Changes in local travel behaviour before and during the COVID-19 pandemic in Hong Kong

Street-level built environment on SARS-CoV-2 transmission: A study of Hong Kong

Understanding the association between SARS-CoV-2 Spatial Transmission Risk (SSTR) and Built Environments (BE) is crucial for implementing effective pandemic prevention measures. Massive efforts have been made to examine the macro-built environment at the regional level, which has neglected the living service areas at the residential scale. Therefore, this study aims to explore the association between Street-level Built Environments (SLBE) and SSTR in Hong Kong from the 1st to the early 5th waves of the pandemic to address this gap. A total of 3693 visited/resided buildings were collected and clustered by spatial autocorrelation, and then Google Street View (GSV) was employed to obtain SLBE features around the buildings. Eventually, the interpretable machine learning framework based on the random forest algorithm (RFA)-based SHapley Additive exPlanations (SHAP) model was proposed to reveal the hidden non-linear association between SSTR and SLBE. The results indicated that in the high-risk cluster area, street sidewalks, street sanitation facilities, and artificial structures were the primary risk factors positively associated with SSTR, in low-risk cluster areas with a significant positive association with traffic control facilities. Our study elucidates the role of SLBE in COVID-19 transmission, facilitates strategic resource allocation, and guides the optimization of outdoor behavior during pandemics for urban policymakers.

Double-Duty Carers' Health and Wellbeing during COVID-19: Exploring the Role of Mobility of the Care Economy in Southern Ontario, Canada

Double-Duty Carers (DDCs) refer to people who work in the healthcare industry while also providing unpaid care to relatives, friends, or neighbours. The expectations placed on DDCs is expected to grow, and these employees already experience a high caring burden. As such it is important to understand how best to support their health and wellbeing. This paper explores DDCs' wellbeing during the COVID-19 pandemic, focusing an understudied factor: their mobility constraints. Following the Mobility of the Care Economy framework and a qualitative research design, it does so through a thematic analysis of 16 semi-structured interviews with female DDCs in Southern Ontario, Canada. Once data saturation was reached, three mobility pathways during the pandemic were identified, all of which negatively affected DDCs wellbeing. First, some COVID-19 policies (e.g., testing requirements) resulted in increased mobility demands and increased spatiotemporal constraints. Second, the closure of institutions that care for dependents (schools, daycares, day centres) resulted in forced reduced mobility, which increased financial stress. Finally, indirect mobility effects were identified: the reduced mobility of other informal carers increased the workload and emotional strain on DDCs. The paper concludes with a discussion of mobility-related policies that could improve DDC wellbeing.

Residents' preferences for urban agriculture in Shanghai

Although urban agriculture (UA) has been gaining greater attention as part of an effort toward sustainable urban development in China, empirical knowledge is limited. We conducted a discrete choice experiment with 756 residents of Shanghai. Overall, the results indicate that respondents supported UA as they tended to favor one of the UA scenarios presented. Residents prefer UA facilities that utilize environmentally friendly production, offer farming activities, and are equipped with dining facilities. Regularly scheduled educational activities hosted by UA facilities are preferred to those on an irregular basis. In terms of location, residents prefer UA facilities that are close by but do not necessarily want UA facilities to be right in front of them. There is a stronger preference for UA among highly educated populations, those with primary and secondary school students, and those with agricultural work experience. Additionally, various UA attributes affect residents' preferences; overall, there is a high degree of homogeneity in residents' preferences for various UA attributes. The marginal willingness to pay value is slightly higher than expected among the estimation results. There are three reasons for this outcome: the characteristics of UA as a recreational facility in China, the relatively high price level in Shanghai, and the impact of the COVID-19 pandemic.

Human movement avoidance decisions during Coronavirus disease 2019 in Japan

Understanding host behavioral change in response to epidemics is important to forecast the disease dynamics. To predict the behavioral change relevant to the epidemic situation (e.g., the number of reported cases), we need to know the epidemic situation at the moment of decision, which is difficult to identify from the records of actually performed human mobility. In this study, the largest travel accommodation reservation data covering half of the existed accommodations in Japan was analyzed to observe decision-making timings and how it responded to the changing epidemic situation during Japan's Coronavirus Disease 2019 until February 2023. To this end, we measured mobility avoidance index proposed in Ito et al., 2022 to indicate people's decision of mobility avoidance and quantified it using the time-series of the accommodation booking/cancellation data. We observed matches of the peak dates of the mobility avoidance and the number of reported cases, and mobility avoidance changed proportional to the logarithmic number of reported cases. We also found that the slope of mobility avoidance against the change of the logarithmic number of reported cases were similar among the epidemic waves, while the intercept of that was much reduced as the first epidemic wave passed by. People measure the intensity of epidemic by logarithm of the number of reported cases. The sensitivity of their response is established during the first wave and the people's response became weakened after the first experience, as if the number of reported cases were multiplied by a constant small factor.

Genomic surveillance reveals dynamic shifts in the connectivity of COVID-19 epidemics

The maturation of genomic surveillance in the past decade has enabled tracking of the emergence and spread of epidemics at an unprecedented level. During the COVID-19 pandemic, for example, genomic data revealed that local epidemics varied considerably in the frequency of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineage importation and persistence, likely due to a combination of COVID-19 restrictions and changing connectivity. Here, we show that local COVID-19 epidemics are driven by regional transmission, including across international boundaries, but can become increasingly connected to distant locations following the relaxation of public health interventions. By integrating genomic, mobility, and epidemiological data, we find abundant transmission occurring between both adjacent and distant locations, supported by dynamic mobility patterns. We find that changing connectivity significantly influences local COVID-19 incidence. Our findings demonstrate a complex meaning of "local" when investigating connected epidemics and emphasize the importance of collaborative interventions for pandemic prevention and mitigation.

Close contact behaviors of university and school students in 10 indoor environments

Close contact routes, including short-range airborne and large-droplet routes, play an important role in the transmission of SARS-CoV-2 in indoor environments. However, the exposure risk of such routes is difficult to quantify due to the lack of data on the close contact behavior of individuals. In this study, a digital wearable device, based on semi-supervised learning, was developed to automatically record human close contact behavior. We collected 337,056 s of indoor close contact of school and university students from 194.5 h of depth video recordings in 10 types of indoor environments. The correlation between aerosol exposure and close contact behaviors was then evaluated. Individuals in restaurants had the highest close contact ratio (64%), as well as the highest probability of face-to-face pattern (78%) during close contact. Accordingly, university students showed greater exposure potential in dormitories than school students in homes, however, a lower exposure was observed in classrooms and postgraduate student offices in comparison with school students in classrooms. In addition, restaurants had the highest aerosol exposure volume for both short-range inhalation and direct deposition on the facial mucosa. Thus, the classroom was established as the primary indoor environment where school students are exposed to aerosols.

Understanding changing public transit travel patterns of urban visitors during COVID-19: A multi-stage study

COVID-19 has caused huge disruptions to urban travel and mobility. As a critical transportation mode in cities, public transit was hit hardest. In this study, we analyze public transit usage of urban visitors with a nearly two-year smart card dataset collected in Jeju, South Korea - a major tourism city in the Asia Pacific. The dataset captures transit usage behavior of millions of domestic visitors who traveled to Jeju between January 1, 2019 and September 30, 2020. By identifying a few key pandemic stages based on COVID-19 timeline, we employ ridge regression models to investigate the impact of pandemic severity on transit ridership. We then derive a set of mobility indicators - from perspectives of trip frequency, spatial diversity, and travel range - to quantify how individual visitors used the transit system during their stay in Jeju. By further employing time series decomposition, we extract the trend component for each mobility indicator to study long-term dynamics of visitors' mobility behavior. According to the regression analysis, the pandemic had a dampening effect on public transit ridership. The overall ridership was jointly affected by national and local pandemic situations. The time series decomposition result reveals a long-term decay of individual transit usage, hinting that visitors in Jeju tended to use the transit system more conservatively as the pandemic endured. The study provides critical insights into urban visitors' transit usage behavior during the pandemic and sheds light on how to restore tourism, public transit usage, and overall urban vibrancy with some policy suggestions.

The impact of ethnic segregation on neighbourhood-level social distancing in the United States amid the early outbreak of COVID-19

The COVID-19 pandemic has been argued to be the 'great equaliser', but, in fact, ethnically and racially segregated communities are bearing a disproportionate burden from the disease. Although more people have been infected and died from the disease among these minority communities, still fewer people in these communities are complying with the suggested public health measures like social distancing. The factors contributing to these ramifications remain a long-lasting debate, in part due to the contested theories between ethnic stratification and ethnic community. To offer empirical evidence to this theoretical debate, we tracked public social-distancing behaviours from mobile phone devices across urban census tracts in the United States and employed a difference-in-difference model to examine the impact of racial/ethnic segregation on these behaviours. Specifically, we focussed on non-Hispanic Black and Hispanic communities at the neighbourhood level from three principal dimensions of ethnic segregation, namely, evenness, exposure, and concentration. Our results suggest that (1) the high ethnic diversity index can decrease social-distancing behaviours and (2) the high dissimilarity between ethnic minorities and non-Hispanic Whites can increase social-distancing behavior; (3) the high interaction index can decrease social-distancing behaviours; and (4) the high concentration of ethnic minorities can increase travel distance and non-home time but decrease work behaviours. The findings of this study shed new light on public health behaviours among minority communities and offer empirical knowledge for policymakers to better inform just and evidence-based public health orders.

Telework and MaaS adoption in a post-pandemic scenario. Evidence from municipal employees of Padua, Italy

In recent years, the adoption of teleworking has witnessed a significant surge, partly driven by the COVID-19 pandemic. The existing literature suggests that its implementation has elicited mixed reactions from workers; while some workers have expressed satisfaction with its introduction, others prefer to work in a traditional, on-site setting. Concurrently, there has been a growing interest in Mobility-as-a-Service (MaaS) and an increase in the number of companies offering such services. However, there is limited research exploring the relationship between teleworking and the utilization of MaaS. This paper aims to fill this gap, by analyzing (1) which factors influence users to adopt teleworking in a post-pandemic scenario and (2) the relationship between willingness to telework and the propensity to join a MaaS system. An ordered logit model and a mixed logit model were developed to achieve the two goals, respectively. These models were calibrated and validated using data collected from questionnaires administered to Padua Municipality employees between October 2020 and January 2021. As expected, the employees most inclined toward teleworking are those who seek more flexibility and are unable to commute by private means. In addition, results show that employees who expressed the preference to telework more in the future are less likely to adopt MaaS, suggesting that the increased popularity of teleworking due to the pandemic may have a negative effect on the uptake of MaaS. These findings were utilized to formulate several policy recommendations.

Exploring non-linear built environment effects on urban vibrancy under COVID-19: The case of Hong Kong

The coronavirus disease (COVID-19) pandemic has enormously changed the way people perceive and use urban spaces, exacerbating some pre-existing issues including urban vibrancy decline. This study aims to explore built environment effects on urban vibrancy under COVID-19, which will help recalibrate planning models and design principles. Based on multi-source geo-tagged big data of Hong Kong, this study reveals variations in urban vibrancy and employs machine learning modeling and interpretation methods to examine built environment effects on urban vibrancy before, during, and after the outbreak of COVID-19, with review volume of restaurants & food retailers as the indicator for urban vibrancy and built environment depicted from five dimensions (i.e., building form, street accessibility, public transport accessibility, functional density, and functional mixture). We found that (1) urban vibrancy concussively decreased during the outbreak and slowly recovered afterwards; (2) built environment's capability to stimulate urban vibrancy was weakened during the outbreak and restored afterwards; (3) the relationships between built environment and urban vibrancy were non-linear and moderated by the pandemic. This research enriches our understandings of the role of the pandemic in influencing urban vibrancy and its correlation with built environment, enlightening decision makers with nuanced criteria for pandemic-adaptive urban planning and design.

Metro travel and perceived COVID-19 infection risks: A case study of Hong Kong

The COVID-19 pandemic has exerted unprecedented impacts on travel behaviors because of people's increased health precautions and the presence of various COVID-19 containment measures. However, little research has explored whether and how people changed their travel with respect to their perceived local infection risks across space and time. In this article, we relate elasticity and resilience thinking to the changes in metro travel and perceived infection risks at the station or community level over time. Using empirical data from Hong Kong, we measure a metro station's elasticity as the ratio of changes in its average trip length to the COVID-19 cases' footprints around that station. We regard those footprints as a proxy for people's perceived infection risks when making trips to that station. To explore influencing factors on travel in the ups and downs of perceived infection risks, we classify stations based on their elasticity values and examine the association between stations' elasticities and characteristics of stations and their served communities. The findings show that stations varied in elasticity values across space and different surges of the local pandemic. The elasticity of stations can be predicted by socio-demographics and physical attributes of station areas. Stations serving a larger percentage of population with higher education degrees and certain occupations observed more pronounced trip length decrease for the same level of perceived infection risks. The number of parking spaces and retail facilities significantly explained variations in stations' elasticity. The results provide references on crisis management and resilience improvement amid and post COVID-19.

A study on the factors that influenced the choice of transport mode before, during, and after the first lockdown in Milan, Italy

COVID-19 measures implied many changes to travel behaviour and transport mode choice during the pandemic. This study seeks to understand what individual characteristics and travel attributes are related to transport mode choice before, during, and after the first lockdown in Italy. Based on an online survey (carried out in May 2020 in Milan), three multinomial regression models are presented. The results show that and in which measure parameters regarding distance and duration of daily travel are markedly related to transport mode before the lockdown. However, these factors are less significantly associated with the transport mode during and after the lockdown. Meanwhile, factors such as Preferences and Worry about using public transport have more significant relationship with the modal choice during the pandemic. Regarding individual characteristics, women are more likely to use active mode during and after the lockdown. Additionally, two personality traits of Agreeableness, and Openness to experiences are related to transport mode during and after the lockdown, respectively. Overall, this study reveals that in addition to socio-demographic factors, other variables such as worry about using public transport, preference, and personality are associated with the choice of transport mode during the lockdown.

Estimation of trip purposes in public transport during the COVID-19 pandemic: The case of Santiago, Chile

The COVID-19 pandemic strongly affected the mobility of people. Several studies have quantified these changes, for example, measuring the effectiveness of quarantine measures and calculating the decrease in the use of public transport. Regarding the latter, however, a low level of understanding persists as to how the pandemic affected the distribution of trip purposes, hindering the design of policies aimed at increasing the demand for public transport in a post-pandemic era. To address this gap, in this article, we study how the purposes of trips made by public transport evolved during the COVID-19 pandemic in the city of Santiago, Chile. For this, we develop an XGBoost model using the latest available origin-destination survey as input. The calibrated model is applied to the information from smart payment cards during one week in 2018, 2020, and 2021. The results show that during the week of maximum restriction, that is, during 2020, the distribution of trips by purpose varied considerably, with the proportion of trips to work increasing, recreational trips decreasing, and trips for health purposes remaining unchanged. In sociodemographic terms, in the higher-income communes, the decrease in the proportion of trips for work purposes was much greater than that in the communes with lower income. Finally, with the gradual return to in-person activities in 2021, the distribution of trip purposes returned to values similar to those before the pandemic, although with a lower total amount, which suggests that unless relevant measures are taken, the low use of public transportation could be permanent.

Spatiotemporal trends of ultraviolet absorbents in oysters from the Pearl River Estuary, south China during 2015-2020

Ultraviolet absorbents (UVAs) are widely used in various industrial materials, pharmaceuticals, and personal care products, resulting in their frequent occurrences in sediment, water, and biota. However, our understanding of the spatiotemporal characteristics and long-term contamination status of UVAs is still limited. Here, a 6-year biomonitoring study with oysters during wet and dry seasons was conducted to examine the annual, seasonal, and spatial characteristics of UVAs in the Pearl River Estuary (PRE), China. The concentrations of Σ₆UVA ranged from 9.1 to 119 (geometric mean ± standard deviation: 31 ± 22) ng/g dry wt. and peaked in 2018. Significant spatiotemporal variations in UVA contamination were observed. The concentrations of UVAs in oysters during the wet season were higher than the dry season, and concentrations in the more industrialized eastern coast were higher than the western coast (p < 0.05). Environmental factors, including precipitation, temperature, and salinity in water significantly impacted the UVA bioaccumulation in the oysters. The present study highlights that long-term biomonitoring with oysters provided valuable insight in the magnitude and seasonal variation of UVAs in this highly dynamic estuary.

Understanding individual-level travel behavior changes due to COVID-19: Trip frequency, trip regularity, and trip distance

Understanding different mechanisms in trip changes depending on transportation modes due to COVID-19 pandemic is the key to providing practical insights for healthy communities. This study aimed to investigate the impact of the COVID-19 pandemic on individual-level travel behavior in Daejeon Metropolitan City, South Korea. Using smart card and private vehicle records, we explored different travel behaviors exhibited while using buses and private vehicles. An individual's travel behavior was represented in trip frequency, trip regularity, and trip distance and was compared weekly for about three months, including the initial period of pandemic. A significant decrease in trip frequency during non-peak hours on weekdays and during weekends indicates that people reduced non-mandatory trips more than commuter trips. This was also verified in that, as the number of infection cases increased, trip regularity with 24-hour intervals intensified. People maintained the size of their activity boundaries but reduced their daily trip distances. The interesting point is that private vehicle usage increased for shorter trip distances while bus usage dropped regardless of the ranges of trip distances under the pandemic. The findings provide evidence of possible inequality issues in transportation during the pandemic and can help make precautionary policies for future pandemics.

Estimating Mode of Transport in Daily Mobility during the COVID-19 Pandemic Using a Multinomial Logistic Regression Model

At the beginning of 2020 there was a spinning point in the travel behavior of people around the world because of the pandemic and its consequences. This paper analyzes the specific behavior of travelers commuting to work or school during the COVID-19 pandemic based on a sample of 2000 respondents from two countries. We obtained data from an online survey, applying multinomial regression analysis. The results demonstrate the multinomial model with an accuracy of almost 70% that estimates the most used modes of transport (walking, public transport, car) based on independent variables. The respondents preferred the car as the most frequently used means of transport. However, commuters without car prefer public transport to walking. This prediction model could be a tool for planning and creating transport policy, especially in exceptional cases such as the limitation of public transport activities. Therefore, predicting travel behavior is essential for policymaking based on people's travel needs.

Factors affecting public transport performance due to the COVID-19 outbreak: A worldwide analysis

In this paper we investigate the public transport trip frequency variations, as well as the reasons that led to the shift away from public transport means, due to the COVID-19 pandemic. We studied relevant data from the Moovit platform, and we compared operational and trip frequency characteristics of public transport systems before and after the outbreak of the pandemic in 87 cities worldwide. On average, waiting times at public transport stops/stations increased while trip distances decreased, apparently due to the mobility restriction and social distancing measures implemented in 2020. Most of the Moovit users who said that they abandoned public transport in 2020 were found in Italy and Greece. We developed linear regression analysis models to investigate (among the 35 variables examined in the study) the relationship between public transport abandonment rates and socioeconomic factors, quality of service characteristics, and indicators of pandemic's spread. Empirical findings show that public transport dropout rates are positively correlated with the COVID-19 death toll figures, the cleanliness of public transport vehicles and facilities, as well as with the income inequality (GINI) index of the population, and thus reconfirm previous research findings. In addition, the waiting time at stops/stations and the number of transfers required for commute trips appeared to be the most critical public transport trip segments, which significantly determine the discontinuation of public transport use under pandemic circumstances. Our research findings indicate specific aspects of public transport services, which require tailored adjustments in order to recover ridership in the post-pandemic period.

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.

Who is returning to public transport for non-work trips after COVID-19? Evidence from older citizens' smart cards in the UK's second largest city region

Harnessing a unique data source - longitudinal travel smartcard data linked to passenger demographics from 2019 to 2022 - we use methods of survival analysis to model the recovery of public transport patronage among 183,891 senior citizens resident in the West Midlands metropolitan region in the United Kingdom. Comparing pre and peri-pandemic patronage, we identify pronounced social and spatial inequalities in the speed of return to public transport. We find that male, younger and non-White passengers are more likely to return to public transport as soon as movement restrictions were lifted, whereas passengers from White ethnic background and affluent areas do not return to public transport within the first year after the outbreak. Pronounced social inequalities persist into the middle of 2021, and only thence they began to attenuate as part of a wider return to public transport among passengers post retirement age. In 2022, 80% of these passengers have returned to public transport but the frequency of use has remained lower than prior to the pandemic. We discuss implications for transport policy and planning.

Quantifying human mobility resilience to the COVID-19 pandemic: A case study of Beijing, China

Human mobility, as a fundamental requirement of everyday life, has been most directly impacted during the COVID-19 pandemic. Existing studies have revealed its ensuing changes. However, its resilience, which is defined as people's ability to resist such impact and maintain their normal mobility, still remains unclear. Such resilience reveals people's response capabilities to the pandemic and quantifying it can help us better understand the interplay between them. Herein, we introduced an integrated framework to quantify the resilience of human mobility to COVID-19 based on its change process. Taking Beijing as a case study, the resilience of different mobility characteristics among different population groups, and under different waves of COVID-19, were compared. Overall, the mobility range and diversity were found to be less resilient than decisions on whether to move. Females consistently exhibited lower resilience than males; middle-aged people exhibited the lowest resilience under the first wave of COVID-19 while older adult's resilience became the lowest during the COVID-19 rebound. With the refinement of pandemic-control measures, human mobility resilience was enhanced. These findings reveal heterogeneities and variations in people's response capabilities to the pandemic, which can help formulate targeted and flexible policies, and thereby promote sustainable and resilient urban management.

Optimizing Operation Time and Travel Distance for Motorcycle Ambulances in Emergency Medical Services

INTRODUCTION

The motorcycle ambulance is used for quick access to patients. The response time to reach the patient takes less time than with a van ambulance. Moreover, accidents involving ambulances tend to be higher. However, at present, there is no study regarding the appropriate situation used of motorcycle ambulances in Emergency Medical Services (EMS) in Thailand.

STUDY OBJECTIVE

This study aims to optimize the travel distance and the operation time of motorcycle ambulances used.

METHODS

This study was a prospective, randomized controlled study at the EMS unit of Srinagarind Hospital, Thailand. The data collection period was from November 2021 through May 2022. All data involving dispatch of both ambulances in need were collected.

RESULTS

A total of 2,398 cases of EMS operation were examined. The mean age of the patients in the motorcycle ambulance group was 42.5 (SD = 6.5) years, and 51.3% (n = 616) were male. The response time for motorcycle ambulances and van ambulances during the operation time between 6:00am-9:00am was 6.2 minutes and 9.1 minutes, respectively. The response times for motorcycle ambulances and van ambulances regarding distance traveled from 0-5km were 4.2 minutes and 7.5 minutes, respectively (P <.001); distance traveled from 5-10km were 6.3 minutes and 8.2 minutes, respectively (P = .010).

CONCLUSION

The motorcycle ambulance can reach patients faster than the ambulance at the operation time from 6:00am-9:00am and 3:00pm-6:00pm. This study focused on the distance less than 10 kilometers.

Applying a Goal-Directed Behavior Model to Determine Risk Perception of COVID-19 and War on Potential Travelers' Behavioral Intentions

The purpose of this study is to verify the influence of the relationship between risk perception of COVID-19 and the war-applied Model of Goal-directed Behavior (MGB) based on stimulus-organism-response (SOR) and potential travelers' behavioral intention. In addition, this study attempted to verify the relationship among uncertainty toward international travel, mental well-being toward international travel, and desire toward travelers' behavioral intention. Moreover, we examined the moderating effect of gender (female vs. male) among all variables for dependents. The survey was conducted on potential travelers in Korea. As for the survey period, a survey was conducted for one month beginning on 2 September 2022. Of the total 413 surveys, 361 surveys were used for the final analysis, and 52 unfaithful surveys were excluded. In addition, demographic, CFA, correlation analysis, structural equation modeling, and moderation effect analysis were verified using SPSS and AMOS. For the data analysis, we used SPSS 18.0 and Amos 20.0 to perform factor analysis and SEM. Significant effects were found in support for Hypotheses 1-5. Further, when it comes to the difference of gender on the relationship between all the variables, while no significant effect was found for Hypotheses 6a,c,e,g, a significant effect was found for Hypotheses 6b,d,f. Thus, H6a,c,e were rejected and H6b,d,f were supported. It was found that females had a greater influence on mental health and desire for overseas travel than males, but it was found that there was no difference between females and males in the relationship between desire and behavioral intention. Therefore, it was possible to verify that the MGB desire is an important psychological variable for both females and males. Furthermore, these findings offer academic practical implications to travel and tourism companies by presenting basic data based on the results of empirical research analysis in the context of the current dangerous situation.

Results from the Hong Kong's 2022 report card on physical activity for children and adolescents

Background/objective

This paper aimed to summarize the findings of the third (2022) Active Healthy Kids Hong Kong Report Card on Physical Activity for Children and Adolescents and evaluate the secular trends of physical activity related indicators.

Methods

Five behavioral indicators (Overall Physical Activity, Organized Sport and Physical Activity, Active Play, Active Transportation, and Sedentary Behavior), three outcome indicators (Physical Fitness, Sleep, and Obesity) and four sources of influence indicators (Family and Peers, School, Community and Environment, and Government) were assigned a letter grade (ranging from A+ to F or incomplete) based on the best available evidence following a harmonized approach developed by the Active Healthy Kids Global Alliance. Data sources included published journal articles, government reports, manual searches, and personal contacts; and consisted of both pre-COVID-19 and after-COVID-19 evidence.

Results

Grades for Overall Physical Activity (D-∗∗) and Sedentary Behavior (D) deteriorated compared to the 2018 Report Card. The other three behavioral indicators, Organized Sport and Physical Activity, Active Play, and Active Transportation, were assigned B-, D, and B+, respectively. Physical Fitness (D), Sleep (C-), and Obesity (D-) obtained the same grades as in the 2018 Report Card. School (B) and Government (C+) grades slightly improved, while Community and Environment grade (B) was stable. Family and Peers was not graded due to insufficient evidence.

Conclusions

Despite slight improvements in influence indicators, physical activity and sedentary behavior have changed unfavorably for children and adolescents in Hong Kong. Strategic investments are needed to improve adoption and implementation of effective interventions.

Jobs-housing relationships before and amid COVID-19: An excess-commuting approach

The outbreak of COVID-19 and subsequent pandemic containment measures have significantly affected our daily life, which has been extensively examined in the existing scholarship. However, the existing scholarship has done little on the jobs/housing relationship impacts of COVID-19. We attempted to fill this gap by using an excess-commuting approach. The approach allows us to analyse a series of jobs-housing matrices based on the location-based service big data of around fifty million individuals in the Pearl River Delta (PRD), China before and amid COVID-19. In the PRD, a zero-COVID policy was implemented, which presents a distinct and interesting context for our study. We found that after the COVID-19 outbreak: (1) residences and employment became more centrally located in downtowns, which is opposite to the suburbanization trend elsewhere; (2) in the whole PRD, the minimum and maximum commutes became smaller while the actual commute became larger, indicating the simultaneous presences of some paradoxical phenomena: a better spatial juxtaposition of jobs and housing, more compressed distribution of jobs and housing, and longer average actual commutes; (3) inter-city commutes between large cities were significantly refrained and decreased, while new inter-city commuters between smaller cities emerged; (4) it was more likely for the less-educated and female workers to see smaller minimum commutes amid COVID-19. This paper illustrates the potential of big data in the longitudinal study on jobs-housing relationships and excess commuting. It also produces new insights into such relationships in a unique context where stringent anti-COVID-19 policies have been continuously in place.

Changes on local travel behaviors under travel reduction-related interventions during COVID-19 pandemic: a case study in Hong Kong

The emerging Omicron variant poses a serious threat to human health. Public transports play a critical role in infection spread. Based on the data of nearly 4 billion smartcard uses, between January 1, 2019 and January 31, 2021 from the Mass Transit Railway Corporation of Hong Kong, we analyzed the subway travel behavior of different population groups (adults, children, students and senior citizens) due to the COVID-19 pandemic and human travel behavior under different interventions (e.g. work suspension, school closure). Due to the pandemic, the number of MTR passengers (the daily number of passengers in close proximity in subway carriages) decreased by 37.4% (40.8%) for adults, 80.3% (78.5%) for children, 71.6% (71.6%) for students, and 33.5% (36.1%) for senior citizens. Due to work from home (school suspension), the number of contacted adults (students/children) in the same carriage during the rush hours decreased by 39.6% (38.6%/43.2%). If all workers, students, and children were encouraged to commute avoiding rush hours, the possible repeated contacts during rush hour of adults, children and students decreased by 73.3%, 77.9% and 79.5%, respectively. Since adults accounted for 87.3% of the total number of subway passengers during the pandemic, work from home and staggered shift pattern of workers can reduce the infection risk effectively. Our objective is to find the changes of local travel behavior due to the pandemic. From the perspective of public transports, the results provide a scientific support for COVID-19 prevention and control in cities.

Changes in mobility amid the COVID-19 pandemic in Sapporo City, Japan: An investigation through the relationship between spatiotemporal population density and urban facilities

By the end of 2021, the Omicron variant of coronavirus disease 2019 had become the dominant cause of a worldwide pandemic crisis. This demands a deeper analysis to support policy makers in creating interventions that not only protect people from the pandemic but also remedy its negative effects on the economy. Thus, this study investigated people's mobility changes through the relationship between spatiotemporal population density and urban facilities. Results showed that places related to daily services, restaurants, commercial areas, and offices experienced decreased visits, with the highest decline belonging to commercial facilities. Visits to health care and production facilities were stable on weekdays but increased on holidays. Educational institutions' visits decreased on weekdays but increased on holidays. People's visits to residential housing and open spaces increased, with the rise in residential housing visits being more substantial. The results also confirmed that policy interventions (e.g., declaration of emergency and upgrade of restriction level) have a great impact on people's mobility in the short term. The findings would seem to indicate that visit patterns at service and restaurant places decreased least during the pandemic. The analysis outcomes suggest that policy makers should pay more attention to risk perception enhancement as a long-term measure. Furthermore, the study clarified the population density of each facility type in a time series. Improving model performance would be promising for tracking and predicting the spread of future pandemics.

The impact of COVID-19 and related containment measures on Bangkok's public transport ridership

The COVID-19 pandemic and related measures used to contain its spread affected public transport ridership in cities around the world. In Thailand, the government issued 41 Royal Decrees between April 2020 and December 2021 to mitigate the spread of the pandemic. In this study, we investigate how Bangkok's public transport services (bus, metro, and boat) have been affected during this period by analyzing the daily ridership data, confirmed COVID-19 cases, and aggregated travel trends by trip destinations using from Google mobility reports. The results show that public transport ridership decreased as daily COVID cases increased and the levels of restraining measures became higher. However, other factors, such as relative strictness compared to earlier measures and sequencing of the measures seems to have had an impact on the ridership. Moreover, the impact on ridership trends is unique for each of the three modes. Bus and metro ridership appear to be more sensitive to the changes in restrictions than the boats. Bus and metro ridership also shows similar changes in the travel trends concerning the place of visit. The findings reported here provide first insights into how Bangkok's public transport systems were affected and suggest the rationale of why different public transport modes were affected differently. These results can be useful for researchers and for decision-makers who plan and design policies and measures for public transport services.

A supervised machine learning model for imputing missing boarding stops in smart card data

Public transport has become an essential part of urban existence with increased population densities and environmental awareness. Large quantities of data are currently generated, allowing for more robust methods to understand travel behavior by harvesting smart card usage. However, public transport datasets suffer from data integrity problems; boarding stop information may be missing due to imperfect acquirement processes or inadequate reporting. This study introduces a supervised machine learning method to impute missing boarding stops based on ordinal classification using GTFS timetable, smart card, and geospatial datasets. A new metric, Pareto Accuracy, is suggested to evaluate algorithms where classes have an ordinal nature. The results are based on a case study in the city of Beer Sheva, Israel, consisting of one month of smart card data. We show that our proposed method is robust to irregular travelers and significantly outperforms well-known imputation methods without the need to mine any additional datasets. The data validation from another Israeli city using transfer learning shows the presented model is general and context-free. The implications for transportation planning and travel behavior research are further discussed.

Perceived risks of transit use during COVID-19: Correlates and strategies from a case study in El Paso, Texas

Introduction

COVID-19 has led to public transportation ridership plummeting and thus created fiscal crises and operational difficulties for transit operators. Although transit services remain essential for people with limited access to alternative transportation modes, the unfavorable public view keeps potential riders from transit. The public transportation industry is in dire need of restoring trust and recovering ridership.

Methods

In July 2020, we conducted an online survey in El Paso, Texas, to investigate COVID-19 risk perceptions related to transit use. A total of 712 valid responses, consisting of 613 English and 99 Spanish responses, were included in the analyses.

Results and conclusions

Descriptive and bivariate analyses showed many choice transit riders changed their mobility patterns by abandoning public transportation. Survey participants reported that social distancing and cleaning services were very important in encouraging them to return to transit. The free fare policy was also a stimulus for the existing riders to continue their transit use during the pandemic. The multivariable analysis results highlighted that public knowledge plays a vital role in COVID-19 risk perceptions and transit use decisions. Major opportunities exist in terms of public awareness campaigns that inform the public about the COVID-19 related safety measures transit operators are implementing. We further suggest some practical strategies to enhance public communication and help transit operators recover from COVID-19. This study offers timely insights for public policy stakeholders, including transit agencies, to better utilize their resources to protect public health, regain public confidence, and bring passengers back.

Non-pharmaceutical interventions and urban vehicle mobility in Seoul during the COVID-19 pandemic

Non-pharmaceutical interventions to control human mobility are important in preventing COVID-19 transmission. These interventions must also help effectively control the urban mobility of vehicles, which can be a safer travel mode during the pandemic, at any time and place. However, few studies have identified the effectiveness of vehicle mobility in terms of time and place. This study demonstrates the effectiveness of non-pharmaceutical interventions at both local and national levels on intra- and inter-urban vehicle mobility by time of day in Seoul, South Korea, by applying the autoregressive integrated moving average with exogenous variables. The study found that social distancing measures at the national level were effective for intra-urban vehicle mobility, especially at night-time, but not for inter-urban mobility. Information provision with emergency text messages by cell phone was effective in reducing vehicle mobility in daytime and night-time, but not during morning peak hours. At the local level, both restrictions on late-night transit operations and stricter social distancing measures were mostly significant in reducing night-time mobility only in intra-urban areas. The study also indicates when (what time of the day), where (which area within the city), and which combination strategy could be more effective in containing urban vehicle mobility. This study recommends that restrictions on human mobility should also be extended to vehicle mobility, especially in inter-urban areas and during morning peak hours, by systematically designing diverse non-pharmaceutical interventions.

Mass Screening of SARS-CoV-2 With Rapid Antigen Tests in a Receding Omicron Wave: Population-Based Survey for Epidemiologic Evaluation

BACKGROUND

The COVID-19 Omicron BA.2 epidemic wave in Hong Kong peaked in the first quarter of 2022. Following the implementation of stringent public health measures, the daily number of reported cases fell from over 50,000 to below 2000. Although outbreaks steadily receded, the government rolled out a 3-day "voluntary universal rapid testing" campaign to invite all citizens to self-perform a rapid antigen test (RAT) daily to identify undetected prevalent infections.

OBJECTIVE

This study aimed to evaluate the uptake and results of RAT mass screening to estimate the population's residual epidemic burden and assess the risk of further transmission.

METHODS

A cross-sectional study comprising an open web-based population-based survey was conducted a week after the RAT campaign. Participants were asked to report their COVID-19 vaccination and infection history and the RAT performance and test result during the period. They were also invited to report their coliving individuals' test performance and results. Reasons for nonuptake were enquired. Testing and positive rates were age-adjusted. Determinants of undergoing RAT were identified using univariable and multivariable logistic regression models.

RESULTS

In total, particulars from 21,769 individuals were reported by 8338 participants. The overall age-adjusted testing rate was 74.94% (95% CI 73.71%-76.18%), with over 80% of participants in the age groups between 45-84 years having self-performed RAT during the campaign period. After age-adjustment, 1.03% (95% CI 0.86%-1.21%) of participants tested positive. The positive rates in the age groups between 20-29 years and >84 years exceeded 2%. Taking into account the positive rate and 5819 reported cases during the period, the cases identified in the campaign might account for 7.65% (95% CI 6.47%-9.14%) of all infections. Testers were more likely to be female, older, not previously diagnosed with COVID-19, and have received COVID-19 vaccination. Adjusting for the number of household members, those living with a child aged <12 years and whose household members were also tested were more likely to have self-performed an RAT. Main reasons for not performing an RAT included the absence of symptoms (598/1108, 53.97%), disbelief of the appropriateness of the campaign as an antiepidemic measure (355/1108, 32.04%), and a recent COVID-19 diagnosis (332/1108, 29.96%).

CONCLUSIONS

The residual population burden remained substantial in spite of the clear evidence of a receding epidemic wave. Despite caution in generalization to the Hong Kong population, the high participation rate in mass screening indicated that the voluntary RAT was well accepted, making it a feasible option for implementation as a complementary means of public health surveillance.

Unraveling the dynamic impacts of COVID-19 on metro ridership: An empirical analysis of Beijing and Shanghai, China

The outbreak of coronavirus disease 2019 (COVID-19) has had severely disruptive impacts on transportation, particularly public transit. To understand metro ridership changes due to the COVID-19 pandemic, this study conducts an in-depth analysis of two Chinese megacities from January 1, 2020, to August 31, 2021. Generalized linear models are used to explore the impact of the COVID-19 pandemic on metro ridership. The dependent variable is the relative change in metro ridership, and the independent variables include COVID-19, socio-economic, and weather variables. The results suggested the following: (1) The COVID-19 pandemic has a significantly negative effect on the relative change in metro ridership, and the number of cumulative confirmed COVID-19 cases within 14 days performs better in regression models, which reflects the existence of the time lag effect of the COVID-19 pandemic. (2) Emergency responses are negatively associated with metro system usage according to severity and duration. (3) The marginal effects of the COVID-19 variables and emergency responses are larger on weekdays than on weekends. (4) The number of imported confirmed COVID-19 cases only significantly affects metro ridership in the weekend and new-normal-phase models for Beijing. In addition, the daily gross domestic product and weather variables are significantly associated with metro ridership. These findings can aid in understanding the usage of metro systems in the outbreak and new-normal phases and provide transit operators with guidance to adjust services.

Close contact behavior-based COVID-19 transmission and interventions in a subway system

During COVID-19 pandemic, analysis on virus exposure and intervention efficiency in public transports based on real passenger's close contact behaviors is critical to curb infectious disease transmission. A monitoring device was developed to gather a total of 145,821 close contact data in subways based on semi-supervision learning. A virus transmission model considering both short- and long-range inhalation and deposition was established to calculate the virus exposure. During rush-hour, short-range inhalation exposure is 3.2 times higher than deposition exposure and 7.5 times higher than long-range inhalation exposure of all passengers in the subway. The close contact rate was 56.1 % and the average interpersonal distance was 0.8 m. Face-to-back was the main pattern during close contact. Comparing with random distribution, if all passengers stand facing in the same direction, personal virus exposure through inhalation (deposition) can be reduced by 74.1 % (98.5 %). If the talk rate was decreased from 20 % to 5 %, the inhalation (deposition) exposure can be reduced by 69.3 % (73.8 %). In addition, we found that virus exposure could be reduced by 82.0 % if all passengers wear surgical masks. This study provides scientific support for COVID-19 prevention and control in subways based on real human close contact behaviors.

Data-driven analysis of the impact of COVID-19 on Madrid's public transport during each phase of the pandemic

COVID-19 has become a major global issue with large social-economic and health impacts, which led to important changes in people's behavior. One of these changes affected the way people use public transport. In this work we present a data-driven analysis of the impact of COVID-19 on public transport demand in the Community of Madrid, Spain, using data from ticket validations between February and September 2020. This period of time covers all stages of pandemic in Spain, including de-escalation phases. We find that ridership has dramatically decreased by 95% at the pandemic peak, recovering very slowly and reaching only half its pre-pandemic levels at the end of September. We analyze results for different transport modes, ticket types, and groups of users. Our work corroborates that low-income groups are the most reliant on public transportation, thus observing significantly lower decreases in their ridership during pandemic. This paper also shows different average daily patterns of public transit demand during each phase of the pandemic in Madrid. All these findings provide relevant information for transit agencies to design responses to an emergence situation like this pandemic, contributing to extend the global knowledge about COVID-19 impact on transport comparing results with other cities worldwide.

Potential long-term effects of Covid-19 on telecommuting and environment: An Italian case-study

To contain the sudden spread of SARS-CoV-2, many governments encouraged people to work from home, generating an unprecedented diffusion of this activity. Furthermore, Covid-19 has induced drastic changes in everyday life and travel habits, which might persist in the future. This paper aims to understand and estimate the potential long-term impacts of telework on the environment due to the pandemic, by analyzing factors affecting the frequency of telecommuting, the mode choice for traveling to work, and pollutant emissions generated by these trips. Data from a mobility survey administered in Padova (Italy) was used. Results indicate that Covid-19 could cause a rebound effect reversing the positive impacts of working from home, since, even if the number of trips could be reduced, many shifts towards non-sustainable travel modes could occur. The promotion of telework should be combined with measures fostering sustainable travel habits to pave the way towards a future green mobility.

Analysis of the impact of non-compulsory measures on human mobility in Japan during the COVID-19 pandemic

To curb the spread of the COVID-19 pandemic, countries around the world have imposed restrictions on their population. This study quantitatively assessed the impact of non-compulsory measures on human mobility in Japan during the COVID-19 pandemic, through the analysis of large-scale anonymized mobile-phone data. The non-negative matrix factorization (NMF) method was used to analyze mobile statistics data from the Tokyo area. The results confirmed the suitability of the NMF method for extracting behavior patterns from aggregated mobile statistics data. Data analysis results indicated that although non-pharmaceutical interventions (NPIs) measures adopted by the Japanese government are non-compulsory and rely largely on requests for voluntary self-restriction, they are effective in reducing population mobility and motivating people to practice social distancing. In addition, the current study compared the mobility change in three cities (i.e., Tokyo, Osaka, and Hiroshima), and discussed their similarity and difference in behavior pattern changes during the pandemic. It is expected that the analytical tool proposed in this study can be used to monitor mobility changes in real-time during the pandemic, as well as the long-term evolution of population mobility patterns in the post-pandemic phase.

Impacts of COVID-19 Travel Restriction Policies on the Traffic Quality of the National and Provincial Trunk Highway Network: A Case Study of Shaanxi Province

According to recent research, the COVID-19 pandemic has impacted road traffic quality. This study aims to analyze the impacts of COVID-19 travel restriction policies on the traffic quality of the national and provincial trunk highway network (NPTHN) in Shaanxi Province. We collected the traffic data of the NPTHN for three consecutive years (from 2019 to 2021), before and after the COVID-19 outbreak, including weekly average daily traffic, weekly traffic interruption times, weekly traffic control time, weekly traffic accidents, weekly traffic injuries, and weekly traffic deaths. Using descriptive statistics and dynamic analysis methods, we studied the safety and service levels of the NPTHN. We set up an assessment model of the NPTHN operational orderliness through dissipative structure theory and entropy theory to study the operational orderliness of the NPTHN. Results show that in 2020, the service level, safety level, and operational orderliness of the NPTHN dropped to the lowest levels. The pandemic was gradually brought under control, and the travel restriction policies were gradually reduced and lifted. The adverse impacts on the operational orderliness of the NPTHN decreased, but the operational orderliness did not yet recover to the pre-pandemic level. Meanwhile, the service and safety levels of the NPTHN did not recover. Taken together, the COVID-19 travel restriction policies had adverse impacts on the traffic quality of the NPTHN in Shaanxi Province.

Designing for COVID-2x: Reflecting on Future-Proofing Human Habitation for the Inevitable Next Pandemic

The COVID-19 pandemic of 2020–2022 has revealed the vulnerability of modern society to a highly contagious airborne virus. Many spaces in the urban and built environment designed during the late twentieth and early twenty-first century are ill-suited to maintain the level of social distancing required to reduce the probability of virus transmission. Enclosed spaces—in particular, communal circulation spaces such as corridors, elevators and lobbies—have proven loci of transmission, together with circulating reticulated air and lack of proper ventilation. While urban planning needs to incorporate the lessons learnt during COVID-19 in order to future-proof our communities through the provision of well-designed greenspaces, the main burden will fall on architects, who will play an instrumental role in designing buildings that are fit-for purpose. This conceptual paper reviews the status quo and discusses a number of strategies to future-proof human habitation for the inevitable next pandemic.

COVID-19 effects on urban driving, walking, and transit usage trends: Evidence from Indian metropolitan cities

The outbreak of the COVID-19 pandemic disrupted all walks of life, including the transportation sector. Fear of the contagion coupled with government regulations to restrict mobility altered the travel behavior of the public. This study proposes integrating freely accessible aggregate mobility datasets published by tech giants Apple and Google, which opens a broader avenue for mobility research in the light of difficult data collection circumstances. A comparative analysis of the changes in usage of different mobility modes during the national lockdown and unlock policy periods across 6 Indian cities (Bangalore, Chennai, Delhi, Hyderabad, Mumbai, and Pune) explain the spatio-temporal differences in mode usages. The study shows a preference for individual travel modes (walking and driving) over public transit. Comparisons with pre-pandemic mode shares present evidence of inertia in the choice of travel modes. Association investigations through generalized linear mixed-effects models identify income, vehicle registrations, and employment rates at the city level to significantly impact the community mobility trends. The methods and interpretations from this study benefit government, planners, and researchers to boost informed policymaking and implementation during a future emergency demanding mobility regulations in the high-density urban conglomerations.

The breathing human infrastructure: Integrating air quality, traffic, and social media indicators

Outdoor air pollution is a complex system that is responsible for the deaths of millions of people annually, yet the integration of interdisciplinary data necessary to assess air quality's multiple metrics is still lacking. This case study integrates atmospheric indicators (concentrations of criteria pollutants including particulate matter and gaseous pollutants), traffic indicators (permanent traffic monitoring station data), and social indicators (community responses in Twitter archives) representing the interplay of the three critical pillars of the United Nations' Triple Bottom Line: environment, economy, and society. During the watershed moment of the COVID-19 pandemic lockdowns in Florida, urban centers demonstrated the gaps and opportunities for understanding the relationships, through correlations rather than causations, between urban air quality, traffic emissions, and public perceptions. The relationship between the perception and the traffic variables were strongly correlated, however no correlation was observed between the perception and actual air quality indicators, except for NO₂. These observations might consequently infer that traffic serves as people's proxy for air quality, regardless of actual air quality, suggesting that social media messaging around asthma may be a way to monitor traffic patterns in areas where no infrastructure currently exists or is prohibited to build. It also indicates that people are less likely to be reliable sensors to accurately measure air quality due to bias in their observations of traffic volume and/or confirmation biases in broader social discourse. Results presented herein are of significance in demonstrating the capacity for interdisciplinary studies to consider the predictive capacities of social media and air pollution, its use as both lever and indicator of public support for air quality legislation and clean-air transitions, and its ability to overcome limitations of surface monitoring stations.

ANN-Based traffic volume prediction models in response to COVID-19 imposed measures

Many countries around the globe have imposed several response measures to suppress the rapid spread of the COVID-19 pandemic since the beginning of 2020. These measures have impacted routine daily activities, along with their impact on economy, education, social and recreational activities, and domestic and international travels. Intuitively, the different imposed policies and measures have indirect impacts on urban traffic mobility. As a result of those imposed measures and policies, urban traffic flows have changed. However, those impacts are neither measured nor quantified. Therefore, estimating the impact of these combined yet different policies and measures on urban traffic flows is a challenging task. This paper demonstrates the development of an artificial neural networks (ANN) model which correlates the impact of the imposed response measure and other factors on urban traffic flows. The results show that the adopted ANN model is capable of mapping the complex relationship between traffic flows and the response measures with a high level of accuracy and good performance. The predicted values are closed to the observed ones. They are clustered around the regression line, with a coefficient of determination ( R 2 ) of 0.9761. Furthermore, the developed model can be generalized to determine the anticipated demand levels resulted from imposing any of the response measures in the post-pandemic era. This model can be used to manage traffic during mega-events. It can be also utilized for disaster or emergency situations, where traffic flow estimates are highly required for operational and planning purposes.

Accessibility and Essential Travel: Public Transport Reliance Among Senior Citizens During the COVID-19 Pandemic

Using smart card travel data, we compare demand for bus services by passengers of age 65 or older prior to and during the COVID-19 pandemic to identify public transport-reliant users residing in more car-dependent environments-i.e., people who rely on public transport services to carry out essential activities, such as daily shopping and live in areas with low public transport accessibility. Viewing lockdowns as natural experiments, we use spatial analysis combined with multilevel logistic regressions to characterize the demographic and geographic context of those passengers who continued to use public transport services in these areas during lockdown periods, or quickly returned to public transport when restrictions were eased. We find that this particular type of public transport reliance is significantly associated with socio-demographic characteristics alongside urban residential conditions. Specifically, we identify suburban geographies of public transport reliance, which are at risk of being overlooked in approaches that view public transport dependence mainly as an outcome of deprivation. Our research demonstrates once again that inclusive, healthy and sustainable mobility can only be achieved if all areas of metropolitan regions are well and reliably served by public transport.

Hong Kong, the Virus and Illiberalism: Between Flattening the Curve and an Authoritarian Slide?

Can the pandemic measures be used to advance particular political means? The question of correlation between illiberal legal changes adopted amongst the wave of legislation focused on battling COVID has arisen in a number of countries around the world; as an increasing number of states finds leaving restrictions behind in 2022, however, Hong Kong is still battling the Omicron wave of the pandemic. Ever since its transition to China in 1997, Hong Kong has retained its place on the world stage as an international business hub and, while getting closer to the Mainland, enjoyed the freedoms provided by its SAR status. At the same time, by the end of the second decade of the twenty-first century the tensions between the pro-democratic inclinations of the large part of the city's population and pro-Mainland disposition of the SAR's political elite began to rise, with proposals of various legal acts put forward by the local government often perceived as encroaching freedoms. The street-level 'standoff' between the authorities and the people was brought to a halt by the COVID-19 pandemic; and, with protesting rendered virtually impossible, a number of controversial legal changes were introduced by the government taking advantage of the situation, which, together with the anti-pandemic measures have continued to negatively impact the city's financial hub status. The purpose of this paper is to analyse the correlation between Hong Kong's fight with the pandemic and the local government's taking advantage of the situation in order to implement illiberal legislation, and its aftermath.

Impact of COVID-19 travel-restriction policies on road traffic accident patterns with emphasis on cyclists: A case study of New York City

Since the COVID-19 outbreak, travel-restriction policies widely adopted by cities across the world played a profound role in reshaping urban travel patterns. At the same time, there has been an increase in both cycling trips and traffic accidents involving cyclists. This paper aims to provide new insights and policy guidance regarding the effect of COVID-19 related travel-restriction policies on the road traffic accident patterns, with an emphasis on cyclists' safety. Specifically, by analysing the accidents data in the New York City and estimating three fixed effects logit models on the occurrence of different types of accidents in a given zip code area and time interval, we derived the following findings. First, while the overall number of road traffic accidents plummeted in the NYC after the stay-at-home policy was implemented, the average severity increased. The average number of cyclists killed or injured per accidents more than tripled relative to levels in similar times in previous years. Second, the declaration of the New York State stay-at-home order was significantly associated with a higher risk of accidents resulting in casualties. The number of Citi Bike trips in the area at the time overwhelmingly predicted severe risk for cyclists. Last, we applied the models to detect hot zones for cyclists' severe accidents. We found that these hot zones tend to be spatially and temporally concentrated, making it possible to devise targeted safety measures. This paper contributes to the understanding of the impact of COVID-19 travel-restriction policies on accidents involving cyclists, reveals higher risks for cyclists as an unintended consequence of travel-restriction policies, and provides an analytical tool for road safety impact evaluation should future travel restrictions be considered.

Risk perceptions of COVID-19 transmission in different travel modes

COVID-19 pandemic has caused adverse impacts on different aspects of life around the globe, including travelers' mode choice behavior. To make their travel safe, transportation planners and policymakers need to understand people's perceptions of the risk of COVID-19 transmission in different travel modes. This study aimed to estimate mode-wise perceived risk of viral transmission and identify the factors that influenced the perceived risk in Bangladesh. The study used a five-point Likert scale to measure the perceived risk of COVID-19 transmission in each travel mode. Using ordinal logistic regression models, the study explored the factors that influenced the perceived risk of COVID-19 transmission in different travel modes. The study found that people perceived a very high risk of viral transmission in public transport (bus), moderate risk in shared modes (rickshaw, auto-rickshaw, ridesharing), and very low risk in private modes (private car, motorcycle/scooter, walking, cycling). Such high-risk perception of viral transmission in public transport and shared modes might lead to a modal shift to private modes, which would worsen urban transport problems and undermine sustainable transportation goals. The study also found that socio-economic factors (gender, age, income) significantly influenced perceived risks in all travel modes. Contrarily, psychological factors (worry, care, and trust) were significant only for public and shared modes, but not for private modes. Lastly, travel behavior-related factors influenced perceived risk in shared and private modes.

COVID-19 and urban planning: Built environment, health, and well-being in Greek cities before and during the pandemic

The coronavirus disease (COVID-19) pandemic has led to dramatic changes in quality of life, bringing to the forefront of the debate the question of planning and design of pandemic-resilient cities. Using quasi-longitudinal survey data (via a social media campaign) and geospatial data from Greek cities, we evaluate changes in health and well-being during COVID-19 compared to the pre-COVID-19 period, and then explore in detail how specific built environment characteristics in Athens and Thessaloniki relate to health and well-being before and during COVID-19. Results suggest that overall health, life satisfaction, happiness, personal relationships satisfaction, leisure satisfaction, satisfaction with income, and satisfaction with vacations all decreased during COVID-19, while anxiety and back pain increased during COVID-19. The role of the urban built environment in health and well-being was found to differ considerably for the COVID-19 period compared to pre-COVID-19. Proximity to large parks, proximity to numerous local facilities, lower neighborhood density, living further from the city center, and living in a larger dwelling were associated with better health and well-being outcomes during COVID-19. Urban planning and relevant policies that cities adopt should carefully focus on mitigating implications for critical issues such as the quest for sustainable urban development and city forms.

Exploring the changes in travel behavior in a developing country amidst the COVID-19 pandemic: Insights from Metro Cebu, Philippines

This article investigates changes in travel behavior from selected urban cities in Metro Cebu, Philippines during the COVID-19 pandemic a year after the first lockdown. Different categories of community quarantine and granular lockdowns have since been imposed to curb the spread of the virus. An online survey was distributed to analyze socio-demographic characteristics and reasons for traveling in relation to weekly trip frequency before and during pandemic. These are presented and analyzed through data visualization and multinomial logistic regression. Results show that the major reason for traveling before pandemic was work-related but has since shifted to buying essentials or for leisure or recreation. Weekly trip frequencies were lesser when compared before pandemic, but several socio-demographic groups have shown otherwise. There is statistical significance for those less likely to travel when commuters are employed, self-employed or students compared to unemployed, earning PHP 10,000 or less compared to those earning above PHP 50,000, in a household size of 10 compared to all other household sizes, and those with college degree against elementary or no formal education. By determining the travel behavior of commuters when they have ample time to adjust to the new normal, their mobility needs can be best understood and consequently satisfied. Interventions in fulfilling the travel needs for those belonging to socio-demographic groups that are highly affected by the pandemic, such as the working class, blue-collar workers, and have limited financial capabilities, can also be developed when a similar outbreak in the future is imminent.

COVID-19 and the aviation industry: The interrelationship between the spread of the COVID-19 pandemic and the frequency of flights on the EU market

This study aims to investigate the contribution of aviation related travel restrictions to control the spread of COVID-19 in Europe by using quasi-experiment approaches including the regression discontinuity design and a two-stage spatial Durbin model with an instrumental variable. The study provides concrete evidence that the severe curtailing of flights had a spontaneous impact in controlling the spread of COVID-19. The counterfactual analysis encapsulated the spillover effects deduced that a 1% decrease in flight frequency can decrease the number of confirmed cases by 0.908%. The study also reveals that during the lockdown, the aviation industry cancelled over 795,000 flights, which resulted in averting an additional six million people being from being infected and saving 101,309 lives.

Analysis of efficacy of intervention strategies for COVID-19 transmission: A case study of Hong Kong

By the end of February 2021, COVID-19 had spread to over 230 countries, with more than 100 million confirmed cases and 2.5 million deaths. To control infection spread with the least disruption to economic and societal activities, it is crucial to implement the various interventions effectively. In this study, we developed an agent-based SEIR model, using real demographic and geographic data from Hong Kong, to analyse the efficiency of various intervention strategies in preventing infection by the SARS-CoV-2 virus. Close contact route including short-range airborne is considered as the main transmission routes for COVID-19 spread. Contact tracing is not that useful if all other interventions have been fully deployed. The number of infected individuals could be halved if people reduced their close contact rate by 25%. For reducing transmission, students should be prioritized for vaccination rather than retired older people and preschool aged children. Home isolation, and taking the nucleic acid test (NAT) as soon as possible after symptom onset, are much more effective interventions than wearing masks in public places. Temperature screening in public places only disrupted the infection spread by a small amount when other interventions have been fully implemented. Our results may be useful for other highly populated cities, when choosing their intervention strategies to prevent outbreaks of COVID-19 and similar diseases.

How did human dwelling and working intensity change over different stages of COVID-19 in Beijing?

The COVID-19 pandemic has changed human daily activities significantly. Understanding the nature, causes, and extent of these changes is essential to evaluate the pandemic's influence on commerce, transportation, employment, and environment, among others. However, existing studies mainly focus on changes to general human mobility patterns; few have investigated changes in specific human daily activities. Based on one-year longitudinal mobile phone positioning data for more than 31 million users in Beijing, we tracked intensity changes in two basic human daily activities, dwelling and working, over the stages of COVID-19. The results show that during COVID-19 outbreak, human working intensity decreased about 60% citywide, while dwelling intensity decreased about 40% in some work and education areas. After COVID-19 was under control, intensity in most regions has recovered, but that in schools, hotels, entertainment venues, and tourism areas has not. These intensity changes at regional scale are due to behavior changes at individual scale: about 43% of residents left Beijing before COVID-19, while only 16% have returned back; all commuters decreased their commuting times during COVID-19, while only 75% have reverted to normal. The findings reveal variations in human activities caused by COVID-19 that can support targeted urban management in the post-epidemic era.