Search Results (72)

School buses serve as the primary mode of transportation for children traveling to and from school, and their safety measures represent a critical safeguard for children’s lives. Nevertheless, incidents in which children are left unattended on school buses—due to inadequate supervision or the children’s own actions—occur with notable frequency and can lead to fatal outcomes. To mitigate or prevent such tragedies, this paper proposes an in-vehicle thermal imaging solution based on infrared array sensors, integrated with a dedicated algorithm to detect whether a child has been left behind in the school bus. The system collects background temperature, presence temperature, and real-time temperature data inside the bus using infrared array sensors. By comparing the real-time temperature difference against a predefined presence temperature difference threshold, the algorithm determines whether a child is present under the current thermal conditions. It then verifies whether the number of positive detections within a specified temperature range meets a preset presence count threshold, thereby reaching a final decision regarding child presence. Experiments identified optimal parameters: a temperature range of 26–33 °C, a double-difference threshold (ε = 1), and a presence count threshold (P = 4). Random testing demonstrated that the proposed technical solution and algorithm achieve an overall detection success rate of 92.5%. This study develops a low-cost, easily deployable, non-contact thermal imaging method capable of identifying forgotten children on school buses with satisfactory accuracy. By detecting retention before harm occurs, the approach enhances the safety of children traveling by school bus. Full article

The aim of this scoping review is to categorize and examine the relationships between school neighbourhood built environment categories and the physical health of children and adolescents worldwide. The search strategy initially found 8837 studies in four databases (Web of Science, PubMed, SportDiscus and Transportation Research Board) and after applying the inclusion and exclusion criteria 55 articles were included. The findings report on seven school neighbourhood built environment categories: building, connectivity and network, food environment, greenness, land use, safety and other variables. Interestingly, the connectivity and network category comprises 32 variables. Likewise, this category, together with the food environment, shows a clear predominance, with both categories accounting for 71.04% of all significant associations. The greenness category stands out due to its association density similarly to the predominant categories. The physical health categories were body composition, mode of commuting, physical activity, sedentary behaviour and weight status. Complementary weighted cross-tabulation analyses showed that when associations were weighted by participant sample size and school sample size, the food environment–weight status relationship became the most prominent, whereas connectivity-related associations became less dominant. The findings indicate preferential links between school neighbourhood built environment and physical health domains, with the connectivity and network category mainly associated with commuting mode and physical activity, and the food environment was primarily linked to weight status and dietary intake. Consequently, special attention must be given to urban planning and policies in the school neighbourhood built environment. Full article

Improving cyclist safety conditions in the urban context is a key strategy to promote sustainable transport modes and reduce noise and environmental pollution. Recent plans have also addressed this point. In September 2020, the UN General Assembly declared the Decade of Action for Road Safety 2021–2030, aiming to reduce the number of road deaths by at least half. To achieve this task and highlight the risk factor, after collecting and pre-processing cyclist crash data in the city of Rome between 2013 and 2020, Random Forest and Ordered Logistic Regression models are proposed. The crash dataset is also enriched with vehicular speed and flows, and geographical information. A DBSCAN Clustering Analysis is also proposed to identify anomalous areas in the city. The findings show that the presence of cycle paths, the presence of anthropic activities, such as shops, schools, and universities, play a risk mitigation role. Conversely, vehicular speed and heavy vehicles emerge as the main detected risk factors. Finally, spatial analysis indicates that commercial activities reduce cycle path safety due to complex interactions with other road users. Furthermore, historic areas present unique risks driven by pedestrian flows and poor road surfaces, despite low vehicular traffic. Full article

In underdeveloped nations, treatment non-adherence continues to be a significant barrier to effective disease management. It has a major impact on patients and healthcare systems in public primary healthcare settings. Patients who do not take their medications as prescribed may be at higher risk for negative health consequences. Polypharmacy, side-effects, and drug-related problems are factors contributing to non-adherence. Additional patient-related issues include multimorbidity, lack of support, chronic-drugs, and health-literacy. The purpose of this study was to ascertain the prevalence and contributing factors of treatment non-adherence among patients presenting to a public primary healthcare setting in South Africa. Between September and October 2014, cross-sectional quantitative research using structured questionnaires was carried out with one hundred patients who were chosen using random sampling. Self-reports from patients were used to assess non-adherence to therapy. A standardized questionnaire administered by the interviewer was used to gather data, and IBM SPSS version 29 was used for analysis. Patients aged 18 years and older who were using prescribed medications were included. The characteristics of the participants were obtained using descriptive statistics, and 95% confidence intervals (CIs) are reported for Odds ratios (ORs). Associations between related factors and treatment non-adherence were obtained using the Pearson Chi-square test; a p-value of less than 0.05 was deemed statistically significant. Of the 100 patients interviewed, 35% were men and 65% women. The majority were in the age-range of 60–80 years with a high school level of education. Demographic characteristics associated with non-adherence to treatment were gender (p = 0.03) and age (p = 0.03). Chronic conditions, alcohol consumption, recreational drug use, use of medication reminders, waiting time to get treatment and support from healthcare providers all were statistically significant with p-values < 0.001, time to get to the clinic (p = 0.02), mode of transport (p = 0.01), alcohol consumption (OR 22.25 [95% CI: 8.539–57.977], p < 0.001) and recreational drug use (OR 8.73 [95% CI: 5.01–15.98], p < 0.001) were also examined. Patient medication non-adherence is a major medical problem globally. Though patient education is the key to improving compliance, use of compliance aids, proper motivation, and support are also shown to increase medication adherence. Full article

Given that urban mobility arises from the interaction between social and environmental conditions, this study constructs a Bayesian network to represent these relationships in David, Panama, using 500 georeferenced household surveys that recorded variables related to demographics, travel behavior, infrastructure, mobility patterns and perceptions of risk, safety, and vulnerability. The Bayesian network was built and validated through a consensus-driven hybrid procedure combining structural learning and expert knowledge, resulting in a directed acyclic graph (DAG) with 127 nodes and 189 arcs; and conditional probability tables (CPTs) were learned from data. The topology of the network was analyzed with Louvain community detection, revealing eleven subsystems that group household economy and mode choice, hydrometeorological mobility barriers, congestion, public-transport quality, and safety in school travel. The inferences show gender-based differences in the risk of harassment on public transport, higher perceived vulnerability on longer trips, and elevated stress among middle-aged drivers. The model highlights potential priority interventions such as reinforcing public-transport safety, promoting self-contained trips, and encouraging short-distance active mobility, based on population perceptions. The resulting DAG functions as both an analytical and communication tool for urban management, is visually understandable to all stakeholders, and provides unprecedented evidence for Panama in a little-studied context. Full article

This study investigates the factors influencing school trip mode choice among senior high school students in the Chiang Rai urban area, Chiang Rai, Thailand, under normal and adverse weather conditions. Utilizing data from 472 students across six extra-large urban schools, a Multinomial Logit (MNL) regression model was applied to examine the effects of socio-demographic attributes, household vehicle ownership, travel distance, and spatial variables on mode selection. The results revealed notable modal shifts during adverse weather, with motorcycle usage decreasing and private vehicle reliance increasing, while school bus usage remained stable, highlighting its role as a resilient transport option. Car ownership emerged as a strong enabler of modal flexibility, whereas students with limited access to private transport demonstrated reduced adaptability. Additionally, increased waiting and travel times during adverse conditions underscored infrastructure and service vulnerabilities, particularly for mid-distance travelers. The findings suggest an urgent need for transport policies that promote inclusive and climate-resilient mobility systems, particularly in the context of Chiang Rai, including expanded school bus services, improved first-mile connectivity, and enhanced pedestrian infrastructure. This study contributes to the literature by addressing environmental variability in school travel behavior and offers actionable insights for sustainable transport planning in secondary cities and border regions. Full article

This study investigates the factors affecting the choice of school travel mode among students in Mersin, Türkiye, focusing on walking, private car, public transit and school bus. A two-step modeling approach was adopted. First, a latent class cluster analysis (LCCA) was applied to identify subgroups of students with similar characteristics. Then, separate multinomial logit (MNL) models were estimated for each cluster. The data come from the 2022 Urban Transport Master Plan household survey and include 2798 students from 2092 households. The results show that trip distance is the most consistent and significant factor across all clusters, as increasing distance makes students more likely to use motorized modes instead of walking. Gender also demonstrates a consistent influence in specific clusters, where male students are less likely to travel by private car. Similarly, residing in a single-family house consistently increases the likelihood of car use in multiple clusters. Conversely, the influence of household structure, parental education, income, and household size differs significantly between clusters, underlining the importance of considering group-level differences in school travel behavior. These findings suggest that policies aiming to promote sustainable school travel should be sensitive to the needs of different student groups. Integrating land use and transportation planning may help to support active and shared modes of travel. Full article

This paper empirically analyzes the factors influencing the choice of transportation mode of commuters (especially married women), as well as the influence of accessibility to educational facilities on the choice of transportation mode of parents with school-age children. The results of this study suggest that improving public transportation and accessibility to educational facilities in the region will be important in relation to increasing public transportation usage. The longer it takes for students in a given area to commute to school using public transportation compared to driving, the more likely it is that parents will drive their children to or from school. An 8.4 min increase in elementary school students’ commuting by public transit leads to a decrease in the probability for their parents to choose public transit in their commuting by 55 percentage points. In addition, when the students are younger, the probability of using public transportation is lower. Married females’ education level and labor force participation have been increasing in Korea. Thus, their opportunity cost of driving their children to or from school rises and accessibility to educational facilities by public transit is more important than before. Full article

The motorization of school commutes reduces the physical activity of children and causes a series of urban traffic and social problems, such as traffic congestion in school districts and parents becoming necessary for transportation. To alleviate traffic jams and related social problems, as well as to encourage physical activity amongst students, we advocate non-motorized travel modes for students, such as walking and cycling. Based on a case study of the Wuhan East Lake High-Tech Development Zone, we use a multiple linear regression model to analyze the relationship between influence factors and student travel mode choices. The results show that built environment factors (the built environment factors are divided into density, diversity, accessibility, and destination) have a significant impact on school travel mode choices, especially accessibility and diversity. Furthermore, the study highlights the pivotal role of travel perceptions, particularly perceptions of safety, comfort, and convenience. Through a questionnaire survey, we collect students’ travel perceptions and their actual school travel modes, which offer valuable insights for urban planners and policymakers. The findings indicate the complex interplay between student commuting and the built environment. Additionally, these findings can be valuable, both in academia and for policymakers. We provide strategies that could be beneficial for reducing motor vehicle activities (especially driving). Full article

Education is crucial for leading a productive life and obtaining necessary resources. Higher education institutions are progressively incorporating artificial intelligence into conventional teaching methods as a result of innovations in technology. As a high academic record raises a university’s ranking and increases student career chances, predicting learning success has been a central focus in education. Both performance analysis and providing high-quality instruction are challenges faced by modern schools. Maintaining high academic standards, juggling life and academics, and adjusting to technology are problems that students must overcome. In this study, we present a comprehensive dataset, SAPEx-D (Student Academic Performance Exploration), designed to predict student performance, encompassing a wide array of personal, familial, academic, and behavioral factors. Our data collection effort at Air University, Islamabad, Pakistan, involved both online and paper questionnaires completed by students across multiple departments, ensuring diverse representation. After meticulous preprocessing to remove duplicates and entries with significant missing values, we retained 494 valid responses. The dataset includes detailed attributes such as demographic information, parental education and occupation, study habits, reading frequencies, and transportation modes. To facilitate robust analysis, we encoded ordinal attributes using label encoding and nominal attributes using one-hot encoding, expanding our dataset from 38 to 88 attributes. Feature scaling was performed to standardize the range and distribution of data, using a normalization technique. Our analysis revealed that factors such as degree major, parental education, reading frequency, and scholarship type significantly influence student performance. The machine learning models applied to this dataset, including Gradient Boosting and Random Forest, demonstrated high accuracy and robustness, underscoring the dataset’s potential for insightful academic performance prediction. In terms of model performance, Gradient Boosting achieved an accuracy of 68.7% and an F1-score of 68% for the eight-class classification task. For the three-class classification, Random Forest outperformed other models, reaching an accuracy of 80.8% and an F1-score of 78%. These findings highlight the importance of comprehensive data in understanding and predicting academic outcomes, paving the way for more personalized and effective educational strategies. Full article

The transportation choices of children commuting to school significantly impact their quality of life and safety. This study focuses on the unique aspect of water transportation, which is prevalent in various regions worldwide. Through a comprehensive analysis of 1000 research papers using Publish or Perish and VOSViewer, we explore the critical factors influencing children’s preferences and experiences with water transport. The findings highlight that understanding these preferences is essential for developing safer and more efficient modes of water-based transportation. By addressing the specific challenges school-aged children face, this research aims to inform the design and implementation of improved water transport solutions, ultimately enhancing their well-being and security. This study underscores the importance of tailored transportation strategies in creating a safer and more supportive environment for children navigating their daily commutes. Full article

Since adolescents are the adults of tomorrow, they are key players in increasing climate change awareness and the adoption of environmentally friendly transportation. Therefore, it is essential to understand the current and future transportation choices of adolescents in order to provide sustainability guidance to schools and communities. To this aim, a questionnaire based on the Theory of Planned Behavior was administered at school to 382 Brazilian students with an average age of 16.36 years. Regarding the results on current transportation choices, students attending public schools commute more often without adult supervision than those attending private schools and tend to travel in a more environmentally friendly manner. In terms of future transportation choices, attitudes, subjective norms, and active participation in environmental groups have a significant impact on adolescents’ intentions to choose a more sustainable mode of transport to get to work in 15 years’ time. Taking into account the socio-economic level, the results are discussed in terms of the need for awareness raising, such as education for sustainable development, and possible interventions to encourage more environmentally friendly transport choices. Full article

The influence of mobility modes within Positive Energy Districts (PEDs) has gained limited attention, despite their crucial role in reducing energy consumption and greenhouse gas emissions. Buildings in the European Union (EU) account for 40% of energy consumption and 36% of greenhouse gas emissions. In comparison, transport contributes 28% of energy use and 25% of emissions, with road transport responsible for 72% of these emissions. This study aims to design and optimize a synthetic PED in Istanbul that integrates renewable energy sources and public mobility systems to address these challenges. The renewable energy sources integrated into the synthetic PED model include solar energy, hydrogen energy, and regenerative braking energy from a tram system. Solar panels provided a substantial portion of the energy, while hydrogen energy contributed to additional electricity generation. Regenerative braking energy from the tram system was also utilized to further optimize energy production within the district. This system powers a middle school, 10 houses, a supermarket, and the tram itself. Optimization techniques, including Linear Programming (LP) for economic purposes and the Weighted Sum Method (WSM) for environmental goals, were applied to balance cost and CO₂ emissions. The LP method identified that the PED model can achieve cost competitiveness with conventional energy grids when hydrogen costs are below $93.16/MWh. Meanwhile, the WSM approach demonstrated that achieving a minimal CO₂ emission level of 5.74 tons requires hydrogen costs to be $32.55/MWh or lower. Compared to a conventional grid producing 97 tons of CO₂ annually, the PED model achieved reductions of up to 91.26 tons. This study contributes to the ongoing discourse on sustainable urban energy systems by addressing key research gaps related to the integration of mobility modes within PEDs and offering insights into the optimization of renewable energy sources for reducing emissions and energy consumption. Full article

The scientific literature commonly states that walking to and from school can provide a regular source of physical activity for children and young people. Transport planners emphasise the importance of interventions in the road network to improve walking practices among this younger population. The Walking School Bus is an initiative that can have a positive impact on traffic management while simultaneously promoting physical health. It is a transportation service used by elementary school students to walk the home–school route accompanied by parent volunteers. It is a similar mode to the school bus in which there are several lines, each consisting of several stops, and each line is associated with a predetermined route that must go through all the stops and then arrive at the school. This paper aims to propose and implement a microsimulation traffic flow model leading to a local solution. The study has two primary purposes: (1) to evaluate the impact of Walking School Bus demands on traffic performance and (2) to examine the effect of different routes on traffic flow delay. Finally, after the implementation, the model will be applied to analyse the situation of an existing school, which will consider this mode of transport. Full article

The transition to sustainable mobility is a recognized socio-economic and environmental challenge, particularly among young adults. In addressing the gap in the literature on young adults’ travel behaviors, especially in Cameroon, this paper investigates the transport mode choices, influencing factors, and barriers to sustainable mobility of students at the National Advanced School of Public Works, Yaoundé (NASPW). Data were collected through online questionnaires with 360 valid responses. Findings revealed that most students used multiple modes of transport for commuting, with moto-taxis being the most common. Accessibility, vehicle speed, and flexibility appeared as the most important reasons for the preferred transport modes, while driver’s license possession, safety perceptions, speed, and proximity were significant predictors for mode choice. Demographic factors were found to influence transport preferences, with distinct clusters prioritizing different aspects. Barriers to public transport were primarily long waiting times and congestion, while active mobility was hindered by distance, infrastructure, and weather. The usage of public transportation was encouraged by its affordability and reduced travel time, whilst active options were preferred due to cost savings and health benefits. To promote sustainable mobility for campus travel, it is crucial to encourage active modes, develop mass transport systems, and raise awareness through symposia and conferences among students and staff. Full article