Intermunicipal travel networks of Mexico during the COVID-19 pandemic.

Human mobility networks are widely used for diverse studies in geography, sociology, and economics. In these networks, nodes usually represent places or regions and links refer to movement between them. They become essential when studying the spread of a virus, the planning of transit, or society's local and global structures. Therefore, the construction and analysis of human mobility networks are crucial for a vast number of real-life applications. This work presents a collection of networks that describe the human travel patterns between municipalities in Mexico in the 2020-2021 period. Using anonymized mobile location data, we constructed directed, weighted networks representing the volume of travels between municipalities. We analysed changes in global, local, and mesoscale network features. We observe that changes in these features are associated with factors such as COVID-19 restrictions and population size. In general, the implementation of restrictions at the start of the COVID-19 pandemic in early 2020, induced more intense changes in network features than later events, which had a less notable impact in network features. These networks will result very useful for researchers and decision-makers in the areas of transportation, infrastructure planning, epidemic control and network science at large.

The Omicron Lineages BA.1 and BA.2 (Betacoronavirus SARS-CoV-2) Have Repeatedly Entered Brazil through a Single Dispersal Hub.

Brazil currently ranks second in absolute deaths by COVID-19, even though most of its population has completed the vaccination protocol. With the introduction of Omicron in late 2021, the number of COVID-19 cases soared once again in the country. We investigated in this work how lineages BA.1 and BA.2 entered and spread in the country by sequencing 2173 new SARS-CoV-2 genomes collected between October 2021 and April 2022 and analyzing them in addition to more than 18,000 publicly available sequences with phylodynamic methods. We registered that Omicron was present in Brazil as early as 16 November 2021 and by January 2022 was already more than 99% of samples. More importantly, we detected that Omicron has been mostly imported through the state of São Paulo, which in turn dispersed the lineages to other states and regions of Brazil. This knowledge can be used to implement more efficient non-pharmaceutical interventions against the introduction of new SARS-CoV variants focused on surveillance of airports and ground transportation.

Unveiling the paths of COVID-19 in a large city based on public transportation data.

Human mobility plays a key role in the dissemination of infectious diseases around the world. However, the complexity introduced by commuting patterns in the daily life of cities makes such a role unclear, especially at the intracity scale. Here, we propose a multiplex network fed with 9 months of mobility data with more than 107 million public bus validations in order to understand the relation between urban mobility and the spreading of COVID-19 within a large city, namely, Fortaleza in the northeast of Brazil. Our results suggest that the shortest bus rides in Fortaleza, measured in the number of daily rides among all neighborhoods, decreased [Formula: see text]% more than the longest ones after an epidemic wave. Such a result is the opposite of what has been observed at the intercity scale. We also find that mobility changes among the neighborhoods are synchronous and geographically homogeneous. Furthermore, we find that the most central neighborhoods in mobility are the first targets for infectious disease outbreaks, which is quantified here in terms of the positive linear relation between the disease arrival time and the average of the closeness centrality ranking. These central neighborhoods are also the top neighborhoods in the number of reported cases at the end of an epidemic wave as indicated by the exponential decay behavior of the disease arrival time in relation to the number of accumulated reported cases with decay constant [Formula: see text] days. We believe that these results can help in the development of new strategies to impose restriction measures in the cities guiding decision-makers with smart actions in public health policies, as well as supporting future research on urban mobility and epidemiology.

COVID-19 vaccine acceptance and adherence to non-pharmaceutical interventions among employees of public transportations company in Addis Ababa, Ethiopia.

This study examined the acceptability of the COVID-19 vaccines and measured adherence to non-pharmaceutical interventions among employees in public transportations in Addis Ababa, Ethiopia. In a public transportation company-based cross-sectional study, a self-administered questionnaire or a structured face-to-face interview was used to obtain information about willingness to get vaccinated, adherence to recommended non-pharmaceutical interventions, and source and quality of information about COVID-19 vaccines. Overall, 23.8% of 412 responding employees were willing to receive a COVID-19 vaccine. A majority (75.2%) never used facemasks, had poor knowledge about COVID-19 vaccines (82.3%), and had the opinion not to be susceptible to COVID-19 (81.1%). Better education increased the odds of being willing to get vaccinated (OR = 3.28, CI: (1.24-8.63)), male sex (OR = 2.45 (1.08-5.58)), history of chronic disease (OR = 3.01 (1.38-6.56)), watching TV for information on COVID-19 (OR = 14.79 (2.53-86.62)) or considering COVID-19, a severe disease (OR = 9.12 (3.89-21.35)). In addition, the opinion that vaccination can prevent COVID-19, trust in the vaccines, and assuming COVID-19 has an impact at the workplace increased the odds to accept vaccination significantly. In contrast, poor knowledge about the COVID-19 vaccines reduced accepting vaccination significantly (OR = 0.20 (0.09-0.44)). Acceptance of COVID-19 vaccines among public transportations workers in Addis Ababa is very low, which may be due to insufficient knowledge about the vaccines, cultural factors, religious beliefs, and a lack of or distorted information about the disease. Therefore, stakeholders should provide credible and tailored information to transportation workers on the severity and impact of COVID-19 and inform them about the effectiveness of the vaccines.

The Impact of Urban Rail Transit Epidemic Prevention Measures on Passengers' Safety Perception.

In 2020, COVID-19 triggered concern about the safety of public transport. To meet passengers' expectations regarding safety, the public transport department has stepped up its pandemic prevention services. Some prevention services require passengers to follow mandatory requirements. However, whether and to what extent these requirements affect passenger satisfaction with public transportation services remains unclear. This study aims to construct an integrated framework to explore the direct and indirect relationships between four constructs (regular services quality, pandemic prevention service, psychological distance, and safety perception) and passengers' satisfaction in the context of urban rail transit services. Based on survey data collected from 500 passengers on the Shanghai Metro, this paper examines the relationships between routine service, pandemic prevention measures, safety perceptions, and satisfaction with the service. The results from the structural equation model indicate that routine service (0.608), pandemic prevention measures (0.56), and safety perception (0.05) have positive effects on passenger satisfaction. Psychological distance negatively impacts safety perception (-0.949) and has indirect effects on passenger satisfaction. Further, in order to identify the service improvements that public transportation departments should focus on, we use the three-factor theory to identify the services that should be improved: Basic factors, such as "punctual arrival of metros", "treatment of harmful garbage", "increasing frequency of platform disinfection", and "measurement of station temperature" should be treated as the first priority. As the second improvement priority, "the planning of metro stations can accommodate my travel scope" can be considered. Last, public transportation departments can enhance the exciting factor by installing "metro entrance signs" when resources are available.

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.

A Cohort Study of Occupational Fatalities among Paramedicine Clinicians: 2003 through 2020.

INTRODUCTION/STUDY OBJECTIVES: Emergency medical technicians (EMTs) and paramedics respond to 40 million calls for assistance every year in the United States; these paramedicine clinicians are a critical component of the nation's health care, disaster response, public safety, and public health systems. The study objective is to identify the risks of occupational fatalities among paramedicine clinicians working in the United States. METHODS: To determine fatality rates and relative risks, this cohort study focused on 2003 through 2020 data of individuals classified as EMTs and paramedics by the United States Department of Labor (DOL). Data provided by the DOL and accessed through its website were used for the analyses. The DOL classifies EMTs and paramedics who have the job title of fire fighter as fire fighters and so they were not included in this analysis. It is unknown how many paramedicine clinicians employed by hospitals, police departments, or other agencies are classified as health workers, police officers, or other and were not included in this analysis. RESULTS: An average of 206,000 paramedicine clinicians per year were employed in the United States during the study period; approximately one-third were women. Thirty percent (30%) were employed by local governments. Of the 204 total fatalities, 153 (75%) were transportation-related incidents. Over one-half of the 204 cases were classified as "multiple traumatic injuries and disorders." The fatality rate for men was three-times higher than for women (95% confidence interval [CI], 1.4 to 6.3). The fatality rate for paramedicine clinicians was eight-times higher than the rate for other health care practitioners (95% CI, 5.8 to 10.1) and 60% higher than the rate for all United States workers (95% CI, 1.24 to 2.04). CONCLUSIONS: Approximately 11 paramedicine clinicians are documented as dying every year. The highest risk is from transportation-related events. However, the methods used by the DOL for tracking occupational fatalities means that many cases among paramedicine clinicians are not included. A better data system, and paramedicine clinician-specific research, are needed to inform the development and implementation of evidence-based interventions to prevent occupational fatalities. Research, and the resulting evidence-based interventions, are needed to meet what should be the ultimate goal of zero occupational fatalities for paramedicine clinicians in the United States and internationally.

Urban-rural disparities in interfacility transfers for children during COVID-19.

PURPOSE: We aimed to identify temporal trends and differences in urban and rural pediatric interfacility transfers (IFTs) before and during the COVID-19 pandemic. METHODS: We conducted a cross-sectional analysis of IFT among children <18 years from January 2019 to June 2022 using the Pediatric Health Information System. The primary outcome was IFTs from general hospitals to referral children's hospitals. The primary exposure was patient rurality, defined by Rural-Urban Commuting Area codes. We categorized IFTs into medical, surgical, and mental health diagnoses and analyzed trends by month. We calculated observed-to-expected (O-E) ratios of pre-pandemic (March 2019-Feb 2020) transfers compared to pandemic year 1 (March 2020-Feb 2021) and year 2 (March 2021-February 2022) using Poisson modeling. FINDINGS: Of 419,250 IFTs, 18.8% (n = 78,751) were experienced by rural-residing children. The O-E ratio of IFT in year 1 for urban children was 14.0% (95% confidence interval [CI] 13.8, 14.2) and 14.8% (95% CI 14.4, 15.3) for rural children compared to pre-pandemic (P = .0001). In year 2, transfers rebounded with IFTs for rural-residing children increasing more than urban-residing children (101.7% [95% CI 100.1, 103.4] compared to 90.7% [95% CI 89.0, 90.4], P < .0001). For mental-health indications in year 2, rural transfer ratios were higher than urban, 126.8% (95% CI, 116.7, 137.6) compared to 113.7% (95% CI 109.9, 117.6), P = .0168. CONCLUSIONS: Pediatric IFTs decreased dramatically during pandemic year 1. In year 2, while medical and surgical transfers continued to lag pre-pandemic volumes, transfers for mental health indications significantly exceeded pre-pandemic levels, particularly among rural-residing children.

Methodology for designing intrahospital transportation of patients with suspected infectious disease that limits infection spread risk in China.

Aims: The transport of patients suspected of having COVID-19 requires careful consideration. Using paths selected at random and not accounting for person flow along the path are risk factors for infection spread. Intrahospital transportation (IHT) protocols and guidelines should be used to help reduce the risk of secondary virus transmission during transport. This study aimed to propose optimal IHT for patients with an infectious disease presenting in an out-patient area. Design: The map of a West China Hospital was used. We also used field investigation findings and simulated person flow to establish pathway length and transportation time. We identified three optimum pathways and estimated safety boundary marks, including a patient transportation border (PTB) and safety transportation border (STB). Finally, IHT, PTB, and STP formed a virtual transport pipeline (VTP) and a traceable IHT management system, which can generate a virtual isolation space. Results: The three pathways met efficiency, accessibility, and by-stander flow criteria. No facility characteristic modification was required. Conclusions: Using virtual models to identify pathways through out-patient hospital areas may help reduce the risk of infection spread.

Factors associated with delaying medical care: cross-sectional study of Nebraska adults.

BACKGROUND: Delayed medical care may result in adverse health outcomes and increased cost. Our purpose was to identify factors associated with delayed medical care in a primarily rural state. METHODS: Using a stratified random sample of 5,300 Nebraska households, we conducted a cross-sectional mailed survey with online response option (27 October 2020 to 8 March 2021) in English and Spanish. Multiple logistic regression models calculated adjusted odds ratios (aOR) and 95% confidence intervals. RESULTS: The overall response rate was 20.8% (n = 1,101). Approximately 37.8% of Nebraskans ever delayed healthcare (cost-related 29.7%, transportation-related 3.7%), with 22.7% delaying care in the past year (10.1% cost-related). Cost-related ever delay was associated with younger age [< 45 years aOR 6.17 (3.24-11.76); 45-64 years aOR 2.36 (1.29-4.32)], low- and middle-income [< $50,000 aOR 2.85 (1.32-6.11); $50,000-$74,999 aOR 3.06 (1.50-6.23)], and no health insurance [aOR 3.56 (1.21-10.49)]. Transportation delays were associated with being non-White [aOR 8.07 (1.54-42.20)], no bachelor's degree [≤ high school aOR 3.06 (1.02-9.18); some college aOR 4.16 (1.32-13.12)], and income < $50,000 [aOR 8.44 (2.18-32.63)]. Those who did not have a primary care provider were 80% less likely to have transportation delays [aOR 0.20 (0.05-0.80)]. CONCLUSIONS: Delayed care affects more than one-third of Nebraskans, primarily due to financial concerns, and impacting low- and middle-income families. Transportation-related delays are associated with more indicators of low socio-economic status. Policies targeting minorities and those with low- and middle-income, such as Medicaid expansion, would contribute to addressing disparities resulting from delayed care.

Decline in older adults' daily mobility during the COVID-19 pandemic: the role of individual and built environment factors.

BACKGROUND: Extensive research has shown that the COVID-19 pandemic dramatically impacted the daily mobility of older adults. However, very little attention has been paid to the role of individual and built environmental factors in decline in older adults' daily mobility during the pandemic. METHODS: Based on a cohort survey of 741 older adults in Hong Kong, we conducted a one-way ANOVA to explore the differences in determinants (individual or environmental factors) of older adults' daily mobility between before and during the COVID-19 pandemic. Further, multilevel linear regression was performed to examine how individual characteristics and built environment factors are associated with changes in older adults' daily mobility during the pandemic. RESULTS: Results show that the duration of active travel declined from 174.72 to 76.92 min per week, and that the public transport use frequency decreased from an average of 6.14 to 3.96 trips per week during the COVID-19 pandemic (before the rollout of vaccination programme). We also found residential density (p < 0.05) and the number of bus stop was negatively associated with the decline in their active travel (p < 0.01), while a higher destination mix was associated with more significant decrease in active travel (p < 0.01). A higher availability of recreational facilities in neighbourhoods was associated with a greater decrease in public transport use (p < 0.05). In addition, those who were older or having depressive symptoms, which are considered a vulnerable group, were negatively associated with decrease in their mobility (p < 0.001). CONCLUSIONS: Maintaining mobility and social interactions are crucial for older adults' health during the COVID-19 pandemic. This study found that individual and environmental factors differentially affected older adults' active travel and public transport use during the pandemic. Our findings contribute to understanding the COVID-19 impact on daily mobility in older adults and support more effective active travel promotion policies in the post-pandemic future.

CEEMDAN-IPSO-LSTM: A Novel Model for Short-Term Passenger Flow Prediction in Urban Rail Transit Systems.

Urban rail transit (URT) is a key mode of public transport, which serves for greatest user demand. Short-term passenger flow prediction aims to improve management validity and avoid extravagance of public transport resources. In order to anticipate passenger flow for URT, managing nonlinearity, correlation, and periodicity of data series in a single model is difficult. This paper offers a short-term passenger flow prediction combination model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and long-short term memory neural network (LSTM) in order to more accurately anticipate the short-period passenger flow of URT. In the meantime, the hyperparameters of LSTM were calculated using the improved particle swarm optimization (IPSO). First, CEEMDAN-IPSO-LSTM model performed the CEEMDAN decomposition of passenger flow data and obtained uncoupled intrinsic mode functions and a residual sequence after removing noisy data. Second, we built a CEEMDAN-IPSO-LSTM passenger flow prediction model for each decomposed component and extracted prediction values. Third, the experimental results showed that compared with the single LSTM model, CEEMDAN-IPSO-LSTM model reduced by 40 persons/35 persons, 44 persons/35 persons, 37 persons/31 persons, and 46.89%/35.1% in SD, RMSE, MAE, and MAPE, and increase by 2.32%/3.63% and 2.19%/1.67% in R and R2, respectively. This model can reduce the risks of public health security due to excessive crowding of passengers (especially in the period of COVID-19), as well as reduce the negative impact on the environment through the optimization of traffic flows, and develop low-carbon transportation.

Logistics Workers Are a Key Factor for SARS-CoV-2 Spread in Brazilian Small Towns: Case-Control Study.

BACKGROUND: Data on how SARS-CoV-2 enters and spreads in a population are essential for guiding public policies. OBJECTIVE: This study seeks to understand the transmission dynamics of SARS-CoV-2 in small Brazilian towns during the early phase of the epidemic and to identify core groups that can serve as the initial source of infection as well as factors associated with a higher risk of COVID-19. METHODS: Two population-based seroprevalence studies, one household survey, and a case-control study were conducted in two small towns in southeastern Brazil between May and June 2020. In the population-based studies, 400 people were evaluated in each town; there were 40 homes in the household survey, and 95 cases and 393 controls in the case-control study. SARS-CoV-2 serology testing was performed on participants, and a questionnaire was applied. Prevalence, household secondary infection rate, and factors associated with infection were assessed. Odds ratios (ORs) were calculated by logistic regression. Logistics worker was defined as an individual with an occupation focused on the transportation of people or goods and whose job involves traveling outside the town of residence at least once a week. RESULTS: Higher seroprevalence of SARS-CoV-2 was observed in the town with a greater proportion of logistics workers. The secondary household infection rate was 49.1% (55/112), and it was observed that in most households (28/40, 70%) the index case was a logistics worker. The case-control study revealed that being a logistics worker (OR 18.0, 95% CI 8.4-38.7) or living with one (OR 6.9, 95% CI 3.3-14.5) increases the risk of infection. In addition, having close contact with a confirmed case (OR 13.4, 95% CI 6.6-27.3) and living with more than four people (OR 2.7, 95% CI 1.1-7.1) were also risk factors. CONCLUSIONS: Our study shows a strong association between logistics workers and the risk of SARS-CoV-2 infection and highlights the key role of these workers in the viral spread in small towns. These findings indicate the need to focus on this population to determine COVID-19 prevention and control strategies, including vaccination and sentinel genomic surveillance.

COVID-19 contagion across remote communities in tropical forests.

Understanding COVID-19 contagion among poor populations is hampered by a paucity of data, and especially so in remote rural communities with limited access to transportation, communication, and health services. We report on the first study on COVID-19 contagion across rural communities without road access. We conducted telephone surveys with over 400 riverine communities in the Peruvian Amazon in the early phase of the pandemic. During the first wave (April-June, 2020), COVID-19 spread from cities to most communities through public and private river transportation according to their remoteness. The initial spread was delayed by transportation restrictions but at the same time was driven in unintended ways by government social assistance. During the second wave (August, 2020), although people's self-protective behaviors (promoted through communication access) helped to suppress the contagion, people responded to transportation restrictions and social assistance in distinct ways, leading to greater contagion among Indigenous communities than mestizo communities. As such, the spatial contagion during the early phase of the pandemic in tropical forests was shaped by river transportation and social behaviors. These novel findings have important implications for research and policies on pandemics in rural areas.

Modelling the propagation of infectious disease via transportation networks.

The dynamics of human mobility have been known to play a critical role in the spread of infectious diseases like COVID-19. In this paper, we present a simple compact way to model the transmission of infectious disease through transportation networks using widely available aggregate mobility data in the form of a zone-level origin-destination (OD) travel flow matrix. A key feature of our model is that it not only captures the propagation of infection via direct connections between zones (first-order effects) as in most existing studies but also transmission effects that are due to subsequent interactions in the remainder of the system (higher-order effects). We demonstrate the importance of capturing higher-order effects in a simulation study. We then apply our model to study the first wave of COVID-19 infections in (i) Italy, and, (ii) the New York Tri-State area. We use daily data on mobility between Italian provinces (province-level OD data) and between Tri-State Area counties (county-level OD data), and daily reported caseloads at the same geographical levels. Our empirical results indicate substantial predictive power, particularly during the early stages of the outbreak. Our model forecasts at least 85% of the spatial variation in observed weekly COVID-19 cases. Most importantly, our model delivers crucial metrics to identify target areas for intervention.

Impact of Different Transportation Modes on the Transmission of COVID-19: Correlation and Strategies from a Case Study in Wuhan, China.

Transportation is the main carrier of population movement, so it is significant to clarify how different transportation modes influence epidemic transmission. This paper verified the relationship between different levels of facilities and epidemic transmission by use of the K-means clustering method and the Mann-Whitney U test. Next, quantile regression and negative binomial regression were adopted to evaluate the relationship between transportation modes and transmission patterns. Finally, this paper proposed a control efficiency indicator to assess the differentiated strategies. The results indicated that the epidemic appeared 2-3 days earlier in cities with strong hubs, and the diagnoses were nearly fourfold than in other cities. In addition, air and road transportation were strongly associated with transmission speed, while railway and road transportation were more correlated with severity. A prevention strategy that considered transportation facility levels resulted in a reduction of the diagnoses of about 6%, for the same cost. The results of different strategies may provide valuable insights for cities to develop more efficient control measures and an orderly restoration of public transportation during the steady phase of the epidemic.

Analysis of the Face Mask Use by Public Transport Passengers and Workers during the COVID-19 Pandemic.

(1) Background: The use of face masks and gloves in public places directly shows the commitment of the population to the established regulations. Public transport is one of the most-at-risk places of contamination. The aim of the study was to analyze the face mask use by public transport passengers and workers during the COVID-19 pandemic. (2) Methods: Public transport passengers and workers were surveyed. Periodic intermittent selective observation was used to gauge the level of adherence to the established regulations among public transport passengers. Factor analysis was used to identify factors determining the face-mask-wearing comfort. (3) Results: The majority of passengers (87.5%) and all transport workers (100%) used face masks and gloves. Most of the users wore only face masks. Only 41.6% of passengers and 74.7% of transport workers wore face masks correctly. Motivational attitudes at the implementation of preventive measures were determined: established regulations in the public place (55.8%) and the protection of one's own health and the health of family members (44.2%). Only 22.5% of those wearing face masks believed that doing so will have any effect on the spread of an infectious disease, and 10.8% wore masks to maintain the health of people around themselves. A low level of social responsibility was demonstrated. For 53.4% of workers, face mask wearing was uncomfortable. The majority of workers had adverse reactions to mask wearing: feeling short of breath (52.8%), hyperemia of face skin (33.8%), and facial hyperhidrosis (67.4%). (4) Conclusions: The comfort of wearing a mask is determined by adverse reactions occurrence, the properties of the mask, working conditions, and the duration of wearing the face mask. It is necessary to develop recommendations to reduce wearing discomfort. These recommendations, along with methods of raising the social responsibility of the population, can contribute to a greater commitment of the population to non-specific prevention measures.

The Canadian Bikeway Comfort and Safety metrics (Can-BICS): National measures of the bicycling environment for use in research and policy.

Background: The lack of consistent measures of the cycling environment across communities hampers cycling research and policy action. Our goal was to develop the first national dataset in Canada for metrics of the cycling environment at the dissemination area (DA) level - the Canadian Bikeway Comfort and Safety (Can-BICS) metrics. Data and methods: The Can-BICS metrics are area-level metrics based on the quantity of cycling infrastructure within a 1 km buffer of the population-weighted centroid of DAs. The base data are a national cycling network dataset derived from OpenStreetMap (OSM) (extracted January 25, 2022) and classified by high-, medium- and low-comfort facilities. A Can-BICS continuous metric (sum of cycling infrastructure per square kilometre weighted by comfort class) and Can-BICS categorical metric were derived and mapped for all 56,589 DAs in Canada. The Can-BICS metrics were correlated with other national datasets (2016 Canadian Active Living Environments [Can-ALE] and 2016 Census journey-to-work data) to test for associations between Can-BICS and related measures. Additionally, city staff were engaged to provide feedback on metrics during the development phase. Results: One-third (34%) of neighbourhoods in Canada have no cycling infrastructure. According to the categorical measure, 5% of all DAs were assigned as the highest category of Can-BICS (corresponding to 6% of the population) and were nearly all within metro areas. The Can-BICS continuous metric had low correlation with bike-to-work rates (R = 0.29) and was more strongly correlated with sustainable-transportation-to-work rates (R = 0.56) and the Can-ALE metrics (R=0.62). These correlations were variable across cities. Interpretation: The Can-BICS metrics provide national research- and practice-ready measures of cycling infrastructure. The metrics complement existing measures of walking and transit environments (Can-ALE), collectively providing a cohesive set of active living measures. The datasets and code are publicly available, facilitating updates as new infrastructure is built.

A long-term travel delay measurement study based on multi-modal human mobility data.

Understanding human mobility is of great significance for sustainable transportation planning. Long-term travel delay change is a key metric to measure human mobility evolution in cities. However, it is challenging to quantify the long-term travel delay because it happens in different modalities, e.g., subway, taxi, bus, and personal cars, with implicated coupling. More importantly, the data for long-term multi-modal delay modeling is challenging to obtain in practice. As a result, the existing travel delay measurements mainly focus on either single-modal system or short-term mobility patterns, which cannot reveal the long-term travel dynamics and the impact among multi-modal systems. In this paper, we perform a travel delay measurement study to quantify and understand long-term multi-modal travel delay. Our measurement study utilizes a 5-year dataset of 8 million residents from 2013 to 2017 including a subway system with 3 million daily passengers, a 15 thousand taxi system, a 10 thousand personal car system, and a 13 thousand bus system in the Chinese city Shenzhen. We share new observations as follows: (1) the aboveground system has a higher delay increase overall than that of the underground system but the increase of it is slow down; (2) the underground system infrastructure upgrades decreases the aboveground system travel delay increase in contrast to the increase the underground system travel delay caused by the aboveground system infrastructure upgrades; (3) the travel delays of the underground system decreases in the higher population region and during the peak hours.

Increased Rate of Fracture Injuries Associated With Alternative Modes of Transportation During COVID-19.

OBJECTIVE: COVID-19 disrupted public transit and led to increased reliance on alternative modes of transportation (AMTs) internationally. This study hypothesizes that public interest and fracture injuries associated with AMTs increased during COVID-19 in the United States. METHODS: Monthly Google search probabilities and the number of fracture injuries associated with bicycles, scooters, skateboards/longboards, rollerblades, electric bicycles, and electric micromobility vehicles were collected from January 2017 to December 2021. Wilcoxon signed-rank tests were used to assess differences in search probabilities and fracture injuries between 2021, 2020, and 2019. Linear regression was used to study the relationship between search probabilities and number of fracture injuries. RESULTS: For bicycles, skateboards/longboards, electric bicycles, and electric micromobility vehicles, search probabilities and fracture injuries were higher in 2021 and 2020 compared with 2019, except for bicycle fractures in 2021 (P < 0.05). For every AMT, except roller skates, search probability had an explanatory effect on fracture injuries (P < 0.001). CONCLUSION: Online interest in AMTs and associated fracture injuries increased during the COVID-19 pandemic. Excess fractures seem to be stabilizing as of December 2021, but online search volumes may be used to inform the allocation of orthopaedic trauma resources during future surges in COVID-19 and other epidemics.