Quantitative resilience assessment of the network-level metro rail service's responses to the COVID-19 pandemic

Zhang, Zhipeng; Chai, Hua; Guo, Zhiyong

doi:10.1016/j.scs.2022.104315

Cited by 18 publications

(6 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During the peak period of the pandemic-related disturbances, the number of passengers using public transportation decreased by 50 % to 90 % worldwide [15]. Particularly, there were substantial reductions in metro service durations, with up to a 40 % decrease in travel times [16]. The decrease in the use of public transport was in correlation with the rising number of Covid-19 cases [17][18][19].…”

Section: Literature Reviewmentioning

confidence: 97%

Generalized Ordered Logit Model with Testing Assumptions: A Case Study of Using Urban Light Rail in Bursa

Akgun,

Campisi,

Sunar

2024

Communications

View full text Add to dashboard Cite

Logistic regression has been a widely used prediction technique to analyze categorical variables. However, if the assumptions are violated the results may be biased. The study in this paper applied an analytical technique namely generalized ordered logit model. A case study of using urban light rail under pandemic conditions was applied for the analysis. The results suggested that logistic regression should not be applied before exploring the multicollinearity and applying the test of parallel lines. If the assumptions are violated, generalized ordered logit model should be considered. Regarding the predictive variables, sociodemographic, socioeconomic and travel pattern, related variables were found to have a statistically significant impact on the perception of safety and infrastructure of urban light rail. The outcomes of the study would provide a deeper understanding of developing regression models for categorical variables for future studies.

show abstract

Section: Literature Reviewmentioning

confidence: 97%

Generalized Ordered Logit Model with Testing Assumptions: A Case Study of Using Urban Light Rail in Bursa

Akgun,

Campisi,

Sunar

2024

Communications

View full text Add to dashboard Cite

show abstract

“…In the analysis of extreme events, it is also common to measure instantaneous disturbances using metrics such as duration and the degree of deviation from normal. Zhang used indicators like vulnerability, robustness, speed, and return level to assess the impact of the COVID-19 pandemic on the subway system [34]. Liu utilized mobile signal data along with metrics like mobile population ratio, daily travel distance, and activity entropy, to measure the impact of the COVID-19 pandemic on population mobility and explored factors influencing population movement [35].…”

Section: Related Workmentioning

confidence: 99%

Quantitative Assessment of the Impact of Extreme Events on Electricity Consumption

Xiong,

Yan,

Qin

et al. 2023

Energies

View full text Add to dashboard Cite

Global energy consumption is growing rapidly, with the frequency and intensity of extreme events constantly increasing, posing a long-term threat to power supply and consumption. Therefore, analyzing the spatiotemporal characteristics of electricity consumption and quantitatively assessing the impact of extreme events on electricity consumption are of great significance. Based on fine-grained electricity consumption data from Europe for the years 2019–2022, this paper employs a data mining perspective and four methods including Z-score, Isolation Forest, Local Outlier Factor, and Autoencoder to detect abnormal electricity consumption during extreme events. Additionally, it combines indicators such as elastic loss, vulnerability, and duration to measure the impact of extreme events on electricity consumption. It is found that low temperatures could lead to abrupt changes in electricity consumption, with Northern Europe being more significantly affected by low temperatures. The COVID-19 pandemic had the most significant impact on electricity consumption in Europe, with the middle part of Europe being the hardest hit during the first wave of the pandemic. Electricity anomalies during the pandemic period were related to national pandemic control policies and exhibited some lag. High temperatures persisted for a longer duration in the middle part of Europe.

show abstract

“…Within urban areas and cities, the transport system assumes a central role in the production of particulate matter (PM) emissions, responsible for 20-40% of the total pollutant and environmental emissions (e.g., [1][2][3][4][5]). To mitigate this negative incidence, urban planners emphasize the promotion of sustainable transport policies (e.g., [6][7][8][9][10]), such as the development of high-quality underground metro systems or the retrofitting of the traditional ones (e.g., [3,[11][12][13]). Metro systems, which utilize an electrical power supply and move a high number of passengers/day, represent an effective solution to curbing greenhouse gas emissions.…”

Section: Introductionmentioning

confidence: 99%

Rain-Based Train Washing: A Sustainable Approach to Reduce PM Concentrations in Underground Environments

Cartenì,

Cascetta,

Falanga

et al. 2024

Sustainability

View full text Add to dashboard Cite

Fine particle concentrations measured in many underground rail systems around the world consistently exceed those observed at ground level, potentially posing significant implications for human health. While numerous authors have observed these high particle concentrations and analyzed both their atomic compositions and health impacts, few have investigated devices and technologies capable of reducing these high levels in underground environments. In light of these considerations and recognizing the multifaceted challenges associated with maintaining air quality in underground metro systems, the aim of this paper was to evaluate the usefulness and effectiveness of utilizing rainwater for washing trains to abate particulate matter (PM) concentrations in underground rail systems. To achieve this aim, an ad hoc case study was considered: the Naples Metro Line 1 (Italy), which is characterized by 4.5 km in the ground level and 13.5 km underground. A measurement campaign was carried out during storms of strong intensity through PM measuring instruments placed on station platforms along the metro line. Precisely, the trains were washed by the rain in the initial ground level section, and then continued wet within the underground one. The results of this measurement campaign were compared with those of a comparable survey carried out during average clear weather conditions, and the results showed that the train washing produces a significant PM10 concentration reduction of up to about 60% in the underground environment. If confirmed in other experimental settings, these results could lay the groundwork for the introduction of structured washing system devices (e.g., periodically washing trains and/or tunnels) for the reduction of PM concentration in underground metro systems. The present study sought to contribute valuable insights towards sustainable and environmentally conscious approaches to addressing air quality concerns, particularly by harnessing the natural resource of rainwater during specific meteorological events.

show abstract

Quantitative resilience assessment of the network-level metro rail service's responses to the COVID-19 pandemic

Cited by 18 publications

References 41 publications

Generalized Ordered Logit Model with Testing Assumptions: A Case Study of Using Urban Light Rail in Bursa

Generalized Ordered Logit Model with Testing Assumptions: A Case Study of Using Urban Light Rail in Bursa

Quantitative Assessment of the Impact of Extreme Events on Electricity Consumption

Rain-Based Train Washing: A Sustainable Approach to Reduce PM Concentrations in Underground Environments

Contact Info

Product

Resources

About