The role of high-speed rail and air travel in the spread of COVID-19 in China.

BACKGROUND: Public transportation is a major facilitator of the spread of infectious diseases and has been a focus of policy interventions aiming to suppress the current COVID-19 epidemic. METHODS: We use a random-effects panel data model and a Difference-in-Differences in Reverse (DDR) model to examine how air and rail transport links with Wuhan as well as the suspension of these transport links influenced the development of the epidemic in China. RESULTS: We find high-speed rail (HSR) and air connectivity with Wuhan resulted in 25.4% and 21.2% increases in the average number of daily new confirmed cases, respectively, while their suspension led to 18.6% and 13.3% decreases in that number. We also find that the suspension effect was dynamic, growing stronger over time and peaking 20-23 days after the Wuhan lockdown, then gradually wearing off. It took approximately four weeks for this effect to fully materialize, roughly twice the maximum incubation period, and similar dynamic patterns were seen in both HSR and air models. Overall, HSR had a greater impact on COVID-19 development than air transport. CONCLUSIONS: Our research provides important evidence for implementing transportation-related policies in controlling future infectious diseases.

Neighborhood-level disparities and subway utilization during the COVID-19 pandemic in New York City.

The COVID-19 pandemic has yielded disproportionate impacts on communities of color in New York City (NYC). Researchers have noted that social disadvantage may result in limited capacity to socially distance, and consequent disparities. We investigate the association between neighborhood social disadvantage and the ability to socially distance, infections, and mortality in Spring 2020. We combine Census Bureau and NYC open data with SARS-CoV-2 testing data using supervised dimensionality-reduction with Bayesian Weighted Quantile Sums regression. The result is a ZIP code-level index with weighted social factors associated with infection risk. We find a positive association between neighborhood social disadvantage and infections, adjusting for the number of tests administered. Neighborhood disadvantage is also associated with a proxy of the capacity to socially isolate, NYC subway usage data. Finally, our index is associated with COVID-19-related mortality.

A GIS approach to the development of a segment-level derailment prediction model.

Train related accidents, particularly derailments, can lead to severe consequences especially when they involve injuries or fatalities or when they involve hazardous materials that might result in environmental impacts. Whereas numerous road safety studies have suggested appropriate approaches to predicting vehicle-to-vehicle collisions, very few railway safety studies have considered predicting the number of derailments on rail tracks in North America. In addition, the existing few rail safety assessment and derailment prediction models have often been constrained by aggregated data limiting the safety assessments by, for example, failing to consider segment-level characteristics. This paper focused on the development of an integrated database for the development of a segment-level derailment prediction model for Canada's rail network. The primary objective of this paper is to report how challenges in the data integration process were overcome and also to develop a network screening tool to identify segments with high derailment risk in Canada's rail network. Negative binomial regression and the Empirical Bayes technique were used to estimate the predicted number of derailments on Canada's rail network at the segment level. A network screening process was then successfully applied to identify key segments of safety concern: the top ten segments of concern accounted for approximately 1% of the rail network allowing decision makers to focus their derailment mitigation efforts on a manageable part of Canada's vast rail network. The data processing approach and analysis in this study have strong implications for advancing research on rail safety in North America.

Socioeconomic Disparities in Subway Use and COVID-19 Outcomes in New York City.

Using data from New York City from January 2020 to April 2020, we found an estimated 28-day lag between the onset of reduced subway use and the end of the exponential growth period of severe acute respiratory syndrome coronavirus 2 within New York City boroughs. We also conducted a cross-sectional analysis of the associations between human mobility (i.e., subway ridership) on the week of April 11, 2020, sociodemographic factors, and coronavirus disease 2019 (COVID-19) incidence as of April 26, 2020. Areas with lower median income, a greater percentage of individuals who identify as non-White and/or Hispanic/Latino, a greater percentage of essential workers, and a greater percentage of health-care essential workers had more mobility during the pandemic. When adjusted for the percentage of essential workers, these associations did not remain, suggesting essential work drives human movement in these areas. Increased mobility and all sociodemographic variables (except percentage of people older than 75 years old and percentage of health-care essential workers) were associated with a higher rate of COVID-19 cases per 100,000 people, when adjusted for testing effort. Our study demonstrates that the most socially disadvantaged not only are at an increased risk for COVID-19 infection, they lack the privilege to fully engage in social distancing interventions.

StationRank: Aggregate dynamics of the Swiss railway.

Increasing availability and quality of actual, as opposed to scheduled, open transport data offers new possibilities for capturing the spatiotemporal dynamics of railway and other networks of social infrastructure. One way to describe such complex phenomena is in terms of stochastic processes. At its core, a stochastic model is domain-agnostic and algorithms discussed here have been successfully used in other applications, including Google's PageRank citation ranking. Our key assumption is that train routes constitute meaningful sequences analogous to sentences of literary text. A corpus of routes is thus susceptible to the same analytic tool-set as a corpus of sentences. With our experiment in Switzerland, we introduce a method for building Markov Chains from aggregated daily streams of railway traffic data. The stationary distributions under normal and perturbed conditions are used to define systemic risk measures with non-evident, valuable information about railway infrastructure.

A Hybrid PSO-SVM Model Based on Safety Risk Prediction for the Design Process in Metro Station Construction.

Incorporating safety risk into the design process is one of the most effective design sciences to enhance the safety of metro station construction. In such a case, the concept of Design for Safety (DFS) has attracted much attention. However, most of the current research overlooks the risk-prediction process in the application of DFS. Therefore, this paper proposes a hybrid risk-prediction framework to enhance the effectiveness of DFS in practice. Firstly, 12 influencing factors related to the safety risk of metro construction are identified by adopting the literature review method and code of construction safety management analysis. Then, a structured interview is used to collect safety risk cases of metro construction projects. Next, a developed support vector machine (SVM) model based on particle swarm optimization (PSO) is presented to predict the safety risk in metro construction, in which the multi-class SVM prediction model with an improved binary tree is designed. The results show that the average accuracy of the test sets is 85.26%, and the PSO-SVM model has a high predictive accuracy for non-linear relationship and small samples. The results show that the average accuracy of the test sets is 85.26%, and the PSO-SVM model has a high predictive accuracy for non-linear relationship and small samples. Finally, the proposed framework is applied to a case study of metro station construction. The prediction results show the PSO-SVM model is applicable and reasonable for safety risk prediction. This research also identifies the most important influencing factors to reduce the safety risk of metro station construction, which provides a guideline for the safety risk prediction of metro construction for design process.

Analysis Factors That Influence Escalator-Related Injuries in Metro Stations Based on Bayesian Networks: A Case Study in China.

Escalator-related injuries have become an important issue in daily metro operation. To reduce the probability and severity of escalator-related injuries, this study conducted a probability and severity analysis of escalator-related injuries by using a Bayesian network to identify the risk factors that affect the escalator safety in metro stations. The Bayesian network structure was constructed based on expert knowledge and Dempster-Shafer evidence theory, and further modified based on conditional-independence test. Then, 950 escalator-related injuries were used to estimate the posterior probabilities of the Bayesian network with expectation-maximization (EM) algorithm. The results of probability analysis indicate that the most influential factor in four passenger behaviors is failing to stand firm (p = 0.48), followed by carrying out other tasks (p = 0.32), not holding the handrail (p = 0.23), and another passenger's movement (p = 0.20). Women (p = 0.64) and elderly people (aged 66 years and above, p = 0.48) are more likely to be involved in escalator-related injuries. Riding an escalator with company (p = 0.63) has a relatively high likelihood of resulting in escalator-related injuries. The results from the severity analysis show that head and neck injuries seem to be more serious and are more likely to require an ambulance for treatment. Passengers who suffer from entrapment injury tend to claim for compensation. Severe injuries, as expected, significantly increase the probability of a claim for compensation. These findings could provide valuable references for metro operation corporations to understand the characteristics of escalator-related injuries and develop effective injury prevention measures.

Impact of comorbidity on health outcome after a transport-related injury.

INTRODUCTION: Understanding the impact of comorbidity on health outcomes is important given that comorbidities can affect survival, morbidity, service delivery costs and healthcare utilisation. However, little is known about the types of comorbidities affecting specific health outcomes after minor to moderate road trauma. METHODS: This study involved 1574 participants who claimed injury compensation following transport-related injury. Cross sectional data were collected. Health outcomes were assessed using the EQ-5D-3L specific domains and summary score. Twelve self-reported pre-existing chronic conditions were assessed using a multivariate logistic regression, adjusting for demographic and injury characteristics. RESULTS: Out of 1574 participants, only 17 (1%) participants reported no pre-existing comorbidities, 72% reported one, 13% reported two and 14% reported three or more comorbidities. Hypertension (15%), depression (14%) and anxiety (14%) were the most commonly reported comorbidities, followed by arthritis (13%), chronic pain (11%) and asthma (11%). Participants with a history of arthritis (adjusted odds ratio [AOR] 1.90, 95% CI 1.24 to 2.91); chronic back pain (AOR 1.59, 95% CI, 1.04 to 2.43); other chronic pain (AOR 2.73, 95% CI 1.42 to 4.24); depression (AOR 2.55, 95% CI 1.60 to 4.05) and anxiety (AOR 2.08, 95% CI 1.32 to 3.26) were at increased risk of poorer health outcomes, after controlling for age, gender, type of injury and time since injury. CONCLUSION: This study found that comorbidities such as arthritis, chronic back pain, other chronic pain, depression and anxiety significantly increase the odds of poorer health postinjury, regardless of the time since injury. Regular screening of comorbid conditions may help identify people likely to have poorer outcomes, thereby enabling the implementation of interventions to optimise health despite the presence of comorbidities.

How do organizational practices relate to perceived system safety effectiveness? Perceptions of safety climate and co-worker commitment to safety as workplace safety signals.

INTRODUCTION: Integrating safety climate research with signaling theory, we propose that individual perceptions of safety climate signal the importance of safety in the organization. Specifically, we expect that three work-related organizational practices (training effectiveness, procedure effectiveness, and work pressure) relate to the broader risk control system in the workplace via individual perceptions of safety climate as a broad management signal. Further, we expect this broad management signal interacts with a local environmental signal (co-worker commitment to safety) to amplify or diminish perceived system safety effectiveness. METHOD: In a field study of oil and gas workers (N = 219; Study 1), we used mediation modeling to determine the relationships between work-related organizational practices, perceived safety climate, and perceived safety system effectiveness. In a field study of railway construction workers (N = 131; Study 2), we used moderated mediation modeling to explore the conditional role of co-worker commitment to safety. RESULTS: We found that training effectiveness, procedure effectiveness, and work pressure predicted perceived system safety effectiveness indirectly via perceived safety climate (Studies 1 and 2) and that these indirect paths are influenced by co-worker commitment to safety (Study 2). CONCLUSIONS: Findings suggest that perceived safety climate is driven in part by work practices, and that perceived safety climate (from managers) and co-worker commitment to safety (from the local environment) interact to shape workplace safety system effectiveness. Practical applications: The insight that training, procedures, and work pressure are meaningful predictors of perceived safety climate as a signal suggests that organizations should be cognizant of the quality of work-related practices for safety. The insight we offer on the competing versus complimentary nature of managerial safety signals (perceived safety climate) and co-worker safety signals (co-worker commitment to safety) could also be used by safety personnel to develop safety interventions directed in both areas.

Self-Reported Sleep Disturbance from Road, Rail and Aircraft Noise: Exposure-Response Relationships and Effect Modifiers in the SiRENE Study.

This survey investigates the cross-sectional association between nighttime road, rail and aircraft noise exposure and the probability to be highly sleep disturbed (%HSD), as measured by self-report in postal and online questionnaires. As part of the Swiss SiRENE study, a total of 5592 survey participants in the entire country were selected based on a stratified random sample of their dwelling. Self-reported sleep disturbance was measured using an ICBEN-style 5-point verbal scale. The survey was carried out in four waves at different times of the year. Source-specific noise exposure was calculated for several façade points for each dwelling. After adjustment for potential confounders, all three noise sources showed a statistically significant association between the nighttime noise level LNight at the most exposed façade point and the probability to report high sleep disturbance, as determined by logistic regression. The association was strongest for aircraft noise and weakest for road traffic noise. We a priori studied the role of a range of effect modifiers, including the "eventfulness" of noise exposure, expressed as the Intermittency Ratio (IR) metric, bedroom window position, bedroom orientation towards the closest street, access to a quiet side of the dwelling, degree of urbanization, sleep timing factors (bedtime and sleep duration), sleep medication intake, survey season and night air temperature. While bedroom orientation exhibited a strong moderating effect, with an Leq-equivalent of nearly 20 dB if the bedroom faces away from the nearest street, the LNight-%HSD associations were not affected by bedroom window position, sleep timing factors, survey season, or temperature.

Empirical mode decomposition based long short-term memory neural network forecasting model for the short-term metro passenger flow.

Short-term metro passenger flow forecasting is an essential component of intelligent transportation systems (ITS) and can be applied to optimize the passenger flow organization of a station and offer data support for metro passenger flow early warning and system management. LSTM neural networks have recently achieved remarkable recent in the field of natural language processing (NLP) because they are well suited for learning from experience to predict time series. For this purpose, we propose an empirical mode decomposition (EMD)-based long short-term memory (LSTM) neural network model for predicting short-term metro inbound passenger flow. The EMD algorithm decomposes the original sequential passenger flow into several intrinsic mode functions (IMFs) and a residual. Selected IMFs that are strongly correlated with the original data can be obtained via feature selection. The selected IMFs and the original data are integrated into inputs for LSTM neural networks, and a single LSTM prediction model and an EMD-LSTM hybrid forecasting model are developed. Finally, historical real automatic fare collection (AFC) data from metro passengers are collected from Chengdu Metro to verify the validity of the proposed EMD-LSTM prediction model. The results indicate that the proposed EMD-LSTM hybrid forecasting model outperforms the LSTM, ARIMA and BPN models.

Using the Haddon matrix to explore medical response strategies for terrorist subway bombings.

BACKGROUND: Since the 1970s, terrorist bombings in subways have been frequently occurring worldwide. To cope with this threat and to provide medical response countermeasures, we analyzed the characteristics of subway bombing terrorist attacks and used the Haddon matrix to explore medical response strategies. METHODS: First, we analyzed 111 subway bombings from 1970 to 2017 recorded in the Global Terrorism Database to provide a reference for the strategy exploration. Then, we convened an expert panel to use the Haddon matrix to explore the medical response strategies to subway bombings. RESULTS: In recent decades, at least one bombing attack occurs every 3 years. Summarized by the Haddon matrix, the influencing factors of medical responses to conventional subway bombings include the adequacy of first-aid kits and the medical evacuation equipment, the traffic conditions affecting the evacuation, the continuity and stability of communication, as well as the factors exclusively attributed to dirty bomb attacks in subways, such as ionizing radiation protection capabilities, the structure of the radiation sickness treatment network based on the subway lines, and the disposal of radioactive sewage. These factors form the basis of the strategy discussion. CONCLUSION: Since subway bombings are long-term threats, it is necessary to have proper medical response preparation. Based on the Haddon matrix, we explored the medical response strategies for terrorist subway bombings, especially dirty bomb attacks. Haddon matrix can help policymakers systematically find the most important factors, which makes the preparations of the response more efficient.

Sleeping, sleeping environments, and human errors in South Korean male train drivers.

OBJECTIVES: Reducing human errors caused by daytime sleepiness among train drivers is important to prevent train accidents. Our purpose of the study was to investigate the association among sleep, workplace sleeping environments, and human errors. METHODS: We recruited 144 South Korean train drivers belongs to the Korean Railroad Corporation. This cross-sectional data was analyzed to investigate the association of insomnia (insomnia severity index), sleep quality (Pittsburgh sleep quality index), obstructive sleep apnea (Berlin questionnaire), and daytime sleepiness (Epworth scale) with human error and near-miss experiences. We examined whether human error and near-miss events were associated with various sleeping environments at work and at home after adjusting for the sleep indices. RESULTS: The experience of human errors was associated with insomnia and daytime sleepiness, and near-miss events were associated with insomnia among South Korean drivers. Sleeping environments including cold temperature and odor were related to both human errors and near-miss events among South Korean train drivers, after adjusted for age, working years, shiftwork, obesity, smoking, binge drinking, regular exercise, caffeine consumption, sleep quality, severity of insomnia, obstructive sleep apnea, and daytime sleepiness. CONCLUSIONS: The train drivers' workplace sleeping environment is significantly associated with human error events and near-miss events after adjusting for sleep quality, insomnia, obstructive sleep apnea, and daytime sleepiness. To prevent train accidents caused by human errors, more attention is necessary for improving workplace sleeping environments.

Traverses, delays and fatalities at railway level crossings in Great Britain.

This paper investigates relationships between traverses, delays and fatalities to road users at railway level crossings in Great Britain. A 'traverse' means a passage across a level crossing by a road user, who may be a pedestrian, cyclist, or occupant of a road vehicle. The paper finds that the road users with the highest fatality rate per traverse are pedestrians at passive crossings. Their rate is about three orders of magnitude higher than that of users with the lowest risk, who are road vehicle occupants at railway-controlled crossings. The paper considers the choice between automatic and railway-controlled crossings on public roads. Railway-controlled crossings are widely used in Britain. They are about one order of magnitude safer than automatic crossings, but they impose greater delays on users. A formula is developed to give the overall delay to road users at either type of crossing in terms of the numbers of road users and trains per day, and in terms of the length of time that the crossing must be closed to the road to allow the passage of one train. It is found that automatic level crossings cause substantially less delay than railway-controlled level crossings. The official monetary values of road user delay and of preventing a fatality were used to estimate the valuations of delays and fatalities at hypothetical but representative automatic and railway-controlled crossings. These valuations were then used to explore the effect of replacing representative railway-controlled with automatic crossings or vice-versa. It is found that the valuation of the reduced delays from adopting automatic crossings typically outweighs the valuation of the losses from the increased casualties. However, in practice Britain has chosen to retain a large number of railway-controlled crossings, which implies accepting the delays in return for a good level crossing safety record. Finally, an analysis is carried out to determine the additional risk of typical car and walk journeys that involve traversing a level crossing compared with similar journeys that do not. It is found that the additional risk is small for motor vehicle journeys, but substantial for walk journeys.

Feasibility and acceptability of using Wi-Fi access points to provide smoking cessation help at 17 airports and 38 railway stations across China.

BACKGROUND: The use of mobile phone significantly improved the outcomes of tobacco cessation. However, its feasibility and acceptability were unclear in the Chinese population. This study was to explore the feasibility of using Wi-Fi access points (APs) as a platform to provide smoking cessation help at 17 airports and 38 railway stations across China. METHODS: This study was divided into two stages: platform development and population survey. In the first stage, a survey platform was developed and incorporated into Wi-Fi service at airports and railway stations, which could provide survey content as a pop-up window when participants tried to access the Wi-Fi service. In the second stage, a population survey was conducted to explore the intention to receive tobacco cessation support. RESULTS: A total of 20,199 users participated and 13,628 users submitted the survey, with a response rate of 67.47%. The smoking rate was 30.9%. A total of 86.58% of smoking participants and 2.44% of non-smoking participants wished to receive tobacco cessation support, respectively. The multivariate analysis showed intention to receive support did not differ in age, gender, and heaviness of smoking (P > 0.05). CONCLUSION: Providing tobacco cessation support via Wi-Fi APs is feasible and efficient, and smokers have high intention to receive tobacco cessation support. It is suggested hospitals, academia, information technology industries, and government agencies must work together to provide tobacco cessation support via mHealth.

A Comprehensive Railroad-Highway Grade Crossing Consolidation Model: A Machine Learning Approach.

In the United States, there are approximately 212,000 highway-rail grade crossings, some of which experience vehicle-train incidents that often cause a massive financial burden, loss of life, and injury. In 2017, there were 2,108 highway-rail incidents resulting in 827 injuries and 307 fatalities nationwide. To eliminate collision risks, crossing grade separation and active alarm improvement are commonly used. Moreover, crossing closures are considered to be the most effective safety improvement program. While it may be very difficult, and in some cases impossible to close crossings, there are some incentive programs that facilitate the closure process. One of these programs is a working consolidation model that aims to determine crossing closure suitability. Using details of highway-rail grade crossings in the United States and applying an eXtreme Gradient Boosting (XGboost) algorithm, this paper proposes a data-driven consolidation model that takes into consideration a number of engineering variables. The results indicated an overall accuracy of 0.991 for the proposed model. In addition, the developed XGboost consolidation model reported the relative importance of the variables input to the model, offering an in-depth understanding of the model's behavior. Finally, for the practical implementation of the model, a simplified version containing fewer variables was developed. A sensitivity analysis was performed considering the aggregate gain and the different correlation threshold values between variables. This analysis developed a simplified model utilizing 14 variables, with aggregated gain values of 75% and a correlation threshold of 0.9 which would perform similarly to the full model. Based on this model, 62% of current highway-rail grade crossings should be closed.

The Health and economic effects of light rail lines: design, methods, and protocol for a natural experiment.

BACKGROUND: The health impacts of community design have been studied extensively over the past two decades. In particular, public transportation use is associated with more walking between transit stops and shops, work, home and other destinations. Change in transit access has been linked with physical activity and obesity but seldom to health outcomes and associated costs, especially within a causal framework. Health related fiscal impacts of transit investment should be a key consideration in major transit investment decisions. METHODS: The Rails & Health study is a natural experiment evaluating changes in clinical measures, health care utilization and health care costs among Kaiser Permanente Northwest (KPNW) members following the opening of a new light rail transit (LRT) line in Portland, Oregon. The study is prospectively following 3036 adults exposed to the new LRT line and a similar cohort of 4386 adults who do not live close to the new line. Individual-level outcomes and covariates are extracted from the electronic medical record at KPNW, including member demographics and comorbidities, blood pressure, body mass index, lipids, glycosylated hemoglobin, and health care utilization and costs. In addition, participants are surveyed about additional demographics, travel patterns, physical activity (PA), and perceived neighborhood walkability. In a subsample of the study population, we are collecting direct measures of travel-related behavior-physical activity (accelerometry), global positioning system (GPS) tracking, and travel diaries-to document mechanisms responsible for observed changes in health outcomes and cost. Comprehensive measures of the built environment at baseline and after rail construction are also collected. Statistical analyses will (1) examine the effects of opening a new LRT line on chronic disease indicators, health care utilization, and health care costs and (2) evaluate the degree to which observed effects of the LRT line on health measures and costs are mediated by changes in total and transportation-associated PA. DISCUSSION: The results of the Rails & Health study will provide urban planners, transportation engineers, health practitioners, developers, and decision makers with critical information needed to document how transit investments impact population health and related costs.

Radon and PM10 concentrations in underground parking lots and subway stations with health risks in South Korea.

This study examined the environmental factors that affect indoor radon (Rn) and particulate matter (PM10) concentrations in underground public facilities such as subway stations and underground parking lots. Rn and PM10 concentrations from March 2014 to October 2015 were evaluated at 40 subway stations and 19 underground parking lots. Average underground concentrations of Rn and PM10 were 37.3 ± 17.1 Bq/m3 and 78.6 ± 15.1 µg/m3, respectively. There were significant difference in Rn concentration between sampling points, with the highest concentration 41.8 ± 18.1 Bq/m3 on subway platforms, while the highest concentration of PM10 was 83.7 ± 13.8 µg/m3 in transfer pathways. Rn concentration showed positive correlation with PM10 concentration (r = 0.380, p < 0.001). The highest Rn concentration occurred during fall season, followed by summer and spring. At 60-h exposure duration in underground subway stations and parking lots, the hazard quotient (HQ) of Rn exceeded 1 for children less than 1 year old and those between 8 and 10 years old.

Seismic responses of rectangular subway tunnels in a clayey ground.

Observations from past earthquakes have shown that subway tunnels can suffer severe damage or excessive deformation due to seismic shaking. This study presents the results of finite element analyses on subway tunnels installed in normally consolidated clay deposits subjected to far-field ground motions. The clay strata were modelled by a hyperbolic-hysteretic constitutive model. The influences of three factors on the seismic response of the clay-tunnel systems were examined, namely ground motion intensity, tunnel wall thickness and clay stiffness. Furthermore, the computed racking deformations of the tunnel were compared with several analytical estimations from the literature, and the relationship between racking ratio and flexibility ratio for rectangular tunnels installed in normally consolidated clay deposits was proposed. The findings may provide a useful reference for practical seismic design of tunnels.