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This work shows that fillers - short utterances like “ehm” and “uhm” - allow one to predict whether someone is above median along the Big-Five personality traits. The experiments have been performed over a corpus of 2,988 fillers uttered by 120 different speakers in spontaneous conversations. The results show that the prediction accuracies range between 74% and 82% depending on the particular trait. The proposed approach includes a feature selection step - based on Quantum Evolutionary Algorithms - that has been used to detect the personality markers, i.e., the subset of the features that better account for the prediction outcomes and, indirectly, for the personality of the speakers. The results show that only a relatively few features tend to be consistently selected, thus acting as reliable personality markers.

The COVID-19 pandemic has had an unprecedented impact on the transportation sector, with changes expected to last for a long time. Following the reopening phases, increased use of private vehicles and continued avoidance of exposure to the infectious virus in public transportation exacerbated urban traffic and environmental problems. The paper aims to evaluate the impact of travel demand management strategies during the COVID-19 pandemic in Tehran, Iran. That city provided a unique case study because of its high COVID-19 contagion and death rates, traffic congestion, and lack of government restriction plans, which are common in developing countries. Four scenarios combining various travel demand management strategies were considered during the pandemic. The findings highlight the importance of considering the tradeoffs between different performance measures when selecting a suitable travel demand management strategy. Depending on the goals that decision-makers have set, the strategies may not be additive. While teleworking, e-learning, and rescheduling working hours all help reduce traffic congestion, the pricing strategy contributes significantly to meeting environmental and health goals. The findings also highlight the nonlinear pattern of change associated with each strategy.

COVID-19 has raised new challenges for transportation in the post-pandemic era. The social distancing requirement, with the aim of reducing contact risk in public transit, could exacerbate traffic congestion and emissions. We propose a simulation tool to evaluate the trade-offs between traffic congestion, emissions, and policies impacting travel behavior to mitigate the spread of COVID-19 including social distancing and working from home. Open-source agent-based simulation models are used to evaluate the transportation system usage for the case study of New York City. A Post Processing Software for Air Quality (PPS-AQ) estimation is used to evaluate the air quality impacts. Finally, system-wide contact exposure on the subway is estimated from the traffic simulation output. The social distancing requirement in public transit is found to be effective in reducing contact exposure, but it has negative congestion and emission impacts on Manhattan and neighborhoods at transit and commercial hubs. While telework can reduce congestion and emissions citywide, in Manhattan the negative impacts are higher due to behavioral inertia and social distancing. The findings suggest that contact exposure to COVID-19 on subways is relatively low, especially if social distancing practices are followed. The proposed integrated traffic simulation models and air quality estimation model can help policymakers evaluate the impact of policies on traffic congestion and emissions as well as identifying hot spots, both temporally and spatially.

Objective Electronic health record (EHR) data linked with address-based metrics using geographic information systems (GIS) are emerging data sources in population health studies. This study examined this approach through a case study on the associations between changes in ejection fraction (EF) and the built environment among heart failure (HF) patients. Materials and Methods We identified 1287 HF patients with at least 2 left ventricular EF measurements that are minimally 1 year apart. EHR data were obtained at an academic medical center in New York for patients who visited between 2012 and 2017. Longitudinal clinical information was linked with address-based built environment metrics related to transportation, air quality, land use, and accessibility by GIS. The primary outcome is the increase in the severity of EF categories. Statistical analyses were performed using mixed-effects models, including a subgroup analysis of patients who initially had normal EF measurements. Results Previously reported effects from the built environment among HF patients were identified. Increased daily nitrogen dioxide concentration was associated with the outcome while controlling for known HF risk factors including sex, comorbidities, and medication usage. In the subgroup analysis, the outcome was significantly associated with decreased distance to subway stops and increased distance to parks. Conclusions Population health studies using EHR data may drive efficient hypothesis generation and enable novel information technology-based interventions. The availability of more precise outcome measurements and home locations, and frequent collection of individual-level social determinants of health may further drive the use of EHR data in population health studies.

Introduction: Externalities from transportation, and in particular exposure to vehicle emissions have been considered a possible cause of several negative health outcomes including mortality. However, the existing findings are too inconsistent to drive a well-founded exposure-response function to be fully exploited to curb the negative impacts of transportation systems on public health. In this study, we investigate the association between exposure to air pollution and mortality. We then evaluate how using different air quality methods may result in detecting different health outcomes. Methods: We conduct an analysis of reviewing a representative sample of main published studies that specifically focused on the association between vehicle air pollution and mortality. Results: Our study finds that vehicle air pollution may increase the risk of mortality through a slightly high association. Most importantly, the risk of overall mortality increases by 5% per 10 µg/m3 increase in NO2 concentration, 2% per unit of traffic intensity on the road, and 7% per unit of distance closer to the road. Conclusion: The findings imply the role of exposure to vehicle emissions in increasing the risk of mortality. The method used to detect the health outcomes can alter the health finding from positive to null or vice versa and even extensively affect the analysis outcomes. The results suggest the need for establishing indicators to benchmark the performance of air quality methods and emphasize the necessity to integrate public health measures into the urban and transportation planning process.

Background Attachment research has been limited by the lack of quick and easy measures. We report development and validation of the School Attachment Monitor (SAM), a novel measure for largescale assessment of attachment in children aged 5–9, in the general population. SAM offers automatic presentation, on computer, of story-stems based on the Manchester Child Attachment Story Task (MCAST), without the need for trained administrators. SAM is delivered by novel software which interacts with child participants, starting with warm-up activities to familiarise them with the task. Children’s story completion is video recorded and augmented by ‘smart dolls’ that the child can hold and manipulate, with movement sensors for data collection. The design of SAM was informed by children of users’ age range to establish their task understanding and incorporate their innovative ideas for improving SAM software. Methods 130 5–9 year old children were recruited from mainstream primary schools. In Phase 1, sixty-one children completed both SAM and MCAST. Inter-rater reliability and rating concordance was compared between SAM and MCAST. In Phase 2, a further 44 children completed SAM complete and, including those children completing SAM in Phase 1 (total n = 105), a machine learning algorithm was developed using a “majority vote” procedure where, for each child, 500 non-overlapping video frames contribute to the decision. Results Using manual rating, SAM-MCAST concordance was excellent (89% secure versus insecure; 97% organised versus disorganised; 86% four-way). Comparison of human ratings of SAM versus the machine learning algorithm showed over 80% concordance. Conclusions We have developed a new tool for measuring attachment at the population level, which has good reliability compared to a validated attachment measure and has the potential for automatic rating–opening the door to measurement of attachment in large populations.

Traffic congestion is a major challenge in metropolitan areas due to economic and negative health impacts. Several strategies have been tested all around the globe to relieve traffic congestion and minimize transportation externalities. Congestion pricing is among the most cited strategies with the potential to manage the travel demand. This study aims to investigate potential travel behavior changes in response to cordon pricing in Manhattan, New York. Several pricing schemes with variable cordon charging fees are designed and examined using an activity-based microsimulation travel demand model. The findings demonstrate a decreasing trend in the total number of trips interacting with the central business district (CBD) as the price goes up, except for intrazonal trips. We also analyze a set of other performance measures, such as Vehicle-Hours of Delay, Vehicle-Miles Traveled, and vehicle emissions. While the results show considerable growth in transit ridership (6%), single-occupant vehicles and taxis trips destined to the CBD reduced by 30% and 40%, respectively, under the $20 pricing scheme. The aggregated value of delay for all vehicles was also reduced by 32%. Our findings suggest that cordon pricing can positively ameliorate transportation network performance and consequently, improve air quality by reducing particular matter inventory by up to 17.5%. The results might facilitate public acceptance of cordon pricing strategies for the case study of NYC. More broadly, this study provides a robust framework for decision-makers across the US for further analysis on the subject.

The Intergovernmental Panel on Climate Change estimates that greenhouse gas emissions (GHG) must be cut 40–70% by 2050 to prevent a greater than 2 °Celsius increase in the global mean temperature; a threshold that may avoid the most severe climate change impacts. Transportation accounts for about one third of GHG emissions in the United States; reducing these emissions should therefore be an important part of any strategy aimed at meeting the IPCC targets. Prior studies find that improvements in vehicle energy efficiency or decarbonization of the transportation fuel supply would be required for the transportation sector to achieve the IPCC targets. Strategies that could be implemented by regional transportation planning organizations are generally found to have only a modest GHG reduction potential. In this study we challenge these findings. We evaluate what it would take to achieve deep GHG emission reductions from transportation without advances in vehicle energy efficiency and fuel decarbonization beyond what is currently expected under existing regulations and market expectations. We find, based on modeling conducted in the Albuquerque, New Mexico metropolitan area that it is possible to achieve deep reductions that may be able to achieve the IPCC targets. Achieving deep reductions requires changes in transportation policy and land-use planning that go far beyond what is currently planned in Albuquerque and likely anywhere else in the United States.

Long range regional transportation plans (LRTPs) are typically evaluated with performance measures calculated for the first and final years of the planning period. We call this the endpoint modeling method. Planning periods span 20–30 or more years, and therefore the endpoint method can overlook important changes that occur during interim years as well as cumulative impacts. For example, the impact of GHG emissions accumulating in the atmosphere and chronic or deadly diseases caused by exposure to high concentrations of toxic vehicle emissions cannot be reversed by plans that only perform well in the distant future. In this study we evaluate the annual performance of a LRTP created for the Albuquerque, New Mexico metropolitan area over a 28-year period by modeling land-use, travel demand, vehicle emissions and emissions exposure using an incremental and highly integrated land-use and travel demand modeling method. We call this the annual modeling method. We find non-linear and sometimes complex changes in annual emission rates, pollution exposure and other performance measures, indicating that end of period performance metrics may not be robust indicators of average and overall plan performance, which we argue are important considerations. Furthermore, we find that the annual modeling method has a large effect on land-use, traffic and emission exposure forecasts. By the plan’s final year, the annual modeling method forecasts greater population and employment, and correspondingly greater traffic congestion and air pollutant concentrations in the region’s largest activity centers than the endpoint modeling method, which is used by most MPOs.

The discussion of whether, and to what extent, telecommuting can curb congestion in urban areas has spanned more than three decades. This study develops an integrated framework to provide the empirical evidence of the potential impacts of home-based telecommuting on travel behavior, network congestion, and air quality. In the first step, we estimate a telecommuting adoption model using a zero-inflated hierarchical ordered probit model to determine the factors associated with workers’ propensity to adopt telecommuting. Second, we implement the estimated model in the POLARIS activity-based framework to simulate the potential changes in workers’ activity-travel patterns and network congestion. Third, the MOVES mobile source emission simulator and Autonomie vehicle energy simulator are used to estimate the potential changes in vehicular emissions and fuel use in the network as a result of this policy. Different policy adoption scenarios are then tested in the proposed integrated platform. We found that compared to the current baseline situation where almost 12% of workers in Chicago region have flexible working time schedule, in the case when 50% of workers have flexible working time, telecommuting can reduce total daily vehicle miles traveled (VMT) and vehicle hours traveled (VHT) up to 0.69% and 2.09%, respectively. Considering the same comparison settings, this policy has the potential to reduce greenhouse gas and particulate matter emissions by up to 0.71% and 1.14%, respectively. In summary, our results endorse the fact that telecommuting policy has the potential to reduce network congestion and vehicular emissions specifically during rush hours.

Road pricing is advocated as an effective travel demand management strategy to alleviate traffic congestion and improve environmental conditions. This paper analyzes the impacts of cordon pricing on the population’s daily activity pattern and their exposure to particulate matter by integrating activity-based models with air quality and exposure models in the case of New York City. To estimate changes in public exposure under cordon pricing scenarios, we take a sample of employees and study their mobility behavior during the day, which is mainly attributed to the location of the work and the time spent at work. The selection of employees and their exposure during the duration of their work is due to the unavailability of exact activity patterns for each individual. We show that the Central Business District (CBD) experiences a high concentration of PM2.5 emissions. Results indicate that implementing cordon pricing scenarios can reduce the population-weighted mean of exposure to PM2.5 emissions by 7% to 13% for our sample and, in particular, by 22% to 28% for those who work in the CBD. Furthermore, using an experimental model and assuming constant conditions, we point out the positive influence on indoor exposure for two locations inside and outside the CBD in response to cordon pricing. Considering the correlation between long-term exposure to fine particulate matter and the risks of developing cardiovascular disease and lung cancer, our findings suggest that improved public health conditions could be provided by implementing cordon pricing in the New York City CBD.

The air quality impacts of a metropolitan region's long range transportation plans are generally evaluated by estimating the change in a region's vehicle emission inventory. A change in the overall quantity of vehicle emissions in a region is generally associated with lower concentrations of vehicle related air pollutants and therefore a reduction in public health risks from exposure to vehicle emissions. A major limitation of this common approach is that aggregate emission inventories provide no information about localized impacts. While some areas experience air quality improvements, air quality can become worse in others. Such aggregate analyses are ill suited for addressing contemporary transportation planning questions such as identifying vehicle emission exposure hotspots, quantifying health risks, and evaluating environmental justice concerns. In this paper we describe a computationally efficient analysis framework for evaluating a regional transportation plan with spatially detailed estimates of vehicle emission exposures and related health outcomes. We then apply our framework in a case study of fine particulate matter exposure in the Atlanta, Georgia metropolitan area and the changes expected to occur through the year 2040 under the region's long range transportation plan. We find that exposure and health risks decline from current levels by 2020 but then begin to slowly increase in some areas by 2040. We also find that low income and minority populations have the greatest health risks, confirming environmental justice concerns that have been noted in other regions.

Designing technology for problem-free operation is vital, but equally important is considering how a user may understand or act upon errors and various other 'stuck' situations if and when they occur. Little is currently known about what children think and want for overcoming errors. In this paper we report on design-for-error workshops with children (age 5-10) in which we staged 3 simulated errors with a health assessment technology. In our developmentally-sensitive study, children witnessed the errors via a puppet show and created low-fidelity models of recovery mechanisms using familiar 'play-things'. We found the children were able to grasp the representational nature of the task. Their ideas were playful and inspired by magical thinking. Their work forced us to reflect on and revisit our own design assumptions. The tasks have had a direct impact on the design of the assessment.

This article presents the School Attachment Monitor, a novel interactive system that can reliably administer the Manch- ester Child Attachment Story Task (a standard psychiatric test for the assessment of attachment in children) without the supervision of trained professionals. Attachment prob- lems in children cause significant mental health issues and costs to society which technology has the potential to re- duce. SAM collects, through instrumented doll-play games, enough information to allow a human assessor to manually identify the attachment status of children. Experiments show that the system successfully does this in 87.5% of cases. In addition, the experiments show that an automatic approach based on deep neural networks can map the information collected into the attachment condition of the children. The outcome SAM matches the judgment of expert human asses- sors in 82.8% of cases. This is the first time an automated tool has been successful in measuring attachment. This work has significant implications for psychiatry as it allows profes- sionals to assess many more children cost effectively and to direct healthcare resources more accurately and efficiently to improve mental health.

Autonomous vehicles (AVs) hold great promise for increasing the capacity of existing roadways and intersections, providing more mobility to a wider range of people, and are likely to reduce vehicle crashes. However, AVs are also likely to increase travel demand which could diminish the potential for AVs to reduce congestion and cause emissions of greenhouse gases (GHG) and other air pollutants to increase. Therefore, understanding how AVs will affect travel demand is critical to understanding their potential benefits and impacts. We evaluate how adoption of AVs affects travel demand, congestion and vehicle emissions over several decades using an integrated travel demand, land-use and air quality modeling framework for the Albuquerque, New Mexico metropolitan area. We find that AVs are likely to increase demand and GHG emissions as development patterns shift to the region's periphery and trips become longer. Congestion declines along most roadways as expanded capacity from more efficient AV operation outpaces increasing demand. Most of the population can also expect a reduction in exposure to toxic vehicle emissions. Some locations will experience an increase in air pollution exposure and traffic congestion from changes in land-use and traffic patterns caused by the adoption of AVs.

Transportation projects contain many tradeoffs between environmental, social, and economic benefits and costs that affect different groups of stakeholders, each with different priorities and values. Transportation project sponsors are therefore faced with an incredibly difficult decision making task. Multi-criteria decision analysis (MCDA) provides a flexible framework for considering a wide array of potential impacts that may be used as a supplement of substitute for cost benefit analysis or unstructured decision making. In this study, we evaluate the outcome of two MCDAs, one conducted with input from technical experts and the other with input from a sample of community members for a proposed highway project in Tehran, Iran. We explore how various criteria now commonly considered in urban transportation projects are viewed by these two groups that differ in their technical expertise and values. We find that experts score the project poorly while the community scores it favorably. The results demonstrate that the outcome of seemingly objective analysis tools commonly used in the transportation field depends on who provides critical technical assessments and value judgments and therefore the importance of community involvement.

Many urban areas are perusing infill, transit oriented, and other “smart-growth” strategies to address a range of important regional goals. Denser and more mixed use urban development may increase sustainability and improve public health by reducing vehicle travel and increasing the share of trips made by transit, walking and bicycling. Fewer vehicle trips results in fewer greenhouse gas and toxic vehicle emissions, and more trips made by walking and bicycle increases physical activity. Prior research has largely focused on modeling and estimating the potential size of these and other smart-growth strategy benefits. A largely overlooked area is the potential for unexpected public health costs and environmental justice concerns that may result from increasing density. We evaluate regional land-use and transportation planning scenarios developed for the year 2040 by a metropolitan planning organization with a newly developed regional air quality modeling framework. Our results find that a set of regional plans designed by the MPO to promote smart-growth that are estimated to result in less vehicle use and fewer vehicle emissions than a more typical set of plans results in higher population exposure to toxic vehicle emissions. The smart-growth plans also result in greater income-exposure inequality, raising environmental justice concerns. We conclude that a more spatially detailed regional scale air quality analysis can inform the creation of smarter smart-growth plans.