Long-Term Urban Epidemic and Disaster Resilience: The Planning and Assessment of a Comprehensive Underground Resilience Core

This study utilizes the enclosed and stable environment of underground space for long-term sustainable planning for urban epidemics and disasters. Owing to the COVID-19 epidemic, cities require long-term epidemic-disaster management. Therefore, this study proposed a strategy for integrating multiple functions to plan a comprehensive Underground Resilience Core (URC). A planning and assessment methods of URC were proposed. With this methodology, epidemic- and disaster- URCs were integrated to construct a comprehensive-URC in underground spaces. The results show: (1) Epidemic-resilient URCs adopting a joint progressive approach with designated hospitals can rapidly suppress an epidemic outbreak. (2) The regularity of the morphology of underground spaces determines the area of the URC. Bar-shaped underground spaces have the potential for planning disaster-URCs. (3) The URC planning efficiency ranking is as follows: Bar shapes lead overall, T shapes are second under seismic resilience, and Cross shapes are second under epidemic resilience. (4) The potential analysis of planning a comprehensive-URC in the underground parking in Chinese cities showed that the recovery time can be advanced from 29% to 39% and the comprehensive resilience can be improved by 37.63%. The results of this study can serve as sustainable urban planning strategies and assessment tools for long-term epidemic-disaster management.

Urban Mobility in the Postpandemic Stage: A Comprehensive Investigation of a Variety of Cities in China

The global outbreak of coronavirus disease 2019 (COVID-19) has affected the urban mobility of nations around the world. The pandemic may even have a potentially lasting impact on travel behaviors during the post-pandemic stage. China has basically stopped the spread of COVID-19 and reopened the economy, providing an unprecedented environment for investigating post-pandemic travel behaviors. This study conducts multiple investigations to show the changes in travel behaviors in the post-pandemic stage, on the basis of empirical travel data in a variety of cities in China. Specifically, this study demonstrates the changes in road network travel speed in 57 case cities and the changes in subway ridership in 26 case cities. Comprehensive comparisons can indicate the potential modal share in the post-pandemic stage. Further, this study conducts a case analysis of Beijing, where the city has experienced two waves of COVID-19. The variations in travel speed in the road network of Beijing at different stages of the pandemic help reveal the public's responses towards the varying severity of the pandemic. Finally, a case study of the Yuhang district in Hangzhou is conducted to demonstrate the changes in traffic volume and vehicle travel distance amid the post-pandemic stage based on license plate recognition data. Results indicate a decline in subway trips in the post-pandemic stage among case cities. The vehicular traffic in cities with subways has recovered in peak hours on weekdays and has been even more congested than the pre-pandemic levels;whereas the vehicular traffic in cities without subways has not rebounded to pre-pandemic levels. This situation implies a potential modal shift from public transportation to private vehicular travel modes. Results also indicate that commuting traffic is sensitive to the severity of the pandemic. This may be because countermeasures, e.g., work-from-home and suspension of non-essential businesses, will be implemented if the pandemic restarts. The travel speed in non-peak hours and on non-workdays is higher than pre-pandemic levels, indicating that non-essential travel demand may be reduced and the public's vigilance towards the pandemic may continue to the post-pandemic stage. These findings can help improve policymaking strategies in the post-pandemic new normal. © 2023 American Society of Civil Engineers.

Local travel behaviour under continuing COVID-19 waves- A proxy for pandemic fatigue?

COVID-19 continues to threaten the world. Relaxing local travel behaviours on preventing the spread of COVID-19, may increase the infection risk in subsequent waves of SARS-CoV-2 transmission. In this study, we analysed changes in the travel behaviour of different population groups (adult, child, student, elderly) during four pandemic waves in Hong Kong before January 2021, by 4-billion second-by-second smartcard records of subway. A significant continuous relaxation in human travel behaviour was observed during the four waves of SARS-CoV-2 transmission. Residents sharply reduced their local travel by 51.9%, 50.1%, 27.6%, and 20.5% from the first to fourth pandemic waves, respectively. The population flow in residential areas, workplaces, schools, shopping areas, amusement areas and border areas, decreased on average by 30.3%, 33.5%, 41.9%, 58.1%, 85.4% and 99.6%, respectively, during the pandemic weeks. We also found that many other cities around the world experienced a similar relaxation trend in local travel behaviour, by comparing traffic congestion data during the pandemic with data from the same period in 2019. The quantitative pandemic fatigue in local travel behaviour could help governments partially predicting personal protective behaviours, and thus to suggest more accurate interventions during subsequent waves, especially for highly infectious virus variants such as Omicron.

Non-Stationary Time Series Model for Station-Based Subway Ridership During COVID-19 Pandemic: Case Study of New York City.

The COVID-19 pandemic in 2020 has caused sudden shocks in transportation systems, specifically the subway ridership patterns in New York City (NYC), U.S. Understanding the temporal pattern of subway ridership through statistical models is crucial during such shocks. However, many existing statistical frameworks may not be a good fit to analyze the ridership data sets during the pandemic, since some of the modeling assumptions might be violated during this time. In this paper, utilizing change point detection procedures, a piecewise stationary time series model is proposed to capture the nonstationary structure of subway ridership. Specifically, the model consists of several independent station based autoregressive integrated moving average (ARIMA) models concatenated together at certain time points. Further, data-driven algorithms are utilized to detect the changes of ridership patterns as well as to estimate the model parameters before and during the COVID-19 pandemic. The data sets of focus are daily ridership of subway stations in NYC for randomly selected stations. Fitting the proposed model to these data sets enhances understanding of ridership changes during external shocks, both in relation to mean (average) changes and the temporal correlations.

Station-Level Effects of the COVID-19 Pandemic on Subway Ridership in the Seoul Metropolitan Area.

This paper investigates the station-level impacts of the coronavirus disease (COVID-19) pandemic on subway ridership in the Seoul Metropolitan Area. Spatial econometric models are constructed to examine the association between ridership reduction caused by the pandemic and station-level characteristics during the pandemic years 2020 and 2021. The results reveal unequal effects on station-level ridership, based on the pandemic waves, the demographics, and the economic features of pedestrian catchment areas. First, the subway system was severely disrupted by the pandemic, with significant decreases in ridership-by about 27% for each of the pandemic years-compared with the pre-pandemic year (2019). Second, the ridership reduction was sensitive to the three waves in 2020 and responded accordingly; however, it became less sensitive to the waves in 2021, indicating that subway usage was less responsive to pandemic waves during the second year of the pandemic. Third, pedestrian catchment areas with higher numbers of younger residents (in their 20s) and older residents (65 years and older), those with more businesses requiring face-to-face interactions with consumers, and stations located in the employment centers were hit the hardest in ridership reduction caused by the pandemic.

Bioprotection of Transportation and Facilities from SARS-CoV-2 (COVID-19).

The recent COVID-19 pandemic has led to a nearly world-wide shelter-in-place strategy. This raises several natural concerns about the safe relaxing of current restrictions. This article focuses on the design and operation of heating ventilation and air conditioning (HVAC) systems in the context of transportation. Do HVAC systems have a role in limiting viral spread? During shelter-in-place, can the HVAC system in a dwelling or a vehicle help limit spread of the virus? After the shelter-in-place strategy ends, can typical workplace and transportation HVAC systems limit spread of the virus? This article directly addresses these and other questions. In addition, it also summarizes simplifying assumptions needed to make meaningful predictions. This article derives new results using transform methods first given in Ginsberg and Bui. These new results describe viral spread through an HVAC system and estimate the aggregate dose of virus inhaled by an uninfected building or vehicle occupant when an infected occupant is present within the same building or vehicle. Central to these results is the derivation of a quantity called the "protection factor"-a term-of-art borrowed from the design of gas masks. Older results that rely on numerical approximations to these differential equations have long been lab validated. This article gives the exact solutions in fixed infrastructure for the first time. These solutions, therefore, retain the same lab validation of the older methods of approximation. Further, these exact solutions yield valuable insights into HVAC systems used in transportation.

Understanding nonlinear and synergistic effects of the built environment on urban vibrancy in metro station areas

Transit-oriented development (TOD) has long been recognized as a significant model for prospering urban vibrancy. However, most studies on TOD and urban vibrancy do not consider temporal differences or the nonlinear effects involved. This study applies the gradient boosting decision tree (GBDT) model to metro station areas in Wuhan to explore the nonlinear and synergistic effects of the built-environment features on urban vibrancy during different times. The results show that (1) the effects of the built-environment features on the vibrancy around metro stations differ over time;(2) the most critical features affecting vibrancy are leisure facilities, floor area ratio, commercial facilities, and enterprises;(3) there are approximately linear or complex nonlinear relationships between the built-environment features and the vibrancy;and (4) the synergistic effects suggest that multimodal is more effective at leisure-dominated stations, high-density development is more effective at commercial-dominated stations, and mixed development is more effective at employment-oriented stations. The findings suggest improved planning recommendations for the organization of rail transport to improve the vibrancy of metro station areas.

Shaping the subway microbiome through probiotic-based sanitation during the COVID-19 emergency: a pre-post case-control study.

BACKGROUND: The COVID-19 pandemic has highlighted the extent to which the public transportation environment, such as in subways, may be important for the transmission of potential pathogenic microbes among humans, with the possibility of rapidly impacting large numbers of people. For these reasons, sanitation procedures, including massive use of chemical disinfection, were mandatorily introduced during the emergency and remain in place. However, most chemical disinfectants have temporary action and a high environmental impact, potentially enhancing antimicrobial resistance (AMR) of the treated microbes. By contrast, a biological and eco-sustainable probiotic-based sanitation (PBS) procedure was recently shown to stably shape the microbiome of treated environments, providing effective and long-term control of pathogens and AMR spread in addition to activity against SARS-CoV-2, the causative agent of COVID-19. Our study aims to assess the applicability and impact of PBS compared with chemical disinfectants based on their effects on the surface microbiome of a subway environment. RESULTS: The train microbiome was characterized by both culture-based and culture-independent molecular methods, including 16S rRNA NGS and real-time qPCR microarray, for profiling the train bacteriome and its resistome and to identify and quantify specific human pathogens. SARS-CoV-2 presence was also assessed in parallel using digital droplet PCR. The results showed a clear and significant decrease in bacterial and fungal pathogens (p < 0.001) as well as of SARS-CoV-2 presence (p < 0.01), in the PBS-treated train compared with the chemically disinfected control train. In addition, NGS profiling evidenced diverse clusters in the population of air vs. surface while demonstrating the specific action of PBS against pathogens rather than the entire train bacteriome. CONCLUSIONS: The data presented here provide the first direct assessment of the impact of different sanitation procedures on the subway microbiome, allowing a better understanding of its composition and dynamics and showing that a biological sanitation approach may be highly effective in counteracting pathogens and AMR spread in our increasingly urbanized and interconnected environment. Video Abstract.

Peak-easing strategies for urban subway operations in the context of COVID-19 epidemic.

Subways play an important role in public transportation to and from work. In the traditional working system, the commuting time is often arranged at fixed time nodes, which directly leads to the gathering of "morning peak" and "evening peak" in the subway. Under the COVID-19 pandemic, this congestion is exacerbating the spread of the novel coronavirus. Several countries have resorted to the strategy of stopping production to curb the risk of the spread of the epidemic seriously affecting citizens' living needs and hindering economic operation. Therefore, orderly resumption of work and production without increasing the risk of the spread of the epidemic has become an urgent problem to be solved. To this end, we propose a mixed integer programming model that takes into account both the number of travelers and the efficiency of epidemic prevention and control. Under the condition that the working hours remain the same, it can adjust the working days and commuting time flexibly to realize orderly off-peak travel of the workers who return to work. Through independent design of travel time and reasonable control of the number of passengers, the model relaxes the limitation of the number of subway commuters and reduces the probability of cross-travel between different companies. We also take the data of Beijing subway operation and apply it to the solution of our model as an example. The example analysis results show that our model can realize the optimal travel scheme design of returning to work at the same time node and avoiding the risk of cross infection among enterprises under different epidemic prevention and control levels.

Simulation analysis of passengers' rescheduling strategies in metro station under COVID-19

The spread of COVID-19 has a great impact on public transport which is closely related to social life. As an essential carrier of the cities, metro has become an important object of concern during the epidemic. Due to the high infection risk of COVID-19 in public space, it is necessary to quantitatively evaluate and perform corresponding epidemic control measures on reducing public health risks in metro station. In this paper, three strategies of passenger rescheduling, i.e. controlling the flows of inbound and outbound passengers in the station, setting route guidance in the crucial areas and shortening the interval time of train, are simulated and analyzed based on Anylogic. The performances of different strategies are characterized and evaluated by the important parameters, which include local passengers' density, inbound and outbound time. Finally, the optimization experiments based on an objective function are carried out to obtain the best strategy combination considering passengers' health safety and travel efficiency. The crucial areas with high density are obtained from the simulation results of the initial model. The three independent strategies are helpful in reducing the maximum passengers' density and average travel time. The optimization results of strategy combination and the specific parameters of each strategy are obtained by the final simulation experiment. The research findings are important reference to enhance the present health risk management level and provide specific measures of passenger organization in metro station under COVID-19. © 2023 Elsevier Ltd

A Deep Spatiotemporal Fuzzy Neural Network for Subway Passenger Flow Prediction With COVID-19 Search Engine Data

Passenger flow prediction is of great significance in the operation and management of subways, especially in reducing energy consumption and improving service quality. Due to the impact of COVID-19, subway passenger flow fluctuates a lot, which makes passenger flow estimation or forecasting a very challenging task. This article mainly carries out two aspects of work to solve the task of subway passenger flow prediction under pandemic. First, this article introduces search engine data as a new data source and provides a systematic method to extract valid quires and search volumes that are closely associated with subway passenger flow under pandemic. Second, this article combines the fuzzy theory and neural network to propose a deep learning architecture called "deep spatiotemporal fuzzy neural network” to deal with the complex spatiotemporal features and uncertain external data of subway passenger flow prediction. Experiments on the actual dataset of the Beijing subway prove the superiority of the model and the effectiveness of search engine data in subway passenger flow forecasting.

COVID-19 Confirmed Cases and Subway Passengers

COVID-19 is mainly transmitted between people. Therefore, people's movement may cause the spread of COVID-19. Announcing an increase in the number of confirmed cases affects people's behavior and reduces people's movement. We analyzed the correlation between the number of COVID-19 confirmed cases and the change in the number of subway passengers in the metropolitan area to promote the understanding of the relationship between public transportation volume and COVID-19 confirmed cases. By using the reference number of subway passengers in 2016–2019, we calculated the decrease in passengers during the COVID-19 pandemic period from April 8, 2020 to July 29, 2021. Changes in subway passengers did not seem to affect the number of confirmed cases significantly. However, announcing an increase in confirmed cases greatly reduced the number of passengers. We also found that people avoided the subway on the basis of their normalized risk perception rather than absolute risk based on the number of daily confirmed cases. © 2022 The Korean Physical Society. All rights reserved.

Study on TOD urban design method at district level from the perspective of resilient city

During the novel coronavirus epidemic from 2020 to the present, various cities have experienced different degrees of prevention, control and residents living problems. Taking the subway station area of Sichuan Normal University as an example, based on the theory of resilient city, this paper puts forward a conceptual TOD urban design strategy for the city to cope with sudden public health problems, studies the concept, development context and theoretical connotation of resilient city, and analyzes three types of application logic of resilient city in urban design of TOD area. Based on the analysis of the site basic conditions and users in the subway station area of Sichuan Normal University, the design result of "TOD Healthy Life Mode" combined with the concept of "Health + " is obtained, and the life trajectories of four types of users, including students, office workers, retired elderly and purpose visitors, are simulated. © 2022 Science Press. All rights reserved.

An Overview of the Algiers Subway Management Before and During the Covid-19 Pandemic to Avert Potential Financial Losses

The purpose of this paper is to assess the Algiers subway passengers' demand and to show its importance as mass transport means. Also, to carry out a forecast evaluation on the subway operation proposed by the Algiers Metro Company (EMA) for the year 2022, under the Covid-19 pandemic, to limit future financial losses and to respond optimally to passengers' demand. The methodology used in this research paper is based on the real Algiers subway passengers' demand between 2018 and 2021. In the first step, the consequence of the Covid-19 pandemic has been analyzed. Then, an evaluation of the Algiers subway operation before and during the pandemic has been done. As a result, the study demonstrated that the current operation is inefficient, which will lead to future financial losses. To avoid this last issue, the paper proposes some suggestions to adapt the subway capacity with the real users' demand. © 2022 The Authors. Published by ELSEVIER B.V.

Incomplete pythagorean fuzzy preference relation for subway station safety management during COVID-19 pandemic.

Completing the Pythagorean fuzzy preference relations (PFPRs) based on additive consistency may exceed the defined domain. Therefore, we develop a group decision-making (GDM) method with incomplete PFPRs. Firstly, sufficient conditions for the expressibility of estimated preference values in PFPRs based on additive consistency are presented. Next, the correction algorithm is developed to correct the inexpressible elements in incomplete PFPRs. Then, a GDM method based on incomplete PFPRs is proposed to determine the objective weights of decision-makers. Finally, an example of subway station safety management during COVID-19 is selected to illustrate the applicability of the developed GDM method. The results show that the developed GDM method effectively identifies the crucial risk factor in subway station safety management and has better performance in terms of computational time complexity than the multiplicative consistency method.

Reducing Virus Transmission from Heating, Ventilation, and Air Conditioning Systems of Urban Subways.

Aerosols carrying the virus inside enclosed spaces is an important mode of transmission for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as supported by growing evidence. Urban subways are one of the most frequented enclosed spaces. The subway is a utilitarian and low-cost transit system in modern society. However, studies are yet to demonstrate patterns of viral transmission in subway heating, ventilation, and air conditioning (HVAC) systems. To fill this gap, we performed a computational investigation of the airflow (and associated aerosol transmission) in an urban subway cabin equipped with an HVAC system. We employed a transport equation for aerosol concentration, which was added to the basic buoyant solver to resolve the aerosol transmission inside the subway cabin. This was achieved by considering the thermal, turbulent, and induced ventilation flow effects. Using the probability of encountering aerosols on sampling surfaces crossing the passenger breathing zones, we detected the highest infection risk zones inside the urban subway under different settings. We proposed a novel HVAC system that can impede aerosol spread, both vertically and horizontally, inside the cabin. In the conventional model, the maximum probability of encountering aerosols from the breathing of infected individuals near the fresh-air ducts was equal to 51.2%. This decreased to 3.5% in the proposed HVAC model. Overall, using the proposed HVAC system for urban subways led to a decrease in the mean value of the probability of encountering the aerosol by approximately 84% compared with that of the conventional system.

The influences of facilities management on mental health of underground development users during the pandemic in Hong Kong

The outbreak of COVID-19 has triggered an unprecedented health crisis across the world. Previous research indicated that the fear of being infected in public place has transportation hindered the commuters’ choice on. In fact, underground transportation systems, especially those located in high- density cities, have been perceived as high risk environments under the pandemic. In addition, the prolonged COVID-19 outbreak, together with the negative public impression towards underground environment, have to certain extent triggered various mental health responses amongst citizens (e.g., 42.3% increase of anxiety in Hong Kong). This study thus aims to investigate the impacts of FM on underground development users’ mental health in Hong Kong. To achieve this aim, a questionnaire survey approach is adopted. The survey is designed to contain three parts: background information, satisfaction towards underground FM (space management, building services, and supporting facilities related to the pandemic), and mental health level (emotional exhaustion, depersonalization, and claustrophobia). Data is collected over four underground subway stations in Hong Kong. Person correlation and regression analysis are conducted to determine the statistically significant relationships between underground FM and users’ mental health. The results indicated that satisfaction towards visual access, immediate access, and hygiene practices have negative relationship with the occurrence of emotional exhuastion and depersonalization, except for claustrophobia symptoms. The study results provide empirical evidence for practitioners to make informed decisions in FM plans for enhancing mental health of underground development users under and after the pandemic.

Design of epidemic detection system in railway station based on FPGA

Since the outbreak of COVID-19 in 2020, wearing masks and displaying green codes in and out of public places has become a habit. The identity verification of the existing railway station is mainly based on face recognition. Due to the outbreak and persistence of the epidemic, people often need to call out the green code on their mobile phones for staff verification, which takes a lot of time. At the same time, the existing face recognition equipment requires the inbound personnel to take off their masks, which also increases the infection risk of the inbound personnel. In order to reduce the above infection risk and speed up people's entry and exit speed, we have designed a system that can identify people wearing masks and judge whether they are confirmed or suspected cases at the same time. Firstly, the system measures the passenger's body temperature through the infrared temperature measurement module, carries out face detection and recognition at the same time, and queries the recognition results in the database to judge whether the passenger is diagnosed or in close contact. When the passenger is normal, it is allowed to pass, otherwise it is not allowed to pass, and updates the relevant data in the cloud database. The system uses Yolo algorithm as the face detection algorithm, and then carries out face recognition through FaceNet network, so as to judge its identity and query the relevant information of the person in the cloud database. After testing, the iterative loss rate of the system is basically below 0.1 and the accuracy is basically stable above 99%. Considering that we need to use it on embedded devices and the amount of calculation operation of deep learning algorithm is large, and FPGA can well build circuits according to the needs of the model because of its reconfigurability, and FPGA can realize hardware acceleration because it can run in parallel, so we finally choose to deploy the model to FPGA to complete face recognition. © 2022 IEEE.

Stratum Ventilation: Enabling Simultaneous Energy Conservation and Air Purification in Subway Cars.

The supply of fresh air for underground rail transit systems is not as simple as opening windows, which is a conventional ventilation (CV) measure adopted in aboveground vehicles. This study aims to improve contaminant dilution and air purification in subway car ventilation systems and the safety of rail transit post-coronavirus disease pandemic era. We designed an air conditioning (AC) terminal system combined with stratum ventilation (SV) to enable energy consumption reduction for subway cars. We experimentally tested the effectiveness of a turbulence model to investigate ventilation in subway cars. Further, we compared the velocity fields of CV and SV in subway cars to understand the differences in their airflow organizations and contaminant removal efficiencies, along with the energy savings of four ventilation scenarios, based on the calculations carried out using computational fluid dynamics. At a ventilation flow rate of 7200 m3/h, the CO2 concentration and temperature in the breathing areas of seated passengers were better in the SV than in the CV at a rate of 8500 m3/h. Additionally, the energy-saving rate of SV with AC cooling was 14.05%. The study provides new ideas for reducing the energy consumption of rail transit and broadens indoor application scenarios of SV technology.

Investigating factors affecting university students' use of subway before and after COVID-19 outbreak: A case study in Tehran.

Identifying and examining factors affecting the use of the subway is critical for developing countries as they struggle with high levels of auto use and resulting congestion, noise and air pollution. In this research, we surveyed students of a top-ranked university in the capital of Iran before and after the COVID-19 outbreak to identify the factors affecting their use of the subway. Chi-square tests show that gender, level of education, and being the only child of the family have the highest impact on using a private car. These variables had no significant influence on students' mode choice to university before the COVID-19 pandemic, when students' mode choice was only a function of their residence location. However, the pandemic has affected priorities for mode choice. For instance, hygiene and social distancing, which were previously insignificant to students, are now among their top criteria, and travel time and cost are less important for students than in the past. As a result, subway use has significantly decreased. Based on the results of the research, when making relevant policies, more attention should be paid to the groups of women, undergraduate students and single children that are more likely to use private cars.