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.

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.

A Demand-Centric Repositioning Strategy for Bike-Sharing Systems.

Transport-sharing systems are eco-friendly and the most promising services in smart urban environments, where the booming Internet of things (IoT) technologies play an important role in the smart infrastructure. Due to the imbalanced bike distribution, bikes and stalls in the docking stations could be unavailable when needed, leading to bad customer experiences. We develop a dynamic repositioning strategy for the management of bikes in this paper, which supports dispatchers to keep stations in service. Two open datasets are examined, and the exploratory data analysis presents that there is a significant difference of travel patterns between working and non-working days, where the former has an excess demand at rush hours and the latter is usually at a low demand. To evaluate the effect when the demand outstrips a station's capacity, we propose a non-linear scaling technique to transform demand patterns and perform the clustering analysis for each of five categories obtained from the sophisticated analysis of the dataset. Our repositioning strategy is developed according to the transformed demands. Compared with the previous work, numerical simulations reveal that our strategy has a better performance for high-demand stations, and thus can substantially reduce the repositioning cost, which brings benefit to bike-sharing operators for managing the city bike system.

The TROLLEY Study: assessing travel, health, and equity impacts of a new light rail transit investment during the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic disrupted life in extraordinary ways impacting health and daily mobility. Public transit provides a strategy to improve individual and population health through increased active travel and reduced vehicle dependency, while ensuring equitable access to jobs, healthcare, education, and mitigating climate change. However, health safety concerns during the COVID-19 pandemic eroded ridership, which could have longstanding negative consequences. Research is needed to understand how mobility and health change as the pandemic recedes and how transit investments impact health and equity outcomes. METHODS: The TROLLEY (TRansit Opportunities for HeaLth, Livability, Exercise and EquitY) study will prospectively investigate a diverse cohort of university employees after the opening of a new light rail transit (LRT) line and the easing of campus COVID-19 restrictions. Participants are current staff who live either < 1 mile, 1-2 miles, or > 2 miles from LRT, with equal distribution across economic and racial/ethnic strata. The primary aim is to assess change in physical activity, travel mode, and vehicle miles travelled using accelerometer and GPS devices. Equity outcomes include household transportation and health-related expenditures. Change in health outcomes, including depressive symptoms, stress, quality of life, body mass index and behavior change constructs related to transit use will be assessed via self-report. Pre-pandemic variables will be retrospectively collected. Participants will be measured at 3 times over 2 years of follow up. Longitudinal changes in outcomes will be assessed using multilevel mixed effects models. Analyses will evaluate whether proximity to LRT, sociodemographic, and environmental factors modify change in outcomes over time. DISCUSSION: The TROLLEY study will utilize rigorous methods to advance our understanding of health, well-being, and equity-oriented outcomes of new LRT infrastructure through the COVID-19 recovery period, in a sample of demographically diverse adult workers whose employment location is accessed by new transit. Results will inform land use, transportation and health investments, and workplace interventions. Findings have the potential to elevate LRT as a public health priority and provide insight on how to ensure public transit meets the needs of vulnerable users and is more resilient in the face of future health pandemics. TRIAL REGISTRATION: The TROLLEY study was registered at ClinicalTrials.gov ( NCT04940481 ) June 17, 2021, and OSF Registries ( https://doi.org/10.17605/OSF.IO/PGEHU ) June 24, 2021, prior to participant enrollment.

Analysing the impact of COVID-19 risk perceptions on route choice behaviour in train networks.

INTRODUCTION: Unlike previous pandemics, COVID-19 has sustained over a relatively longer period with cyclical infection waves and numerous variants. Public transport ridership has been hit particularly hard. To restore travellers' confidence it is critical to assess their risk determinants and trade-offs. METHODS: To this end, we survey train travellers in the Netherlands in order to: (i) quantify the impact of trip-specific, policy-based, and pandemic-related attributes on travellers' COVID-19 risk perceptions; and (ii) evaluate the trade-off between this risk perception and other travel attributes. Adopting the hierarchical information integration approach, in a two-stage stated preference experiment, respondents are asked to first rate how risky they perceive different travel situations to be, and then to choose between different travel options that include their own perceived risk rating as an attribute. Perceived risk ratings and choices between travel options are modelled using a linear regression and a mixed multinomial logit model, respectively. RESULTS: We find that on-board crowding and infection rates are the most important factors for risk perception. Amongst personal characteristics, the vulnerability of family and friends has the largest impact-nearly twice that of personal health risk. The bridging choice experiment reveals that while values of time have remained similar to pre-pandemic estimates, travellers are significantly more likely to choose routes with less COVID-19 risk (e.g., due to lower crowding). Respondents making longer trips by train value risk four times as much as their shorter trip counterparts. By combining the two models, we also report willingness to pay for mitigating factors: reduced crowding, mask mandates, and increased sanitization. CONCLUSION: Since we evaluate the impact of a large number of variables on route choice behaviour, we can use the estimated models to predict behaviour under detailed pandemic scenarios. Moreover, in addition to highlighting the importance of COVID-19 risk perceptions in public transport route choices, the results from this study provide valuable information regarding the mitigating impacts of various policies on perceived risk.

Correlation between mobility in mass transport and mortality due to COVID-19: A comparison of Mexico City, New York, and Madrid from a data science perspective.

In most big cities, public transports are enclosed and crowded spaces. Therefore, they are considered as one of the most important triggers of COVID-19 spread. Most of the existing research related to the mobility of people and COVID-19 spread is focused on investigating highly frequented paths by analyzing data collected from mobile devices, which mainly refer to geo-positioning records. In contrast, this paper tackles the problem by studying mass mobility. The relations between daily mobility on public transport (subway or metro) in three big cities and mortality due to COVID-19 are investigated. Data collected for these purposes come from official sources, such as the web pages of the cities' local governments. To provide a systematic framework, we applied the IBM Foundational Methodology for Data Science to the epidemiological domain of this paper. Our analysis consists of moving averages with a moving window equal to seven days so as to avoid bias due to weekly tendencies. Among the main findings of this work are: a) New York City and Madrid show similar distribution on studied variables, which resemble a Gauss bell, in contrast to Mexico City, and b) Non-pharmaceutical interventions don't bring immediate results, and reductions to the number of deaths due to COVID are observed after a certain number of days. This paper yields partial evidence for assessing the effectiveness of public policies in mitigating the COVID-19 pandemic.

COVID-19 and the value of safe transport in the United States.

We investigate the connection between the choice of transportation mode used by commuters and the probability of COVID-19 transmission. This interplay might influence the choice of transportation means for years to come. We present data on commuting, socioeconomic factors, and COVID-19 disease incidence for several US metropolitan areas. The data highlights important connections between population density and mobility, public transportation use, race, and increased likelihood of transmission. We use a transportation model to highlight the effect of uncertainty about transmission on the commuters' choice of transportation means. Using multiple estimation techniques, we found strong evidence that public transit ridership in several US metro areas has been considerably impacted by COVID-19 and by the policy responses to the pandemic. Concerns about disease transmission had a negative effect on ridership, which is over and above the adverse effect from the observed reduction in employment. The COVID-19 effect is likely to reduce the demand for public transport in favor of lower density alternatives. This change relative to the status quo will have implications for fuel use, congestion, accident frequency, and air quality. More vulnerable communities might be disproportionally affected as a result. We point to the need for additional studies to further quantify these effects and to assist policy in planning for the post-COVID-19 transportation future.

Transportation protocols for accurate assessment of microbial burden classification using molecular methods.

Point-of-care testing is cost-effective, rapid, and could assist in avoiding hospital visits during a pandemic. However, they present some significant risks that current technologies cannot fully address. Skin flora contamination and insufficient specimen volume are two major limitations preventing self-collection microbiological testing outside of hospital settings. We are developing a hybrid testing procedure to bridge the laboratory test with patient-side specimen collection and transportation for molecular microbial classification of causative bacterial infection and early identification of microbial susceptibility profiles directly from whole blood or urine specimens collected patient-side by health care workers such as phlebotomists in nursing homes or family clinics. This feasibility study presents our initial development efforts, in which we tested various transportation conditions (tubes, temperature, duration) for direct-from-specimen viable pathogen detection to determine the ideal conditions that allowed for differentiation between contaminant and causative bacteria in urine specimens and optimal growth for low-concentration blood specimens after transportation. For direct-from-urine assays, the viable pathogen at the clinical cutoff of 105 CFU/mL was detected after transportation with molecular assays while contaminants (≤ 104 CFU/mL) were not. For direct-from-blood assays, contrived blood samples as low as 0.8 CFU/mL were reported positive after transportation without the need for blood culture.

A Novel Health-Transportation Partnership Paves The Road For Young Driver Safety Through Virtual Assessment.

Motor vehicle crashes remain the leading cause of adolescent mortality and injury in the United States. For young drivers, crash risk peaks immediately after licensure and declines during the next two years, making the point of licensure an important safety intervention opportunity. Legislation in Ohio established a unique health-transportation partnership among the State of Ohio, Children's Hospital of Philadelphia, and Diagnostic Driving, Inc., to identify underprepared driver license applicants through a virtual driving assessment system. The system, a computer-based virtual driving test, exposes drivers to common serious crash scenarios to identify critical skill deficits and is delivered in testing centers immediately before the on-road examination. A pilot study of license applicants who completed it showed that the virtual driving assessment system accurately predicted which drivers would fail the on-road examination and provided automated feedback that informed drivers on their skill deficits. At this time, the partnership's work is informing policy changes around integrating the virtual driving assessment system into licensing and driver training with the aim of reducing crashes in the first months of independent driving. The system can be developed to identify deficits in safety-critical skills that lead to crashes in new drivers and to address challenges that the coronavirus disease 2019 pandemic has introduced to driver testing and training.

Provisional COVID-19 infrastructure induces large, rapid increases in cycling.

The bicycle is a low-cost means of transport linked to low risk of transmission of infectious disease. During the COVID-19 crisis, governments have therefore incentivized cycling by provisionally redistributing street space. We evaluate the impact of this new bicycle infrastructure on cycling traffic using a generalized difference in differences design. We scrape daily bicycle counts from 736 bicycle counters in 106 European cities. We combine these with data on announced and completed pop-up bike lane road work projects. Within 4 mo, an average of 11.5 km of provisional pop-up bike lanes have been built per city and the policy has increased cycling between 11 and 48% on average. We calculate that the new infrastructure will generate between $1 and $7 billion in health benefits per year if cycling habits are sticky.

Moving a research lab during the COVID-19 pandemic.

The COVID-19 pandemic has created additional challenges for mid-career investigators seeking new academic opportunities. JCB asked scientists to share their experiences of uprooting their research careers and laboratories during the pandemic.

Logistics of an advanced therapy medicinal product during COVID-19 pandemic in Italy: successful delivery of mesenchymal stromal cells in dry ice.

BACKGROUND: During the coronavirus disease-2019 (COVID-19) pandemic, Italian hospitals faced the most daunting challenges of their recent history, and only essential therapeutic interventions were feasible. From March to April 2020, the Laboratory of Advanced Cellular Therapies (Vicenza, Italy) received requests to treat a patient with severe COVID-19 and a patient with acute graft-versus-host disease with umbilical cord-derived mesenchymal stromal cells (UC-MSCs). Access to clinics was restricted due to the risk of contagion. Transport of UC-MSCs in liquid nitrogen was unmanageable, leaving shipment in dry ice as the only option. METHODS: We assessed effects of the transition from liquid nitrogen to dry ice on cell viability; apoptosis; phenotype; proliferation; immunomodulation; and clonogenesis; and validated dry ice-based transport of UC-MSCs to clinics. RESULTS: Our results showed no differences in cell functionality related to the two storage conditions, and demonstrated the preservation of immunomodulatory and clonogenic potentials in dry ice. UC-MSCs were successfully delivered to points-of-care, enabling favourable clinical outcomes. CONCLUSIONS: This experience underscores the flexibility of a public cell factory in its adaptation of the logistics of an advanced therapy medicinal product during a public health crisis. Alternative supply chains should be evaluated for other cell products to guarantee delivery during catastrophes.

Community Outbreak Investigation of SARS-CoV-2 Transmission Among Bus Riders in Eastern China.

Importance: Evidence of whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (COVID-19), can be transmitted as an aerosol (ie, airborne) has substantial public health implications. Objective: To investigate potential transmission routes of SARS-CoV-2 infection with epidemiologic evidence from a COVID-19 outbreak. Design, Setting, and Participants: This cohort study examined a community COVID-19 outbreak in Zhejiang province. On January 19, 2020, 128 individuals took 2 buses (60 [46.9%] from bus 1 and 68 [53.1%] from bus 2) on a 100-minute round trip to attend a 150-minute worship event. The source patient was a passenger on bus 2. We compared risks of SARS-CoV-2 infection among at-risk individuals taking bus 1 (n = 60) and bus 2 (n = 67 [source patient excluded]) and among all other individuals (n = 172) attending the worship event. We also divided seats on the exposed bus into high-risk and low-risk zones according to the distance from the source patient and compared COVID-19 risks in each zone. In both buses, central air conditioners were in indoor recirculation mode. Main Outcomes and Measures: SARS-CoV-2 infection was confirmed by reverse transcription polymerase chain reaction or by viral genome sequencing results. Attack rates for SARS-CoV-2 infection were calculated for different groups, and the spatial distribution of individuals who developed infection on bus 2 was obtained. Results: Of the 128 participants, 15 (11.7%) were men, 113 (88.3%) were women, and the mean age was 58.6 years. On bus 2, 24 of the 68 individuals (35.3% [including the index patient]) received a diagnosis of COVID-19 after the event. Meanwhile, none of the 60 individuals in bus 1 were infected. Among the other 172 individuals at the worship event, 7 (4.1%) subsequently received a COVID-19 diagnosis. Individuals in bus 2 had a 34.3% (95% CI, 24.1%-46.3%) higher risk of getting COVID-19 compared with those in bus 1 and were 11.4 (95% CI, 5.1-25.4) times more likely to have COVID-19 compared with all other individuals attending the worship event. Within bus 2, individuals in high-risk zones had moderately, but nonsignificantly, higher risk for COVID-19 compared with those in the low-risk zones. The absence of a significantly increased risk in the part of the bus closer to the index case suggested that airborne spread of the virus may at least partially explain the markedly high attack rate observed. Conclusions and Relevance: In this cohort study and case investigation of a community outbreak of COVID-19 in Zhejiang province, individuals who rode a bus to a worship event with a patient with COVID-19 had a higher risk of SARS-CoV-2 infection than individuals who rode another bus to the same event. Airborne spread of SARS-CoV-2 seems likely to have contributed to the high attack rate in the exposed bus. Future efforts at prevention and control must consider the potential for airborne spread of the virus.

Path Optimization of Medical Waste Transport Routes in the Emergent Public Health Event of COVID-19: A Hybrid Optimization Algorithm Based on the Immune-Ant Colony Algorithm.

In response to the emergent public health event of COVID-19, the efficiency of transport of medical waste from hospitals to disposal stations is a worthwhile issue to study. In this paper, based on the actual situation of COVID-19 and environmental impact assessment guidelines, an immune algorithm is used to establish a location model of urban medical waste storage sites. In view of the selection of temporary storage stations and realistic transportation demand, an efficiency-of-transport model of medical waste between hospitals and temporary storage stations is established by using an ant colony-tabu hybrid algorithm. In order to specify such status, Wuhan city in Hubei Province, China-considered the first city to suffer from COVID-19-was chosen as an example of verification; the two-level model and the immune algorithm-ant colony optimization-tabu search (IA-ACO-TS) algorithm were used for simulation and testing, which achieved good verification. To a certain extent, the model and the algorithm are proposed to solve the problem of medical waste disposal, based on transit temporary storage stations, which we are convinced will have far-reaching significance for China and other countries to dispatch medical waste in response to such public health emergencies.