and can provide significant assistance to an emergency manager who is designing an efficient emergency evacuation plan.

Last train timetable optimization for metro network to maximize the passenger accessibility over the end-of-service period

PurposeThis study aims to improve the passenger accessibility of passenger demands in the end-of-operation period.Design/methodology/approachA mixed integer nonlinear programming model for last train timetable optimization of the metro was proposed considering the constraints such as the maximum headway, the minimum headway and the latest end-of-operation time. The objective of the model is to maximize the number of reachable passengers in the end-of-operation period. A solution method based on a preset train service is proposed, which significantly reduces the variables of deciding train services in the original model and reformulates it into a mixed integer linear programming model.FindingsThe results of the case study of Wuhan Metro show that the solution method can obtain high-quality solutions in a shorter time; and the shorter the time interval of passenger flow data, the more obvious the advantage of solution speed; after optimization, the number of passengers reaching the destination among the passengers who need to take the last train during the end-of-operation period can be increased by 10%.Originality/valueExisting research results only consider the passengers who take the last train. Compared with previous research, considering the overall passenger demand during the end-of-operation period can make more passengers arrive at their destination. Appropriately delaying the end-of-operation time can increase the proportion of passengers who can reach the destination in the metro network, but due to the decrease in passenger demand, postponing the end-of-operation time has a bottleneck in increasing the proportion of passengers who can reach the destination.

Fifty years of operational research: 1972–2022

In July 2022, I received the EURO Gold medal at the 32nd EURO Conference held in Espoo, Finland. This paper is based on the presentation I made at the medal award ceremony. It covers 10 topics on which I have worked throughout my career. These are the seriation problem, rotating work schedules, deterministic vehicle routing, stochastic vehicle routing, examination timetabling, districting, waste management, metro network design, green transportation, and humanitarian logistics.

Clustering Analysis of Multilayer Complex Network of Nanjing Metro Based on Traffic Line and Passenger Flow Big Data

Complex networks in reality are not just single-layer networks. The connection of nodes in an urban metro network includes two kinds of connections: line and passenger flow. In fact, it is a multilayer network. The line network constructed by the Space L model based on a complex network reflects the geographical proximity of stations, which is an undirected and weightless network. The passenger flow network constructed with smart card big data reflects the passenger flow relationship between stations, which is a directed weighted network. The construction of a line-flow multilayer network can reflect the actual situation of metro traffic passenger flow, and the node clustering coefficient can measure the passenger flow clustering effect of the station on adjacent stations. Combined with the situation of subway lines in Nanjing and card-swiping big data, this research constructs the line network with the Space L model and the passenger flow network with smart card big data, and uses these two networks to construct the multilayer network of line flow. This research improves the calculation method of the clustering coefficient of weighted networks, proposes the concept of node group, distinguishes the inflow and outflow, and successively calculates the clustering coefficient of nodes and the whole network in the multilayer network. The degree of passenger flow activity in the network thermal diagram is used to represent the passenger flow activity of the line-flow network. This method can be used to evaluate the clustering effect of metro stations and identify the business districts in the metro network, so as to improve the level of intelligent transportation management and provide a theoretical basis for transportation construction and business planning.

Dynamic Influence Analysis of the Important Station Evolution on the Resilience of Complex Metro Network

With the flourishing development of the urban metro system, the topology of important nodes changes as the metro network structure evolves further. The identical important node has distinct impacts on various metro networks’ resilience. At present, the dynamic influences of important station evolution on the resilience of metro networks remain to be studied further. Taking Shenzhen Metro Network (SZMN) as an example, the dynamic influences of the structure evolution of important nodes on the resilience of the metro network were investigated in this study. Firstly, the dynamic evolution characteristics of complex network topology and node centralities in metro systems were mined. Then, combined with the node interruption simulation and the resilience loss triangle theory, the resilience levels of distinct metro networks facing the failure of the same critical node were statistically assessed. Additionally, suggestions for optimal network recovery strategies for diverse cases were made. Finally, based on the evaluation results of node importance and network resilience, the dynamic influences of the topological evolution of important nodes on the resilience of metro networks were thoroughly discussed. The study’s findings help us comprehend the metro network’s development features better and can assist the metro management department in making knowledgeable decisions and taking appropriate action in an emergency. This study has theoretical and practical significance for the resilient operation and sustainable planning of urban metro network systems.

Timetable rescheduling of metro network during the last train period

Non-last train passengers in the last train period experience issues with transferability and accessibility in addition to the final service of each line. Therefore, a specified schedule needs to be determined by operators with full consideration of passengers' travel demand for all the trains during this period. Aiming at minimizing the average transfer waiting time, failure transfer rate, and the negative effect of random passenger flow without incurring excessive costs to the operator, a model optimizing the train connection in the last train period is formulated under constraints of operation parameters and transfer passenger flow conditions. To depict the uncertain features of transfer demand, a random passenger flow generation method with stochastic simulation is incorporated into an improved quantum particle swarm algorithm (QPSA). Taking Shenzhen Metro network in 2019 as an example, the rationality of the optimized model, the feasibility of the quantum-behaved particle swarm optimization (QPSO) model, and its superiority over the conventional particle swarm optimization (PSO) model in solving this problem are successfully verified. The results show an 18.54% decrease in the average waiting time for transfer passengers with the optimized last train connections, along with an increase of 8.31% and 6.54% in the number of successful transfer passengers and directions, respectively, after optimization. The proposed QPSO model can reach convergence faster and without falling into local optima. The results also show that the average waiting time of transfer passengers can be significantly decreased after fine-tuning train operation parameters. A reasonable combination of objective weight factors (λ=10, μ=100) can reflect the game characteristics between passenger’s and operator’s preferences, while extending the service closure time within 20 min can further improve the overall service quality of last train period across the network.

Identifying Critical Stations Affecting Vulnerability of a Metro Network Considering Passenger Flow and Cascading Failure: Case of Xi’an Metro in China

Analyzing the importance grade of each metro station is the premise and foundation to ensure the safe and efficient operation of the metro network. This paper explores the identification method of critical stations in the metro network on the basis of considering the influence of passenger flow. Eight indicators were selected from both the static and dynamic aspects to construct the critical station identification index system considering the impact of passenger flow and cascading failure. Then, the triangular fuzzy analytic hierarchy process (TFAHP) method and critical importance through intercriteria correlation (CRITIC) method were used to calculate subjective and objective weight of all indicators, and the combined weighting method based on game theory was applied to calculate the comprehensive weight. The improved technique for order of preference by similarity to ideal solution (TOPSIS) method was developed to calculate the station importance and rank all stations according to their importance. Finally, the Xi’an metro network by mid-2021 was taken as the case study. The results show that there are 21 critical stations in the Xi’an metro network in three time periods (i.e., morning rush hours, evening rush hours, and nonrush hours) on weekdays and weekends, respectively, and those critical stations are divided into three levels. High centrality, high passenger flow intensity, and strong destructive ability are three important characteristics of critical stations in the metro network, and different critical stations have different importance characteristics. The Xi’an metro operation management authority needs to conduct a classification management scheme for those critical stations. The conclusions can provide the basis for the metro operation management authority to formulate accurate station management scheme, so as to increase the robustness of the metro network.

Improving of Interaction of Routes at High Density Stops by Adjusting Bus Arrival Time

Abstract A significant part of the movement of the population in large cities is carried out by bus transport. Higher accessibility and connection to the network of other types of public transport are the main features of bus transport. The need for mass passenger transportation in Baku mainly is met by the metro and bus transport. A small number of metro stations and insufficient coverage of the city territory by metro network have significantly increased the load on the bus service. With the increase in passenger traffic, the frequency of bus routes has increased, and the intervals have decreased. As a result, bus traffic on some streets has become more intense. This led to an increase in the number of buses arriving at the stops at the same time and the formation of queues in front of the stops. To solve the problem, the article proposes a method for coordinating bus schedules of different routes and adjusting the time of arrival at a stop without changing the intervals. As a result of applying the proposed methodology, by modelling the sequence of bus arrivals in the Anylogic program, a reduction in queues and loss of time in front of the bus stop is justified. Simulation testing was carried out for the scenario of bus movement both in the general flow and along the bus lane.

Photonic Gateway and Protocol-Independent End-to-End Optical-Connection Provisioning in All-Photonic Metro-Access Converged Network

The All-Photonics Metro-Access Converged Network (APN) aims to achieve a flat and simple architecture that even covers access areas and represents the evolution of optical transport. Electrical processing functions such as exchange, multiplexing, and switching are uniformly placed at the border of the network segments, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> access, metro, and core networks, in current optical networks. These functions are placed in Data Centric Infrastructure (DCI) and accessed only when needed in the APN. By flexibly providing direct optical connections between user premises and/or DCI according to requests from users and services, the APN creates new types of use cases requiring end-to-end guaranteed wide-bandwidth and/or extremely low latency and jitter along with an information-computing platform. To provide optical paths originating from user terminals that launch client signals using protocols preferable to their applications, we propose a configuration for a novel optical edge node, the Photonic Gateway (GW), and a procedure based on remote control of user terminals to establish end-to-end optical paths that are independent of the client signal protocol. To verify these, we present results of a proof-of-concept experimental demonstration of establishing end-to-end optical paths. An auxiliary management control channel is employed in the protocol-independent access control method. We also describe future challenges facing the Photonic GW that must be addressed.