Impact of School Psychological Environment Variables on Happiness of Korean Youths

Abstract: This study analyzed the structural relationships between school‐related psychological variables (teacher support, learning flow, friendship) and the happiness of Korean youths, and compared structural equation models for middle and high schools. Questionnaire data were collected from students in middle and high schools in 10 cities and provinces of Korea. The final data set from 870 students was analyzed with Amos 20.0. Goodness of fit indices for the hypothetical model were good, and all paths were significant. That is, a model incorporating indirect and direct impact of school‐related psychological environment variables, such as teacher support, learning flow, and friendship, on the happiness of Korean youths is valid. Second, structural equation models for middle and high schools demonstrated significant differences. Therefore, different types of assistance are needed to increase the happiness of students in middle schools and high schools.     

A home-based approach to understanding the effect of spatial autocorrelation on seat belt non-use

Roadside observations indicate that seat belt use rates are often spatially correlated with nearby areas. However, very few studies have examined the effects of spatial autocorrelation on seat belt use. This study used exploratory spatial data analysis (ESDA) to explore spatial autocorrelation in Tennessee, which has a lower seat belt use than the United States national average. We geocoded home-addresses of vehicle occupants involved in traffic crashes between 2014 and 16 (n = 1,251,901) and projected them to the census tract corresponding to their home address. This projection reveals information about the spatial distribution of seat belt non-use and socioeconomics of the areas surrounding the crash victim's home. The presence of highly spatially correlated observations (i.e., a significant positive Moran’s I) suggests that seat belt non-use is not produced solely by the internal structural factors represented in the non-spatial models. ESDA reveals a distinctive regional imprint for spatial autocorrelation, in which Southern-metropolitan areas’ (Southern-MPOs) in Tennessee census tracts have higher than average seat belt non-use compared to Non-Southern-MPOs (16% vs. 9%). The spatial error model was suitable for Non-Southern-MPOs, whereas the spatial lag model was more suitable for Southern MPOs. Comparison of the estimated models indicates that in the Non-Southern MPOs, percentage of the White population, percentage of the population with Bachelor's degree, median household income, vehicle ownership, and population density are significant predictors of seat belt non-use. On the other hand, median household income, vehicle ownership, and percentage of population aged between 16 and 42 years old predict seat belt non-use in Southern MPOs. The study results could be used to identify seat belt non-use clusters at the state level and identify seat belt non-use hot zones. Furthermore, this analysis indicates that the relationship between demographic variables and seat belt non-use varies across regimes. Failing to consider the spatial regimes in the analysis would lead to falsely prioritizing groups prone to seat belt non-use.     

Consequences of autonomous vehicles: Ambivalent expectations and their impact on acceptance

Connected and autonomous vehicles (CAVs) are within reach of widespread deployment on public roads, but public perceptions are ambivalent. The objective of the present research was to assess expectations about the consequences of CAV introduction. These expectations should explain CAV acceptance, but their relative importance is poorly understood. We conducted a survey with a representatively drawn panel sample (N = 529) from France, Germany, Italy, and the UK. The survey consisted of a large item pool of expected consequences from CAV introduction, as well as general and affective evaluation of CAVs, ease of use, and behavioral intention to use CAVs. Exploratory factor analysis revealed four facets of expected consequences: road safety, privacy, efficiency and ecological sustainability. On average, expectations were mostly positive for ecological sustainability and safety, but negative for privacy. At the same time, substantial variance existed between respondents and between countries. For safety and efficiency, improvement was expected by a third of respondents, while another third expected worsening. Respondents from Italy expected more positive consequences for safety, while respondents from both France and Germany expected more negative consequences for privacy. To different degrees, all four facets predicted the intention to use CAVs in a structural equation model, primarily via affective evaluations. For policy makers, manufacturers, and service providers, understanding the trade-offs inherent to different CAV solutions will be central to ensure citizens’ needs are respected.     

Analysis of advanced driver assistance systems in police vehicles: A survey study

Few studies have examined the impact of advanced driver-assistance systems (ADAS) on police officers to improve driver safety and prevent crashes. This is in spite of police officers having higher driving-related mortality rates than average civilians. To fill this gap, a survey study was conducted on 73 police officers to assess their opinions on various ADAS features as well as their recommendations for improvement. Results of the correlation analyses indicated that officer behavior and opinion on ADAS features were influenced by the trust officers had in the available ADAS systems among other key factors such as ADAS training and perceived usefulness. On this basis, guidelines for future research and development of ADAS were provided to improve officer driving safety in police operations. The guidelines need to be further validated in future driving simulation or naturalistic studies.     

Modeling the influence of mobile phone use distraction on pedestrian reaction times to green signals: A multilevel mixed-effects parametric survival model

Understanding and modeling the influence of mobile phone use on pedestrian behaviour is important for several safety and performance evaluations. Mobile phone use affects pedestrian perception of the surrounding traffic environment and reduces situation awareness. This study investigates the effect of distraction due to mobile phone use (i.e., visual and auditory) on pedestrian reaction time to the pedestrian signal. Traffic video data was collected from four crosswalks in Canada and China. A multilevel mixed-effects accelerated failure time (AFT) approach is used to model pedestrian reaction times, with random intercepts capturing the clustered-specific (countries) heterogeneity. Potential reaction time influencing factors were investigated, including pedestrian demographic attributes, distraction characteristics, and environment-related parameters. Results show that pedestrian reaction times were longer in Canada than in China under the non-distraction and distraction conditions. The auditory and visual distractions increase pedestrian reaction time by 67% and 50% on average, respectively. Pedestrian reactions were slower at road segment crosswalks compared to intersection crosswalks, at higher distraction durations, and for males aged over 40 compared to other pedestrians. Moreover, pedestrian reactions were faster at higher traffic awareness levels.     

Factors affecting motorcyclists’ behavior in car-following condition

Car-following (CF) maneuver plays an important role in both traffic flow analysis and road safety assessment. However, many studies on this issue have focused on the drivers’ behaviour, neglecting the different driving behaviour of the motorcycle riders and the vulnerability of this type of user. The aim of the present paper is to analyse the factors affecting the riders’ behaviour in CF condition, by using two surrogate safety indicators, Time Headway (TH) and Time To Collision (TTC). A field experiment was conducted to collect data. Twenty young motorcyclists participated in the study driving their own motorcycles, which were instrumented with a camera and a global positioning system device (GPS). A digital image processing algorithm was used to obtain CF related indicators. The results of the study showed that Time Headway (TH) and Time To Collision (TTC) are independent of each other, extending the result obtained in the literature for the four wheeled vehicles to the motorcycles. TTC and TH were therefore analyzed separately. As for TTC, it was observed that only in few cases (0.8%) the riders were found in risky condition (TTC < 5 s), confirming the role of this parameter for the detection of an impending risk. On the other hand, TH gives information about a potentially risky situation, and 99% of the riders were found in such condition (TH < 2 s). Due to the few cases in TTC risky conditions, only TH was further modelled by using the hazard-based duration analysis. Accelerated Failure Time (AFT) model specification was used, and the Weibull distribution provided the best fit of the data. The model identified four covariates affecting TH and all of them had the effect of reducing TH, increasing the collision risk. The time spent on CF condition, the speed of the motorcycle, the willingness of overtaking and the engine size of the motorcycle were the factors that reduced TH.The findings of this study could be considered in the development of microsimulation models; the factors that were found affecting the motorcyclists’ behavior in car-following condition could be considered for a more realistic simulation of the riders’ choice of Time Headway.     

Factors that influence the acceptance of future shared automated vehicles – A focus group study with United Kingdom drivers

The development of Shared Automated Vehicles (SAVs) is well underway to provide mobility as a service (MaaS) and bring benefits such as reduced traffic congestion, reduced reliance on privately owned vehicles and increased independence to non-drivers. To realise the benefits of SAVs, adoption by private vehicle users is crucial. Previous research has shown this subset of users as the least likely to adopt SAVs, and it is not well understood what factors are important to achieve such adoption. The purpose of this study is to obtain an in-depth understanding of attitudes, perceptions and preferences that influence the acceptance of future SAVs for drivers. This paper presents the results from an online asynchronous focus group study with 21 British drivers as participants. From the analysis, Service Quality, Trust and Price Value emerged as the three most prominent factors to understand user acceptance of SAVs. These three main factors may be of prime importance for convincing naïve private car owners to accept high-speed SAVs. Productive use of travel time has been frequently mentioned in previous research as a benefit of vehicle automation but was scarcely mentioned by participants in this study. Shared Space Quality in introduced as an indicator for Service Quality, together with Security and Trusting Co-passengers as two indicators of Trust. Based on the findings, this paper concludes with a conceptual SAV technology acceptance model is introduced, with the results added as extended model predictors to the Unified Theory of Acceptance and Use of Technology (UTAUT2).     

Toward integrating cognitive components with computational models of emotion using software design patterns

Computational models of emotion (CMEs) are software systems designed to imitate particular aspects of human emotions. The main purpose of this type of computational model is to capture the complexity of the human emotion process in a software system that is incorporated into a cognitive agent architecture. However, creating a CME that closely imitates the actual functioning of emotions demands to address some challenges such as (i) sharing information among independently developed cognitive and affective components, and (ii) interconnecting complex cognitive and affective components that must interact with one another in order to generate realistic emotions, which may even affect agents’ decision making. This paper proposes an architectural pattern aimed at cataloging and describing fundamental components of CMEs and their interrelationships with cognitive components. In this architectural pattern, external cognitive components and internal affective components of CMEs do not interact directly but are extended by including message exchange methods in order to use a publish-subscribe channel, which enables their intercommunication, thus attenuating issues such as software heterogeneity. This structural approach centralizes communication management and separates the inherent complexity of the cognitive and affective processes from the complexity of their interaction mechanisms. In so doing, it enables the design of CMEs’ architectures composed of independently developed affective and cognitive components. The proposed architectural pattern attempts to make progress in capturing the complex process of human emotions in a software system that adheres to software engineering best practices and that incorporates quality attributes such as flexibility and interoperability.     

An adaptive network model for pain and pleasure through spicy food and its desensitization

This paper aims to map out the adaptive causal pathways of processes underlying capsaicin consumption and the desensitization process of the TRPV1 receptor as a feedback loop together with pain and pleasure perception. In order to map out these causal capsaicin pathways, adaptive causal network modeling was applied, which is a way of modeling biological, neural, mental and social processes from an adaptive causal modeling perspective.     

On the dynamics and adaptivity of mental processes: Relating adaptive dynamical systems and self-modeling network models by mathematical analysis

In this paper, it is addressed by mathematical analysis how network-oriented modeling relates to the dynamical systems perspective on mental processes. It has been mathematically proven that any dynamical system can be modeled as a temporal-causal network model and that any adaptive dynamical system (of any order) can be modeled by a self-modeling network (of the same order).