"Kind of empowered": Perceptions of socio-emotional development in children driving ride-on cars.

PURPOSE/OBJECTIVE: Early powered mobility (PM) experiences can be essential facilitators of self-initiated mobility, socialization, and exploration for young children with disabilities. Cerebral palsy (CP) and developmental delay are two of the most common diagnoses associated with motor disability in young children with 1 in 345 children diagnosed with CP and 1 in 6 with developmental delay in the US. The purpose of this study was to explore the longitudinal experiences and caregiver perceptions of socio-emotional development in particular, in young children with disabilities during modified ride-on car (ROC) use. RESEARCH METHOD/DESIGN: A qualitative, grounded theory approach was used. Semi-structured interviews were conducted with 15 families (children ages 1-4 with CP or developmental delay) at baseline, 6 months (as able due to COVID), and 1 year following ROC introduction. Data were coded independently by three researchers using constant comparison until data saturation occurred and themes emerged. RESULTS: Four themes emerged from the data: "Leveling the Playing Field," "Breaking Down Barriers," "Fun and Work: ROC as Toy and Therapy Device," and "Mobility is a Pathway to Autonomy." Conclusions/Implication: Children and caregivers viewed ROCs as both fun and therapeutic, consistently identifying perceived benefits for children's socio-emotional development. This qualitative study provides a better understanding of the complexities and impact of ROCs on children and their families in the socio-emotional domain and may help facilitate clinical decision-making when introducing PM to young children with disabilities as part of a multimodal approach to early intervention. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Uncovering the behavioral determinants behind private car purchase intention during the new normal of COVID-19: An empirical investigation in China.

Based on the Protection Motivation Theory (PMT), the Psychological Reactance Theory (PRT), and the Theory of Planned Behavior (TPB), we revealed the psychological impact factors of individuals' private car purchase intentions during the new normal of COVID-19. Structural equation modeling (SEM) and Bayesian network (BN) were used to analyzed the car purchase decision-making mechanism. A questionnaire survey was conducted to collect empirical data from April 20th to May 26th of 2020 in China. We investigated 645 participants and analyzed the data. The SEM results showed that conditional value, pro-car-purchasing attitude, and perceived behavioral control, health value, and cost factors have significant direct effects on car purchase intention. According to BN's prediction of purchase intention, the probability of high purchase intention grew by 47.6%, 97.3% and 163.0%, respectively, with perceived behavioral control, pro-car-purchasing attitude, and conditional value shifting from "low" to "medium" and "high". This study provided a new perspective for researchers to explore the purchase intention of cars during the epidemic. Meanwhile, we could provide a reference for the government and enterprises to develop measures related to the automobile market."

Characterisation of urban environment and activity across space and time using street images and deep learning in Accra.

The urban environment influences human health, safety and wellbeing. Cities in Africa are growing faster than other regions but have limited data to guide urban planning and policies. Our aim was to use smart sensing and analytics to characterise the spatial patterns and temporal dynamics of features of the urban environment relevant for health, liveability, safety and sustainability. We collected a novel dataset of 2.1 million time-lapsed day and night images at 145 representative locations throughout the Metropolis of Accra, Ghana. We manually labelled a subset of 1,250 images for 20 contextually relevant objects and used transfer learning with data augmentation to retrain a convolutional neural network to detect them in the remaining images. We identified 23.5 million instances of these objects including 9.66 million instances of persons (41% of all objects), followed by cars (4.19 million, 18%), umbrellas (3.00 million, 13%), and informally operated minibuses known as tro tros (2.94 million, 13%). People, large vehicles and market-related objects were most common in the commercial core and densely populated informal neighbourhoods, while refuse and animals were most observed in the peripheries. The daily variability of objects was smallest in densely populated settlements and largest in the commercial centre. Our novel data and methodology shows that smart sensing and analytics can inform planning and policy decisions for making cities more liveable, equitable, sustainable and healthy.

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

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

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.

Effect of Seat Angle when Sleeping in a Car on Quality of Sleep and Its Impact on Calculation Performance the Following Day.

The number of occasions to stay in a car overnight is increasing during disasters; however, the effects on sleep and the impact on daytime functioning are not well understood. We investigated the effect of seat angle when sleeping in a car and its impact on calculation performance the following day. Fifteen healthy males participated in three trials (sleeping in a car with the front seat angled at 45° and 60° in a laboratory and sleeping at home); sleep and calculation performance the following day were compared. Increased wake after sleep onset and decreased slow-wave sleep were observed in the 60° trial, that is, near-vertical, compared with the others. Subjective sleep quality and calculation performance in the 45° and 60° trials were poorer than those in the home trial. The effect of seat angle on sleep was confirmed objectively, but not subjectively, suggesting that a large seat angle might cause sleep impairment.

Application and Communication Optimization Technology of Unmanned Distribution Car under Deep Learning in Logistics Express of COVID-19.

This work aims to solve the problem that the daily necessities of urban residents cannot be delivered during coronavirus disease 2019 (COVID-19), thereby reducing the possibility of the delivery personnel contracting COVID-19 due to the need to transport medicines to the hospital during the epidemic. Firstly, this work studies the application and communication optimization technology of unmanned delivery cars based on deep learning (DL) under COVID-19. Secondly, a route planning method for unmanned delivery cars based on the DL method is proposed under the influence of factors such as maximum flight time, load, and road conditions. This work analyzes and introduces unmanned delivery cars from four aspects combined with the actual operation of unmanned delivery cars and related literature: the characteristics, delivery mode, economy, and limitations of unmanned delivery cars. The unmanned delivery car is in the promotion stage. A basic AVRPTW model is established that minimizes the total delivery cost without considering the charging behavior under the restriction of some routes, delivery time, load, and other factors. The path optimization problem of unmanned delivery cars in various situations is considered. A multiobjective optimization model of the unmanned delivery car in the charging/swap mode is established with the goal of minimizing the total delivery cost and maximizing customer satisfaction under the premise of meeting the car driving requirements. An improved genetic algorithm is designed to solve the established model. Finally, the model is tested, and its results are analyzed. The effectiveness of this route planning method is proved through case analysis. Customer satisfaction, delivery time, cost input, and other aspects have been greatly improved through the improvement and optimization of the unmanned delivery car line, which has been well applied in practice. In addition, unmanned delivery cars are affected by many factors such as load, and the service time required for delivery is longer. Therefore, this work chooses an unmanned distribution car with strong endurance to improve distribution efficiency. The new hospital contactless distribution mode discussed here will play an important role in promoting future development.

Dynamic accessibility by car to tertiary care emergency services in Cali, Colombia, in 2020: cross-sectional equity analyses using travel time big data from a Google API.

OBJECTIVES: To test a new approach to characterise accessibility to tertiary care emergency health services in urban Cali and assess the links between accessibility and sociodemographic factors relevant to health equity. DESIGN: The impact of traffic congestion on accessibility to tertiary care emergency departments was studied with an equity perspective, using a web-based digital platform that integrated publicly available digital data, including sociodemographic characteristics of the population and places of residence with travel times. SETTING AND PARTICIPANTS: Cali, Colombia (population 2.258 million in 2020) using geographic and sociodemographic data. The study used predicted travel times downloaded for a week in July 2020 and a week in November 2020. PRIMARY AND SECONDARY OUTCOMES: The share of the population within a 15 min journey by car from the place of residence to the tertiary care emergency department with the shortest journey (ie, 15 min accessibility rate (15mAR)) at peak-traffic congestion hours. Sociodemographic characteristics were disaggregated for equity analyses. A time-series bivariate analysis explored accessibility rates versus housing stratification. RESULTS: Traffic congestion sharply reduces accessibility to tertiary emergency care (eg, 15mAR was 36.8% during peak-traffic hours vs 84.4% during free-flow hours for the week of 6-12 July 2020). Traffic congestion sharply reduces accessibility to tertiary emergency care. The greatest impact fell on specific ethnic groups, people with less educational attainment and those living in low-income households or on the periphery of Cali (15mAR: 8.1% peak traffic vs 51% free-flow traffic). These populations face longer average travel times to health services than the average population. CONCLUSIONS: These findings suggest that health services and land use planning should prioritise travel times over travel distance and integrate them into urban planning. Existing technology and data can reveal inequities by integrating sociodemographic data with accurate travel times to health services estimates, providing the basis for valuable indicators.

Infection risk in cable cars and other enclosed spaces.

As virus-laden aerosols can accumulate and remain suspended for hours in insufficiently ventilated enclosed spaces, indoor environments can heavily contribute to the spreading of airborne infections. In the COVID-19 pandemics, the role possibly played by cable cars has attracted media attention following several outbreaks in ski resort. To assess the real risk of infection, we experimentally characterize the natural ventilation in cable cars and develop a general stochastic model of infection in an arbitrary indoor space that accounts for the epidemiological situation, the virological parameters, and the indoor characteristics (ventilation rate and occupant number density). As a results of the high air exchange rate (we measured up to 180 air changes per hour) and the relatively short duration of the journey, the infection probability in cable cars traveling with open windows is remarkably lower than in other enclosed spaces such as aircraft cabins, train cars, offices, classrooms, and dining rooms. Accounting for the typical duration of the stay, the probability of infection during a cable-car ride is lower by two to three orders of magnitude than in the other examples considered (the highest risk being estimated in case of a private gathering in a poorly ventilated room). For most practical purposes, the infection probability can be approximated by the inhaled viral dose, which provides an upper bound and allows a simple comparison between different indoor situations once the air exchange rate and the occupant number density are known. Our approach and findings are applicable to any indoor space in which the viral transmission is predominately airborne and the air is well mixed.

Conceptual Design of the "Private Car" Self-Isolation Ecosystem for the 2019-nCoV Infection.

Since the beginning of the COVID-19 outbreak, confirmed and suspected cases of the disease have been increasing rapidly. The isolation of cases is one of the most effective methods for the control and containment of COVID-19 and has been rapidly popularized. Problems with isolation have gradually emerged, such as the inadequate allocation of isolation resources and the failure to properly resettle many of the suspected cases of the 2019-nCoV infection. In this paper, a self-isolation ecosystem of a rapid-deploying negative-pressurized "private car" is proposed for housing patients with 2019-nCoV infection, which could be lightweight, moderately sized and transparent to enable group supervision and communication. This "private car" isolation method aims to achieve self-isolation of patients and essentially solves the problem of where and how to isolate suspected cases while saving isolation resources and preventing the large-scale transmission of COVID-19.

State and city laws governing the use of child restraint systems in rideshare vehicles and taxicabs: requirements and responsibility.

OBJECTIVES: To identify, describe and critique state and local policies related to child passenger safety in for-hire motor vehicles including ridesharing and taxis. METHODS: We used standard legal research methods to collect policies governing the use of child restraint systems (CRS) in rideshare and taxi vehicles for all 50 states and the 50 largest cities in the USA. We abstracted the collected policies to determine whether the policy applies to specific vehicles, requires specific safety restraints in those vehicles, lists specific requirements for use of those safety restraints, seeks to enhance compliance and punishes noncompliance. RESULTS: All 50 states have policies that require the use of CRS for children under a certain age, weight or height. Seven states exempt rideshare vehicles and 28 states exempt taxis from their CRS requirements. Twelve cities have relevant policies with eight requiring CRS in rideshare vehicles, but not taxis, and two cities requiring CRS use in both rideshare vehicles and taxis. CONCLUSION: Most states require CRS use in rideshare vehicles, but not as many require CRS use in taxis. Though states describe penalties for drivers who fail to comply with CRS requirements, these penalties do not actually facilitate the use of CRS in rideshare or taxis. Furthermore, there is ambiguity in the laws about who is responsible for the provision and installation of the restraints. To prevent serious or fatal injuries in children, policy-makers should adopt policies that require, incentivise and facilitate the use of CRS in rideshare vehicles and taxis.

CuO-Coated Antibacterial and Antiviral Car Air-Conditioning Filters.

The demand for improved indoor air quality, especially during the pandemic of Covid-19, has led to renewed interest in antiviral and antibacterial air-conditioning systems. Here, air filters of vehicles made of nonwoven polyester filter media were sonochemically coated with CuO nanoparticles by a roll-to-roll coating method. The product, aimed at providing commuters with high air quality, showed good stability and mechanical properties and potent activity against Escherichia coli and Staphylococcus aureus bacteria, H1N1 influenza, and two SARS-CoV-2 variants. The filtering properties of a coated filter were tested, and they were similar to those of the uncoated filter. Leaching tests as a function of airflow were conducted, and the main outcome was that the coating was stable and particles were not detached from the coated media. Extension to other air-conditioning systems was straightforward.

Numerical study of when and who will get infected by coronavirus in passenger car.

In light of the COVID-19 pandemic, it is becoming extremely necessary to assess respiratory disease transmission in passenger cars. This study numerically investigated the human respiration activities' effects, such as breathing and speaking, on the transport characteristics of respiratory-induced contaminants in passenger car. The main objective of the present study is to accurately predict when and who will get infected by coronavirus while sharing a passenger car with a patient of COVID-19 or similar viruses. To achieve this goal, transient simulations were conducted in passenger car. We conducted a 3D computational fluid dynamics (CFD)-based investigation of indoor airflow and the associated aerosol transport in a passenger car. The Eulerian-Eulerian flow model coupled with k-ε turbulence approach was used to track respiratory contaminants with diameter ≥ 1 µm that were released by different passengers within the passenger car. The results showed that around 6.38 min, this is all that you need to get infected with COVID-19 when sharing a poorly ventilated car with a driver who got coronavirus. It also has been found that enhancing the ventilation system of the passenger car will reduce the risk of contracting Coronavirus. The predicted results could be useful for future engineering studies aimed at designing public transport and passenger cars to face the spread of droplets that may be contaminated with pathogens.

A Study on the Decay Model of Multi-Block Taxi Travel Demand under the Influence of Major Urban Public Health Events.

A sudden major public health event is likely to have a negative impact on public transport travel for residents, with public travel modes such as the metro and conventional buses experiencing varying degrees of decline in patronage. As a complement to public transport, taxi travel will suffer the same impact. Land use and population density among various functional blocks in a city are different, and therefore their changing rates in taxi travel demand are varied. This paper reveals the taxi travel demand correlations between urban blocks and then constructs a taxi travel demand decay model based on the Dynamic Input-Output Inoperability Model (DIIM) to simulate the decay degree of taxi travel demand in each block. When a major public health event occurs, the residential panic levels in different functional blocks may vary. It results in variable changing speeds of residential travel demand in each block. Based on this assumption, we use the intensity of travel demand as a correlation strength factor between blocks, and equate it with the technical coefficient in the DIIM model. We also define other variables to serve in model construction. These variables include the decay degree of travel demand intensity, residential travel willingness, coefficient of travel demand decay, derivative coefficient of travel demand interdependency, and demand perturbation coefficient. Lastly, we select a central area of Ningbo as the study area, and use taxi travel data in Ningbo during the COVID-19 pandemic of 2020 as input, simulate taxi travel demand dynamics, and analyze the accuracy and sensitivity of the model parameters. The relative errors between the five types of blocks and the actual decay of travel demand intensity are 8.3%, 3.8%, 8.7%, 5.5%, and 5.3%, respectively, which can basically match the actual situation, proving the validity of the model. The results of the study reveal the pattern of taxi travel demand decay among various blocks after major public health events. It provides methodological reference for decision makers to understand the development trend of multi-block taxi travel demand, so as to help form effective emergency plans for different blocks.

Risk of SARS-CoV-2 in a car cabin assessed through 3D CFD simulations.

In this study, the risk of infection from SARS-CoV-2 Delta variant of passengers sharing a car cabin with an infected subject for a 30-min journey is estimated through an integrated approach combining a recently developed predictive emission-to-risk approach and a validated CFD numerical model numerically solved using the open-source OpenFOAM software. Different scenarios were investigated to evaluate the effect of the infected subject position within the car cabin, the airflow rate of the HVAC system, the HVAC ventilation mode, and the expiratory activity (breathing vs. speaking). The numerical simulations here performed reveal that the risk of infection is strongly influenced by several key parameters: As an example, under the same ventilation mode and emitting scenario, the risk of infection ranges from zero to roughly 50% as a function of the HVAC flow rate. The results obtained also demonstrate that (i) simplified zero-dimensional approaches limit proper evaluation of the risk in such confined spaces, conversely, (ii) CFD approaches are needed to investigate the complex fluid dynamics in similar indoor environments, and, thus, (iii) the risk of infection in indoor environments characterized by fixed seats can be in principle controlled by properly designing the flow patterns of the environment.

To Use or Not Use Car Sharing Mobility in the Ongoing COVID-19 Pandemic? Identifying Sharing Mobility Behaviour in Times of Crisis.

Car sharing services have expanded in order to meet the new necessities of mobility worldwide in an innovative way. Before the COVID-19 pandemic, car sharing was a very popular mode of transportation among young adults in big cities. However, during this ongoing pandemic and with public transportation considered a super-spreading transmitter, the usage of car sharing is unclear. Therefore, the aim of this study, which is explorative in nature, is to investigate the usage, advantages, drivers, and barriers to car sharing during this ongoing pandemic era. To this end, 66 interviews were conducted among users of car sharing during the COVID-19 pandemic. The findings provide key information for the planning of car sharing operations and public transportation in the context of avoiding COVID-19 infection and respecting the recommendations of local governments. In addition, new emerging profiles of car sharing users in the ongoing pandemic are identified. This research provides relevant insights for both business practice and policy makers.

Positive environmental impact of remote teleconsultation in urology during the COVID-19 pandemic in a highly populated area.

INTRODUCTION: Greenhouse gas (GHG) emissions are a serious environmental issue. The healthcare sector is an important emitter of GHGs. Our aim was to assess the environmental cost of teleconsultations in urology compared to face-to-face consultations. MATERIALS AND METHODS: Prospective study of all patients who had a remote teleconsultation over a 2-week period during COVID-19 pandemic. Main outcome was the reduction in CO2e emissions related to teleconsultation compared to face-to-face consultation and was calculated as: total teleconsultation CO2e emissions-total face-to-face consultation CO2e emissions. Secondary outcome measures were the reduction in travel distance and travel time related to teleconsultation. RESULTS: Eighty patients were included. Face-to-face consultations would have resulted in 6699km (4162 miles) of travel (83.7km (52 miles) per patient). Cars were the usual means of transport. CO2e avoided due to lack of travel was calculated at 1.1 tonnes. Teleconsultation was responsible for 1.1kg CO2e while face-to-face consultation emitted 0.5kg of CO2e. Overall, the total reduction in GHGs with teleconsultation was 1141kg CO2e, representing a 99% decrease in emissions. Total savings on transport were 974 € and savings on travel time were 112h (1.4h/patient). CONCLUSIONS: Teleconsultation reduces the environmental impact of face-to-face consultations. The use of teleconsultation in our urology departments resulted in the avoidance of more than 6000km of travel, equivalent to a reduction of 1.1 tonnes of CO2e. Teleconsultation should be considered for specific indications as the healthcare system attempts to become greener. LEVEL OF EVIDENCE: 3.

Activity-based epidemic propagation and contact network scaling in auto-dependent metropolitan areas.

We build on recent work to develop a fully mechanistic, activity-based and highly spatio-temporally resolved epidemiological model which leverages person-trajectories obtained from an activity-based model calibrated for two full-scale prototype cities, consisting of representative synthetic populations and mobility networks for two contrasting auto-dependent city typologies. We simulate the propagation of the COVID-19 epidemic in both cities to analyze spreading patterns in urban networks across various activity types. Investigating the impact of the transit network, we find that its removal dampens disease propagation significantly, suggesting that transit restriction is more critical for mitigating post-peak disease spreading in transit dense cities. In the latter stages of disease spread, we find that the greatest share of infections occur at work locations. A statistical analysis of the resulting activity-based contact networks indicates that transit contacts are scale-free, work contacts are Weibull distributed, and shopping or leisure contacts are exponentially distributed. We validate our simulation results against existing case and mortality data across multiple cities in their respective typologies. Our framework demonstrates the potential for tracking epidemic propagation in urban networks, analyzing socio-demographic impacts and assessing activity- and mobility-specific implications of both non-pharmaceutical and pharmaceutical intervention strategies.

The nexus between in-car aerosol concentrations, ventilation and the risk of respiratory infection.

We examined the trade-offs between in-car aerosol concentrations, ventilation and respiratory infection transmission under three ventilation settings: windows open (WO); windows closed with air-conditioning on ambient air mode (WC-AA); and windows closed with air-conditioning on recirculation (WC-RC). Forty-five runs, covering a total of 324 km distance on a 7.2-km looped route, were carried out three times a day (morning, afternoon, evening) to monitor aerosols (PM2.5; particulate matter < 2.5 µm and PNC; particle number concentration), CO2 and environmental conditions (temperature and relative humidity). Ideally, higher ventilation rates would give lower in-car pollutant concentrations due to dilution from outdoor air. However, in-car aerosol concentrations increased with ventilation (WO > WC-AA > WC-RC) due to the ingress of polluted outdoor air on urban routes. A clear trade-off, therefore, exists for the in-car air quality (icAQ) versus ventilation; for example, WC-RC showed the least aerosol concentrations (i.e. four-times lower compared with WO), but corresponded to elevated CO2 levels (i.e. five-times higher compared with WO) in 20 mins. We considered COVID-19 as an example of respiratory infection transmission. The probability of its transmission from an infected occupant in a five-seater car was estimated during different quanta generation rates (2-60.5 quanta hr-1) using the Wells-Riley model. In WO, the probability with 50%-efficient and without facemasks under normal speaking (9.4 quanta hr-1) varied only by upto 0.5%. It increased by 2-fold in WC-AA (<1.1%) and 10-fold in WC-RC (<5.2%) during a 20 mins trip. Therefore, a wise selection of ventilation settings is needed to balance in-car exposure in urban areas affected by outdoor air pollution and that by COVID-19 transmission. We also successfully developed and assessed the feasibility of using sensor units in static and dynamic environments to monitor icAQ and potentially infer COVID-19 transmission. Further research is required to develop automatic-alarm systems to help reduce both pollutant exposure and infection from respiratory COVID-19 transmission.