Metro Networks Research Articles

Exposure to PM2.5 on Public Transport: Guidance for Field Measurements with Low-Cost Sensors

Air pollution is one of the most important problems in big cities, resulting in adverse health effects. The aim of the present study was to characterize the personal exposure to indoor and outdoor pollution in the Greater Athens Area in Greece by taking measurements during a journey from suburban to mixed industrial–urban areas, encompassing walking, waiting, bus travel, and metro travel at various depths. For this reason, low-cost (LC) sensors were used, and the inhaled dose of particulate matter with an aerodynamic diameter of less than or equal to 2.5 μm (PM2.5) in different age groups of passengers was calculated. Specific bus routes and the Athens metro network were monitored throughout different hours of the day. Then, the average particulate matter (PM2.5) exposure for a metro passenger was calculated and evaluated. By considering the ventilation rate of a passenger, an estimation of the total PM2.5 inhaled dose for males and females as well as for different age groups was made. The results showed that the highest PM2.5 concentrations were observed inside the wagons with significant increases during rush hours or after rush hours. Furthermore, there should be a concern regarding older individuals using the subway network in Athens during rush hours and in general for sensitive groups (people with asthma, respiratory and cardiovascular problems, etc.).

Timetable Synchronisation for the First Trains in the Day According to Actual Transfer Times

Non-synchronised timetables of the first hour trains can lead to longer waiting times for passengers wishing to transfer at the transfer station. This study aims to reduce the waiting time of passengers by synchronising the timetables of first hour trains using actual transfer times. The transfer times of the passengers are obtained from the observations and are used in this synchronisation study. The genetic and simulated annealing algorithms are implemented to solve the first train synchronisation model. Finally, a case study is conducted on a section of the Istanbul metro network to test the synchronisation model. The results show that the total waiting time of the first hour trains transfer passengers is reduced by 35% by applying the proposed model. Another result of the study shows that using the actual transfer time instead of the average transfer time of the passengers reduces the average waiting time of the passengers by 19%.

Measuring metro station area's TODness: An exploratory study of Shenyang based on multi‐source urban data

AbstractTransit‐oriented development (TOD) promotes sustainable urban growth by integrating land use and transportation systems around public transportation nodes. Many high‐density Chinese cities have effectively applied TOD. However, studies on TODness in cities with relatively less‐developed metro networks are still nascent. This study developed a new geographic information system‐based method to assess TODness utilizing data from official departments and open sources. We employed 15 sub‐indicators of “Transit (T),” “Oriented (O),” and “Development (D)” dimensions to assess the TODness of Shenyang metro station areas (MSAs). We tested the validity of this tool by examining the relationship between TODness and urban vitality using an ordinary least squares model. Each MSA was comprehensively quantified and organized by categorizing their TODness into five types. The results indicate that the TODness index was closely associated with urban vitality, thereby validating the reasonableness of the proposed tool. Furthermore, the TODness of Shenyang's MSAs presents a spatial distribution of “high, medium, and low” from the urban center to the periphery. The proposed method is expected to enhance the effectiveness of TOD planning by providing a comprehensive and data‐driven approach.

Research on cooperative optimization of terminal distribution of Chongqing underground logistics system based on metro network

This thesis presents a novel solution for the distribution problem in the urban underground logistics system, specifically addressing the challenge of the “last kilometer” distribution during the non-operating hours of the Chongqing metro system. The study commences by examining the present condition and significance of subterranean logistics systems. It thoroughly analyzes the distinctive topography and traffic obstacles specific to Chongqing’s underground logistics system, and highlights the practicality and advantages of utilizing the metro system for logistics and distribution. The thesis proposes a comprehensive metro logistics and distribution system, which includes the selection of transfer locations, efficient connection between distribution centers and metro stations, and the implementation of appropriate management and scheduling strategies for distribution trucks. This study presents a unique approach by effectively combining the metro system with urban logistics requirements. It offers a fresh perspective on addressing the main challenges in urban logistics and distribution. Additionally, it provides a comprehensive theoretical and practical framework that can be applied to other regions with similar urban characteristics.

Photonic Gateway accommodating all types of wavelength paths for digital-coherent and IM-DD user terminals in all-photonic metro-access converged networks

In access and metro networks, there is increasing demand for guaranteed wide-bandwidth and stringent/deterministic end-to-end latency characteristics. Converging these networks by expanding dense wavelength-division-multiplexed metro networks to access areas is a promising approach to satisfy this demand. An All-Photonics Metro-Access Converged Network (APN) dynamically and flexibly provides a wavelength connection between any two points including user premises. This evolution in network architecture presents some technical challenges. First, from the access-node architecture viewpoint, conventional reconfigurable optical add/drop multiplexers (ROADMs) do not have functions for remote and in-channel user-terminal (UT) control or turn-back connection within the access area. Therefore, the APN cannot be achieved by simply adopting ROADMs as access nodes, which are defined as Photonic Gateways. Remote and in-channel UT controls are required to control the endpoints of a wavelength path beyond the carrier control-plane network. These types of control should be independent of the protocol and modulation/detection method for the main signal. In addition, the Photonic Gateway must flexibly and efficiently support a new type of wavelength path, specifically a turn-back connection within the access area, due to metro/access convergence. Second, from the viewpoint of the process for dynamic wavelength-path provisioning, the APN controller should autonomously discover physical connectivity between a UT and the Photonic Gateway connected via field access fibers. This paper proposes a novel Photonic Gateway architecture that flexibly and scalably provides various UTs with direct optical connections including turn-back configurations. This paper also proposes a method that autonomously recognizes physical connections between newly connected UTs and the Photonic Gateway. The proposed architecture and method are experimentally verified based on all types of wavelength-path provisioning that take at most 72 s using Photonic Gateway prototypes, 100-Gb/s digital-coherent UTs, and 10-Gb/s intensity-modulation direct-detection UTs. All types of wavelength paths that are provided over the APN architecture are considered including a turn-back connection between two UTs within the same access area and connections between two UTs that traverse the metro area with or without optical/electrical conversion.

Sustainable Transportation in the Gurugram-manesar Urban Complex

In today's world, more than half of the global population resides in urban areas, a significant shift from the past. In 1950, only 30% lived in cities, but by 2050, its projected to reach 66%. Despite rapid urbanization in India, one standout is the cyber town of Gurugram in Haryana, which has embraced sustainable urban growth. Maruti Industry was established with assistance from Suzuki Company of Japan, heralding a period of significant growth. This urban complex, often lauded as India's only city with a rapid metro network, modern commercial hubs, towering cyber parks, stands as a beacon of sustainable urbanization. The nomenclature of this complex was proposed in the district development plan. The establishment of the Maruti Industry in 1972 marked the onset of Gurugram's substantial development. Renowned for its quick metro network, modern malls, and cyber parks, Gurugram- Manesar Urban Complex stands as a model of sustainable urban development. Serving as Haryana's financial and industrial hub, Gurugram has witnessed the third-largest increase in per capita income in India, contributing significantly to the state's revenue. Its strategic location along National Highway Number 48 and proximity to Indira Gandhi International Airport have fueled its rapid expansion. This paper examines its transport system and connections to nearby towns and the national capital, Delhi.

An empirical study on the stochastic long-term travel demands of a large-scale metro network

The widely-existed uncertainty of origin–destination (OD) demand in transportation networks has attracted extensive attention. Most characterizations or modeling of stochastic OD demands in networks assume a homogenous probability distribution, but it lacks empirical studies of large-scale networks to justify this assumption. Given that the long-term continuous automatic fare collection data of metro networks can provide complete OD passenger demand information, this study takes the Shanghai Metro network as an example to empirically examine the stochasticity characteristics of OD passenger demands of metro network. Based on the morning peak OD demand data for 250 weekdays, a local outlier factor method is used to identify and remove outliers in the data. A clustering method is used to cluster the OD pairs, study the fluctuation and distribution characteristics of the OD passenger demands, and select the optimal distribution type through goodness-of-fit indices. The results show that 1) the coefficients of variation of morning peak OD demands in the network are mainly distributed in the range of 0.2–0.6, and different OD pairs have different fluctuations, and the degree of demand fluctuation decreases as the mean increases; 2) the probability distribution types of OD demands based on statistical characteristics are heterogeneous; and 3) the optimal distribution type of OD demands is Poisson, Lognormal/Gamma, and Normal for OD pairs characterized by a small mean and right-skewness, a small mean and skewness close to 0, and a large mean, respectively. In contrast to the simple average-based data processing of OD passenger demand in metro networks, this paper presents a new perspective of mining long-term continuous data to understand the inherent stochasticity of OD passenger demands. The results can provide more realistic and practical inputs and assumptions for theoretical research on stochastic OD demands in metro networks.

A Timetable Optimization Model for the Istanbul, Turkey, Metro Network Considering a Novel Regenerative Braking Energy Model

A Timetable Optimization Model for the Istanbul, Turkey, Metro Network Considering a Novel Regenerative Braking Energy Model

Assessing the Suitability of Fractal Dimension for Measuring Graphic Complexity Change in Schematic Metro Networks

Schematization is a process of generating schematic network maps (e.g., metro network maps), where the graphic complexity of networks is usually reduced. In the past two decades, various automated schematization methods have been developed. A quantitative and accurate description of the complexity variation in the schematization is critical to evaluate the usability of schematization methods. It is noticed that fractal dimension (F) has been widely used to analyze the complexity of geographic objects, and this indicator may be appropriate for this purpose. In some existing studies, although F has been employed to describe the complexity variation, the theoretical and experimental basis for adopting this approach is inadequate. In this study, experiments based on 26 Chinese cities’ metro networks showed that the F of all these metro networks have decreased in schematization, and a significant positive correlation exists between the F of original networks and the reduction of F after schematization. The above results were verified to have similar trends with the subjective opinions of participants in a psychological questionnaire. Therefore, it can be concluded that F can quantitatively measure the complexity change of networks in schematization. These discoveries provide the basis for using F to evaluate the usability of schematization methods.

Mining smart card data to estimate transfer passenger flow in a metro network

AbstractMetro systems play an important role in reducing urban traffic congestion and promoting the sustainable development of urban transport in megacities. With the expansion of a metro network, transfer stations are necessary for increasing the service connectivity of a metro network. An accurate estimation of transfer passenger flow can help improve the operations management of a metro system. This study proposes a data‐driven methodology for estimating the transfer passenger flow volume of each transfer station in a metro network by mining smart card data. The estimated transfer passenger flow data are visualized to show the spatial‐temporal distribution characteristics of metro transfer passenger flow. The case study results of the Shenzhen Metro network demonstrate that the proposed data‐driven methodological framework is very effective in estimating different types of transfer passenger flows, such as total transfer passenger flow, hourly transfer passenger flow, and inbound and outbound transfer flows at each transfer station. The spatial‐temporal distribution characteristics of transfer passenger flow can be very useful for designing effective and efficient passenger flow management measures to ensure the safe and efficient operation of a metro system.

The integrated on-demand bus routing problem: Combining on-demand buses with a high-frequency fixed line public transport network

In this work we analyze the performance of integrating a large-scale on-demand bus system with a high-frequency fixed line public transport network in an urban context. Given are a high-speed metro network, a set of real-time requests, a set of bus station locations and a fleet of fixed capacity minibuses. Requests have a set of possible departure/arrival bus stations within walking distance of the actual departure/arrival location and have to be served within a certain time window. The aim is to simultaneously (1) decide on the trip type for each passenger (only bus, only metro or mixed), (2) route the on-demand buses, (3) in the case of a bus-leg in the trip, assign each passenger to a departure and arrival bus station (bus station assignment), and (4) in the case of a metro-leg in the trip, decide the assigned transfer station(s) and used metro lines (transfer station assignment). We call this problem the integrated on-demand bus routing problem. After presenting a mathematical model, we propose a quick and scalable insertion-based heuristic to solve the problem.The results found by the heuristic are further used to compare the performance of an integrated system, to a system that only uses on-demand buses. It is concluded that the integrated system always performs better regarding the service rate or number of served requests. Depending on the speed and layout of the metro network, also the average user ride time per passenger improves by the integration.

Ground subsidence risk assessment method using PS-InSAR and LightGBM: a case study of Shanghai metro network

ABSTRACT Ground subsidence is a common geological hazard in urban areas that endangers the safety of infrastructure, such as subways. In this study, the ground subsidence risk assessment method considering both ground subsidence intensity and susceptibility is proposed and applied to assess ground subsidence risk of the Shanghai Metro network. Initially, PS-InSAR is used for the ground subsidence survey in the Shanghai Metro area. Subsequently, ten subsidence causal factors are collected, and the LightGBM machine learning algorithm is employed to conduct the ground subsidence susceptibility analysis. Then, a risk matrix is introduced to define ground subsidence risk by combining subsidence intensity and susceptibility. Finally, the risk map is generated in ArcGIS and classified into five levels. The assessment results were used to identify ground subsidence risk at different scales. The results indicate that the risk is higher in the southwest part of the study area, and the ground subsidence risk of the metro network exhibits a regional-related characteristic. On-site investigations were conducted to verify the results. The method enables fast ground subsidence assessment over a large area at a low cost and the assessment results can provide data for the prevention and management of ground subsidence hazards in the city.

Recognizing Critical Stations in Urban Rail Transit Networks Based on the PCA‐TPE Method: Shanghai Metro as an Example

The identification of core stations in urban rail transit (URT) networks remains a vital issue in network structure organization analysis and an integral part of network reliability evaluation. However, the identification of critical stations with a single centrality metric has limitations and the varying interactions between stations cannot be ignored. In this paper, a novel integrated approach is proposed by using the principal component analysis and topological potential considering entropy (PCA‐TPE) method. Taking the Shanghai metro (SHM) network as a case study, a Space L network model is constructed and the network topology characteristics are analyzed. Moreover, the susceptible‐infected (SI) model and the network failure simulation are employed to demonstrate the effectiveness of the proposed method. The results show that the SHM network exhibits characteristics of both small‐world networks and scale‐free networks. According to the experiments of the SI model, the nodes obtained by the PCA‐TPE method have stronger spreading influence than those derived by other methods, especially in the initial stage. The failure simulations illustrate that attacks against the nodes detected by the PCA‐TPE method will lead to devastating network failures. Hence, the proposed method is effective for identifying critical nodes in URT networks, and the findings of the research can provide theoretical evidence for the development planning and emergency management of the public traffic system.

TMFO-AGGRU: A Graph Convolutional Gated Recurrent Network for Metro Passenger Flow Forecasting

Accurately exploiting the spatial correlation of passenger flow related to the metro network is important for improving metro passenger flow prediction. In this paper, a novel metro passenger flow forecasting model termed TMFO-AGGRU is proposed based on parameter estimation optimization for a graph convolutional gated recurrent neural network. First, we model a graph convolutional gated recurrent neural network, which is capable of real-time spatial and temporal information training on the passenger flow at a target station. Second, we design graph convolution operations to replace the linear operations of gated recurrent neural networks. To fully utilize the weighted distribution of output data based on different traffic characteristics, we convert the adjacent static matrices into dynamic characteristic matrices using feature learning and construct the attention mechanism module. Last, an adaptive moth-flame optimization method based on T-variance is proposed in this paper to achieve dynamic optimization of structural parameters and to avoid affecting the accuracy of prediction due to improper parameter selection. Experimental results show that the proposed TMFO-AGGRU effectively improves the prediction accuracy and convergence speed, which outperforms traditional metro passenger flow prediction methods.

Revisiting Packet-Switched WDM Rings for Metro Networks: A Comprehensive Cross-Layer Assessment

Revisiting Packet-Switched WDM Rings for Metro Networks: A Comprehensive Cross-Layer Assessment

Multi-Tb/s Transmission Based on DMT-Modulated VCSELs in Metro Networks Assisted by Volterra Equalization

Multi-Tb/s Transmission Based on DMT-Modulated VCSELs in Metro Networks Assisted by Volterra Equalization

Modelling bus-based substitution capabilities for metro systems using bipartite graphs

Abstract A disruption of metro services can have a negative impact in the performance of a city’s transportation system and hinder mobility needs of travellers. Investigating the vulnerability of metro systems is required for planning mitigation actions, such as bus substitution services. This study develops a model, which consists of a bipartite graph and its projection to represent the bus substitution capabilities for metro networks. The proposed methodology effectively identifies significant substitution elements (bus lines), evaluates the robustness of alternative options in terms of both connectedness and connectivity, and suggests effective strategies for enhancing bus line capacity to improve network robustness. By applying the methodology to a real-world metro network, valuable insights are gained regarding important bus lines and substitution robustness. Study findings suggest that approaches based on the weighted degree exhibit the greatest effectiveness when it comes to connectivity and the overall efficiency of the network. These findings can assist public transport operators in proactively managing disruptions and improving their services.

The positive and negative impact of novel utility demand response programme and stochastic utility-driven events on renewable penetration and flexibility activation of train and station systems

Integrating an incentive-driven demand response program (DRP) into a metro railway system can enhance the techno-economic benefits of grid operators and metro railway operators by modulating the grid interactions. To facilitate demand response (DR), all energy flexibility resources within the metro network, such as heating, cooling, electrical, and vehicle systems, should be individually regulated by optimal control strategies. Therefore, an automated energy management system was developed and implemented to prioritise and logically control various energy flexibility resources to maximise the annual performance based on the requirements of the DR signal from the utility grid. Automated DR management helps to achieve an annual DR incentive of 0.57 × 106 HKD, enabling a 2.9 times lower annual operational cost of 0.28 × 106 HKD compared to the reference, even after accounting for the annual electricity tariff of 0.84 × 106 HKD. The techno-economic impacts of becoming a DR participant were stochastically studied using Hong Kong’s DRP by examining all crucial factors in the DR business model. In addition, this study defines a methodology for determining the optimal demand reduction target without biasing any stakeholders. Then, the DR business model was comparatively evaluated between a prosumer-based energy system and the same system without renewable energy support. The former system loses the DR incentive in proportion to the load matching compared to the latter system. Moreover, the DRP was extended from static to dynamic DR support, and the results show that considering dynamic DR can further increase the economic benefit of all stakeholders.

Festival timetable synchronization of metro trains and high-speed railway trains for late-night operations: an integrated bi-directional transfer optimization model

ABSTRACTThis paper focuses on synchronizing festival timetables between high-speed railway (HSR) and metro for late-night operations. A multi-objective model is proposed to optimize metro timetables of the last service period incorporating collaboration with HSR timetables. The model is designed to facilitate bi-directional transfers by maximizing spatial accessibility and temporal availability of metro service as well as minimizing transfer waiting times for HSR passengers. Our model can be efficiently solved using Gurobi, providing optimal solutions for large-scale metro networks within reasonable computational timeframes. A real-world case study is conducted to validate the effectiveness of our model. The results indicate that slight adjustments to metro timetables, without introducing additional trains, can yield significantly enhancements in late-night metro service for HSR passengers. Moreover, a smaller extension of service time across the entire metro network is proved to be more effective than a greater extension of service time on hub-connected metro lines.

Metro-line expansions and local air quality in Shenzhen: Focusing on network effects

We examine the air-quality effects of urban-rail development in Shenzhen, taking a difference-in-differences approach. This study is motivated by existing mixed evidence on the rail-pollution relationship, which we associate with the dynamic nature of the relationship itself. Our results demonstrate that the relationship varies by time, depending on network density and scale. New station openings had no significant impacts on local air quality or even worsened it until the 2010 metro-line extension, when Shenzhen’s metro network density was still low, with limited spatial service coverage. However, the 2016 extension significantly abated air pollution as the network grew denser and more comprehensive. The rail-driven anti-pollution effects tended to be further strengthened with externalities arising from improved network connectivity, spilling over the effects beyond newly opened stations to preexisting ones. Also, metro stations in proximity to neighborhoods that share key characteristics in transit-oriented development tended to generate a greater anti-pollution effect.