Energy-saving design and implementation in metro weak current systems: a case study of the Kunming metro system

With the accelerated urbanization in China, along with the growing scale of the metro transportation network, the energy consumption of metro systems continues to increase. To face the tough challenge of climate change, China has put forward the goal of peak carbon emissions by 2030 and achieving carbon neutrality by 2060. Energy consumption has become a serious burden for metro operation companies, since 10.2% of the total operational budgets is spent on electricity. Thus the development of methods to realize energy saving and emission reduction has become a major challenge for metros. In this study we conduct an in-depth research and analysis on metro energy load classification and energy management, focusing in particular on the design and usage of power supply systems for metro weak current electromechanical systems, including tunnel fans, station air conditioners, station escalators, automatic ticketing equipment, screen doors, drainage pumps, sewage pumps, platform doors, communication systems, signals, integrated monitoring systems, automatic ticketing and various lighting equipment and facilities. It is proposed that the five weak current systems, namely platform doors, communication systems, signals, integrated monitoring and automatic fare collection, should adopt a backup power supply. In order to ensure the reliable operation of all weak current systems in the station, the traditional decentralized power supply mode is changed to a centralized power supply and uninterruptible power supply (UPS) (1 + 1) parallel double-bus system. At the same time, combined with the data on equipment quantity, station passenger flow and station building floorage, the Boruta algorithm is used to filter out the equipment related to station weak current energy consumption, and a principal component analysis (PCA) algorithm is used to further reduce the dimensions of the filtered features to reduce the algorithm overhead of the subsequent quota analysis model. The XGBoost algorithm is used to establish a prediction model for station weak current system energy consumption. Analysis shows that there is a strong correlation between the energy consumption quota and the equipment quantity as well as station building floorage. By setting different metering instruments for power supply circuits, the main energy consumption data are collected to meet the requirements for graded metering of metro energy consumption, and then the energy consumption quota for the station weak current system is reasonably predicted. By adding metering instruments to the power supply circuits of different areas and equipment, the energy consumption of the weak current system can be measured and monitored in different grades. The combination of the energy management platform and energy consumption quota provides the basis for energy management of each energy-consuming unit, and ultimately realizes energy saving and reduced consumption.

Gearless Track-Friendly Metro with Guided Independently Rotating Wheels

Track–vehicle severe interaction on track with small curve radius results in rail wear and corrugation, and wheel polygonization, which drain considerable resources for rail grinding and wheels re-profiling in metro lines. To reduce the damage caused by track-vehicle severe interaction, the paper analyzes the reasons leading to rail wear and then proposes an architecture of a metro vehicle with independently rotating wheels driven directly by permanent magnet synchronous motors. The architecture is axle guidance, offered by passive linkages, which ensures that all axles are oriented radially, while control strategy was kept as simple as possible, identifying only two basic traction conditions. The concept is first discussed and then validated through a comprehensive set of running dynamics simulation performed with a multibody software to evaluate rail wear and rolling contact fatigue in traction/braking, coasting with different cant deficiency/excess conditions. The multibody dynamics simulation shows that the proposed architecture is virtually capable of avoiding both wear and rolling contact fatigue damages, and achieves the highest possible track friendliness. The concept of the proposed architecture is a track-fiendly metro architecture and could be a good reference for reducing rail-track interaction damages and maintainace cost.

Comparison of the Spreading Characters of Fire Products in the Typical Metro Stations of Washington, D.C., and Guangzhou

Fire is one of the most common disasters that threaten the safety of the crowd in metro stations. Due to the variations in the design of metro stations, the hazard posed by the spreading products of the fire can pose different risks. The typical structures of metro stations in Guangzhou and Washington, D.C., are very different from each other. In Washington, D.C., the “high-dome” structure is predominant in the construction of metro stations, while in Guangzhou, most metro stations have the “flat ceiling” structure. In this article, a numerical modeling for fire dynamic simulation is used to predict and compare the spreading characters of fire products (the smoke height change, the temperature distribution and the visibility change) when fires with 2.5 MW heat release rate occur in the platform center and at the platform end in the two kinds of metro stations. The results show that, in the same fire scenario, the lowest smoke heights monitored in the Guangzhou model is 0.6 m (fire at the platform end) and 0.8 m (fire in the platform center) above the safe smoke height in 360 s after a fire breaks out, while it is 6.15 m (fire in the platform center) and 6.2 m (fire at the platform end) above the smoke height in the Washington model. The temperature increment in the Guangzhou model is 23 °C (fire in the platform center) to 29 °C (fire at the platform end) in 360 s after the fire breaks out, while the temperature increment in the same period in the Washington model is 8.5 °C (fire at the platform end) to 9 °C (fire in the platform center). The visibility of most areas on the platform of the Guangzhou model is about 1 m no matter the fire is in the platform center or at the platform end at 360 s after the fire begins, while in the Washington model, the visibility of most areas is 1.5–13.5 mm (fire at the platform end) to 4–14 m (fire in the platform center) at the same moment. Based on the results, the environment is worse when the fire happens at the end of the platform than that when the fire happens in the platform center of the Guangzhou model. While the fire location has fewer impacts on the smoke height, temperature, and visibility in the Washington model, metro stations with a high-dome structure can be beneficial to fire evacuation safety; however, the construction cost can be high. Metro stations with flat ceiling are widely used in more cities for it has lower construction cost; to compensate for its weaker abilities under fire conditions, it is suggested that smoke exhaust systems should be carefully and fully considered.

Two-Step Optimization of Urban Rail Transit Marshalling and Real-Time Station Control at a Comprehensive Transportation Hub

Urban rail transit connecting with a comprehensive transportation hub should meet passenger demands not only within the urban area, but also from outer areas through high-speed railways or planes, which leads to different characteristics of passenger demands. This paper discusses two strategies to deal with these complex passenger demands from two aspects: transit train formation and real-time holding control. First, we establish a model to optimize the multi-marshalling problem by minimizing the trains’ vacant capacities to cope with the fluctuation of demand in different periods. Then, we establish another model to control the multi-marshalling trains in real time to minimize the passengers’ total waiting time. A genetic algorithm (GA) is designed to solve the integrated two-step model of optimizing the number, timetable and real-time holding control of the multi-marshalling trains. The numerical results show that the combined two-step model of multi-marshalling operation and holding control at stations can better deal with the demand fluctuation of urban rail transit connecting with the comprehensive transportation hub. This method can efficiently reduce the number of passengers detained at the hub station as well as the waiting time without increasing the passengers’ on-train time even with highly fluctuating passenger flow.

TOD Typology Based on Urban Renewal: A Classification of Metro Stations for Ningbo City

In recent decades, the transit-oriented development (TOD) concept has been widely used all over the world, especially in China, for the massive construction of urban public transportation systems with rail transit as the backbone. However, it is not easy to make significant changes in a city while building a transportation system, and the transit-guided urban development expected by the TOD concept has not been completely realized. The transformation of nearby areas with the guidance of transit is also becoming the choice of many Chinese cities, especially for cities that have only had subways for a few years. Unlike other international metropolitan cities, with metro systems of considerable scale, the modernization process of most of the small and medium-sized cities in China is being carried out simultaneously with metro-based public transportation guidance. For cities which are still in their initial stage of the backbone public transportation system, there is not enough previous experience and evidence to support the suitability of TOD typological analysis based on the node-place model. More research based on the node-place model has also shown practical applications of the TOD in developed cities. However, there are very few studies that analyse cities in which rail transit and urban development are both in a period of rapid growth. The goal of this research is to identify which metro stations in these cities are suitable for TOD improvement and optimization. This article attempts to expand the willingness of residents on the basis of the traditional node-place model as one of the judgment indicators for evaluating whether existing stations and surrounding areas are suitable for TOD improvement. At the same time, traditional statistical analysis is combined with GIS and machine learning technology. Using this method, we propose the TOD improvement-oriented station area classification and identification method based on TOD typology theory. The results show that Ningbo's subway stations can be divided into four categories according to the suitability for TOD improvement, and we selected seven stations suitable for TOD improvement according to the characteristics of the node-place model. The practice in Ningbo has proved that this method is effective for identifying sites suitable for TOD improvement, especially for cities that have recently built subways.

Identification and Quantification of Node Criticality through EWM–TOPSIS: A Study of Hong Kong’s MTR System

Public transport networks (PTNs) are critical in populated and rapidly densifying cities such as Hong Kong, Beijing, Shanghai, Mumbai, and Tokyo. Public transportation plays an indispensable role in urban resilience with an integrated, complex, and dynamically changeable network structure. Consequently, identifying and quantifying node criticality in complex PTNs is of great practical significance to improve network robustness from damage. Despite the proposition of various node criticality criteria to address this problem, few succeeded in more comprehensive aspects. Therefore, this paper presents an efficient and thorough ranking method, that is, entropy weight method (EWM)–technology for order preference by similarity to an ideal solution (TOPSIS), named EWM–TOPSIS, to evaluate node criticality by taking into account various node features in complex networks. Then we demonstrate it on the Mass Transit Railway (MTR) in Hong Kong by removing and recovering the top k critical nodes in descending order to compare the effectiveness of degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), and the proposed EWM–TOPSIS method. Four evaluation indicators, that is, the frequency of nodes with the same ranking (F), the global network efficiency (E), the size of the largest connected component (LCC), and the average path length (APL), are computed to compare the performance of the four methods and measure network robustness under different designed attack and recovery strategies. The results demonstrate that the EWM–TOPSIS method has more obvious advantages than the others, especially in the early stage.

Understanding the Impact of Street Patterns on Pedestrian Distribution: A Case Study in Tianjin, China

This paper investigates the impact of street pattern, metro stations, and density of urban functions on pedestrian distribution in Tianjin, China. Thirteen neighborhoods are selected from the city center and suburbs. Pedestrian and vehicle volumes are observed through detailed gate count from 703 street segments in these neighborhoods. Regression models are constructed to analyze the impact of the street pattern, points of interest (POIs), and vehicle and metro accessibility on pedestrian volumes in each neighborhood and across the city. The results show that when analyzing all neighborhoods together, local street connectivity and POIs had a strong influence on pedestrian distribution. Proximity to metro stations and vehicle accessibility had a minor impact. When analyzing each neighborhood separately, both local- and city-scale street patterns affect pedestrian distributions. These findings suggest that the street pattern provides a base layer for metro stations to attract both the emergence of active urban functions and pedestrian movement.

Economical Speed for Optimizing the Travel Time and Energy Consumption in Train Scheduling using a Fuzzy Multi-Objective Model

Speed is one of the most influential variables in both energy consumption and train scheduling problems. Increasing speed guarantees punctuality, thereby improving railroad capacity and railway stakeholders’ satisfaction and revenues. However, a rise in speed leads to more energy consumption, costs, and thus, more pollutant emissions. Therefore, determining an economic speed, which requires a trade-off between the user’s expectations and the capabilities of the railway system in providing tractive forces to overcome the running resistance due to rail route and moving conditions, is a critical challenge in railway studies. This paper proposes a new fuzzy multi-objective model, which, by integrating micro and macro levels and determining the economical speed for trains in block sections, can optimize train travel time and energy consumption. Implementing the proposed model in a real case with different scenarios for train scheduling reveals that this model can enhance the total travel time by 19% without changing the energy consumption ratio. The proposed model has little need for input from experts’ opinions to determine the rates and parameters.

Unplanned Disruption Analysis in Urban Railway Systems Using Smart Card Data

Metro system disruptions are a big concern due to their impacts on safety, service quality, and operating efficiency. A better understanding of system performance and passenger behavior under unplanned disruptions is critical for efficient decision making, effective customer communication, and identifying potential improvements. However, few studies explore disruption impacts on individual passenger behavior, and most studies use manually collected survey data. This study examines the potential of using automated collection data to comprehensively analyze unplanned disruption impacts. We propose a systematic approach to evaluate disruption impacts on system performance and individual responses in urban railway systems using automated fare collection (AFC) data. We develop a set of performance metrics to evaluate performance from the perspectives of train operations, information provision (communication), and bridging strategy (shuttle bus services to connect stations impacted by a disruption). We also propose an inference method to quantify the individual response to disruptions (e.g. travel or not, change stations or modes) depending on their trip characteristics with respect to the location and timing of the disruption. The proposed approach is demonstrated using data from a busy metro system. The results highlight the ability of AFC data in providing new insights for the analysis of unplanned disruptions, which are difficult to extract from traditional data collection methods. The case study shows that the disruption impacts are network-wide, and the impacts on passengers continue for a significant amount of time after the incident ended. The behavior highlights the importance of real-time information and the need for timely dissemination.

Methodology for the Characterisation of Linear Rail Transport Infrastructures with the Machine Learning Technique and Their Application in a Hyperloop Network

The layout solution for linear rail transport infrastructure will always alternate ‘surface’ sections with ‘tunnel’ and ‘viaduct’ sections. The capital expenditure (CapEx) linked at the planning stage to this type of public asset is strongly connected to the quantity of tunnels and viaducts planned. In this context, for similar lengths, a railway line using 15% tunnels and 7% viaducts to link two cities should not have the same financial viability as one using 8% tunnels and 3% viaducts to link the same cities. The process of planning, design and construction of linear works is heavily scrutinised by public administrations in all countries, and in many cases similar standards of work are shared. Firstly, this research paper highlights the existence of hidden geometric patterns in all linear transport infrastructures worldwide. Secondly, it proposes to exploit the existence of such patterns for the benefit of planners through the computational power available today in machine learning-as-a-service (MLaaS) platforms. This article demonstrates how geometric features extracted from any succession of rectangular trapeziums in linear infrastructures can predict the quantity of kilometres in ‘surface’, ‘tunnel’ and ‘viaduct’ sections in future linear rail transport infrastructures that have not yet been built. The practical application of the proposed working methodology has made it possible to intuit the characteristics of a future Hyperloop transport network in Europe of more than 12,000 km in length.