Safe Road Research Articles

An investigation into the role of driver characteristics in driving behaviours and subsequent clustering of Indian bus drivers

Objectives Aberrant driving behaviours among bus drivers cause serious concerns to safety on the road. The present study aimed to measure the effects of driver characteristics on aberrant and positive driving behaviours among bus drivers and to group drivers accordingly. The study was carried out among bus drivers of the government-owned road transport corporation in Kerala, India. Methods A total of 1120 drivers took part in the survey, out of which 617 drivers returned the forms with all questions answered (response rate = 55.1%). SPSS version 27.0 was used for analysis. Results Structural equation modelling proved the predictive nature of driver characteristics on driving behaviours. K-means cluster analysis revealed clusters such as violating and angry drivers (cluster 1), knowledgeable drivers with positive driving behaviours (cluster 2), healthy and stress-free drivers (cluster 3), erroneous and stressed drivers (cluster 4) and calm drivers with positive driving behaviours (cluster 5). Conclusion Positive relationships of anger and stress on aberrant driving behaviours and higher number of drivers in clusters 1 and 4 reveal concerns of safety. The study points towards the necessity of organizational commitment for improving driver characteristics for better driver behaviours and safer roads.

Assessing public trust in road traffic injuries prevention policies in Iran: a cross-sectional study.

Road Traffic Injuries (RTIs) represent the predominant cause of morbidity and mortality globally. Thus, impactful interventions and trustworthy authorities are imperative to mitigate traffic accidents. The present study seeks to assess public trust in traffic accident diminution policies and measures within the country of Iran. This cross-sectional study utilized a questionnaire comprising 31 questions distributed across eight dimensions. The questionnaire's design was informed by a literature review and semi-structured interviews with experts specializing in traffic accidents. The target population consisted of Tabriz city residents, who were selected via convenience sampling. The collected data were then analyzed using SPSS-16 software, employing the T-test, One-way ANOVA, and Pearson correlation coefficient to present the study's results. structural equation modeling (SEM) was conducted using R 4-4-2 programming language. A total of 681 participants completed the questionnaire, yielding a commendable response rate of 88.6%. The majority of participants were male (60.8%), and 86.2% identified as drivers, with 61.8% having experienced driving accidents. The overall score for public trust in RTIs policies was calculated to be 46.9%. The individual scores for specific domains of trust in traffic safety were as follows: safer roads (43.1%), safe vehicle (43.3%), safety laws (48.8%), safe user (51.1%), safe technology (51.5%), road safety management (46.8%). All the latent variables except education are significantly effective on public trust. The study findings indicate relatively low levels of public trust in RTIs policies among the Iranian population. These results emphasize the need for targeted interventions to enhance public confidence in specific aspects of traffic safety. Policymakers can use these insights to implement effective measures, thus contributing to the RTIs and the promotion of road safety in the country.

How do complete streets policies and legislative ordinances implement explicit wording regarding low-income populations and communities of color? A qualitative analysis

OverviewActive transportation, walking/bicycling for transportation, provides physical/mental health benefits to those who participate; however, there are inequities in participation among various populations, which creates health disparities among groups (racial/ethnic minorities, LGBTQ+ communities, low-income, disabled/older individuals, youth/children, women). Complete Streets policies and legislative ordinances aim to ‘create useable streets for all’ in various cities across the United States; however, previous studies showed a lack of wording surrounding low-income populations and communities of color within these specific city-level literatures. The purpose of this current study was to qualitatively examine city-level policies/legislative ordinances addressed low-income populations and communities of color using thematic analysis. MethodsComplete Streets plans which had mentioned low-income populations and communities of color were obtained from a previous study and a public database. Thematic qualitative analysis was conducted to identify common themes. ResultsComplete Streets initiatives (n = 57) were analyzed with various themes identified, including actionable strategies for implementation, identification of funding mechanisms, and others. DiscussionComplete Streets policies and legislative ordinancesguide key stakeholders to creating safe and accessible roads for populations of different demographics and traveling in different transportation modes. Underserved and under resourced populations need be included within policy/legislation literature to ensure equitable engagement within city-level Complete Streets plans.

Design and Development of a Real-Time Monitoring System for Highway Road Surface Anomalies

The recent unprecedented increase in highway networks in India has increased the need to maintain safe and smooth roads. The increasing number of road surface anomalies such as cracks, surface irregularities, and anomalies can lead to vehicle damage and accidents. This research work presents the design and development of a real-time monitoring system for detecting anomalies in highway road surfaces. The proposed system utilizes the machine vision model integrated with cameras, sensors, and edge computing to provide timely and accurate alerts for the drivers and also to the road maintenance authorities. The proposed solution is designed specially to work under different environmental conditions and enable large-scale deployment. The output generated from the proposed model is visual feedback of the detected anomalies and its severity analysis, enabling quick road maintenance actions.

Establishing transoral thyroid and parathyroid endoscopic vestibular approach (toetva-toepva) service in the united kingdom national health system: the safe road to success

Establishing transoral thyroid and parathyroid endoscopic vestibular approach (toetva-toepva) service in the united kingdom national health system: the safe road to success

»Relationships are Key« A Semantic Relationship Awareness Framework for Operational Technology Monitoring

Critical infrastructures in areas like road traffic management naturally rely on the broad use of "Operational Technology (OT)" to ensure efficient and safe road traffic monitoring (RTM) through "OT objects", like sensors and actuators, whereby monitoring OT itself ("OTM") is evenly crucial. OTM is highly challenging, not least due to massive heterogeneity of OT, immense complexity and size, and omnipresence of evolution. As a consequence, knowledge about interdependencies between OT objects in form of semantic relationships is often outdated or simply not available. Thus, in case of incidents, detection of cause and effect in the sense of a situational picture of OT is missing. In order to counteract this fundamental deficiency, we aim to automatically discover semantic relationships between OT objects, to build up an ontological knowledge base as prerequisite for achieving OT situation awareness. Thereby, the contribution of this paper is threefold. First, a systematic exploration of the induced challenges is provided, derived from an in-depth analysis of real-world OT message logs in the area of RTM. Based on that, we sketch out a research roadmap, thereby guiding the identification of existing concepts and technologies appearing to be useful for realizing a framework for semantic relationship awareness, being the crucial pre-step for achieving OT situation awareness. Finally, a first proof-of-concept prototype is put forward, complemented by an evaluation of its applicability and a detailed comparison to related approaches.

Toward Safer Roads: Predicting the Severity of Traffic Accidents in Montreal Using Machine Learning

Toward Safer Roads: Predicting the Severity of Traffic Accidents in Montreal Using Machine Learning

Editorial: Responsibility for safe roads

Editorial: Responsibility for safe roads

Features of the structure and properties of stone mastic asphalt

AbstractThe purpose of this study is to investigate the features and properties of stone mastic asphalt in the context of its potential for practical application in road repair works, aiming to create a high‐quality road surface and replace outdated structural materials with newer alternatives. The leading approach combines a systematic analysis of the issues submitted for consideration and an experimental study of the properties of stone mastic asphalt, which are important in the context of the practical use of this building material to ensure high quality parameters of the roadway and the timeliness of its repair, if necessary. Within the framework of this study, results were obtained that indicate a significant variety of approaches to determining the characteristics and properties of the material under consideration, clearly demonstrating current solutions to the problem of improving the performance of stone mastic asphalt by introducing special admixtures into its composition. The findings and conclusions of this research are valuable for road service employees and developers of modern construction materials, aiding in high‐quality road surface repair and creating durable asphalt for safe road use in the future.

Predicting Number of Vehicles Involved in Rural Crashes Using Learning Vector Quantization Algorithm

Roads represent very important infrastructure and play a significant role in economic, cultural, and social growth. Therefore, there is a critical need for many researchers to model crash injury severity in order to study how safe roads are. When measuring the cost of crashes, the severity of the crash is a critical criterion, and it is classified into various categories. The number of vehicles involved in the crash (NVIC) is a crucial factor in all of these categories. For this purpose, this research examines road safety and provides a prediction model for the number of vehicles involved in a crash. Specifically, learning vector quantization (LVQ 2.1), one of the sub-branches of artificial neural networks (ANNs), is used to build a classification model. The novelty of this study demonstrates LVQ 2.1’s efficacy in categorizing accident data and its ability to improve road safety strategies. The LVQ 2.1 algorithm is particularly suitable for classification tasks and works by adjusting prototype vectors to improve the classification performance. The research emphasizes how urgently better prediction algorithms are needed to handle issues related to road safety. In this study, a dataset of 564 crash records from rural roads in Calabria between 2017 and 2048, a region in southern Italy, was utilized. The study analyzed several key parameters, including daylight, the crash type, day of the week, location, speed limit, average speed, and annual average daily traffic, as input variables to predict the number of vehicles involved in rural crashes. The findings revealed that the “crash type” parameter had the most significant impact, whereas “location” had the least significant impact on the occurrence of rural crashes in the investigated areas.

Assessment of traffic sign retroreflectivity for autonomous vehicles: a comparison between handheld retroreflectometer and LiDAR data

This study investigates the critical role of retroreflectivity in traffic signs, particularly in the context of autonomous vehicles (AVs), where accurate detection is paramount for road safety. Retroreflectivity, influencing visibility and legibility, is essential for ensuring safe road conditions. The study aims to assess traffic sign retroreflectivity using handheld retroreflectometers and LiDAR data, offering a comprehensive comparison of results with a specific focus on the RA1 and RA2 traffic sign classes. In a real-world setting, an AV equipped with LiDAR sensors, GPS units, and a stereo camera collects data on traffic signs, including point cloud attributes such as intensity and density. Simultaneously, a handheld retroreflectometer measures retroreflectivity coefficients from identified traffic signs. While retroreflectometers provide precision, they face limitations regarding time-consuming measurements and handling large or elevated signs. In contrast, LiDAR systems efficiently evaluate retroreflective features for numerous signs without such constraints. Despite both methods consistently yielding accurate retroreflectivity, the study reveals a limited correlation between LiDAR point cloud data and handheld retroreflectivity coefficients. The implications of these findings are significant, particularly in the selection and maintenance of retroreflective materials in traffic signs, with direct repercussions on overall road safety. The results offer valuable insights into leveraging LiDAR technology to enhance AVs' detection capabilities. Recommendations for further research include exploring factors influencing LiDAR intensity, establishing a more accurate relationship between intensity and retroreflectivity, correcting the point cloud during intensity calibration, and testing empirical prediction models with a larger sample size. These endeavors aim to generate a robust regression graph and determine correlation coefficients, providing a more nuanced understanding of the intricate relationship between LiDAR data and handheld retroreflectivity coefficients in the context of traffic sign assessment.

Feeling unwell of passenger travel by small vehicles and associated risk factors in the North Shewa Zone, Oromiya, Ethiopia

The current study investigated how and why sociocultural structures, situational conditions, and personal behavioural factors cause passengers to feel ill when travelling by minibuses, drawing on ideas from the social construction theory of illness. A significant objective was to investigate associated risk variables that influence passengers’ feelings of illness related to the social environment, addressing their beliefs, meanings, practices, and behaviours. A survey method was used to obtain data from 384 passengers for the study. The results of logistic regression indicated that feeling ill when travelling by minibuses differed from passenger to passenger; then, they had their own set of practical and emotional challenges that had no known medical reason. Compared with male and older passengers, female and younger passengers were more likely to feel ill. Furthermore, stress and role-set effects increased passengers’ experiences of feeling ill more than did passengers who had no stress prior to the trip and who had only one role. Additionally, passengers who travelled intermittently, utilized suppression techniques to lessen travel discomfort, and fastened seat belts were less likely to experience symptoms of illness. Passengers who travelled on unsafe roads and used alcohol before travel, on the other hand, were more likely to feel ill than those who travelled on safer roads and did not use alcohol before the trip. The findings suggest that passengers should be aware of predisposing conditions that result in illness, be able to rest before travelling, and use all suppressive methods to reduce or prevent illness while travelling by small buses.

A Critical Analysis of Safe Abortion Road Map in Nepal.

Abortion was legalized by the 2002 Muluki Ain to combat the surging rates of maternal mortality and morbidity. By 2021, the Maternal Mortality Rate plummeted to 151 from 539 in 1996. The decline in the abortion-related maternal mortality attributes to the implication of progressive abortion policies that includes expanded safe abortion services introduction of medical abortion, constitutional recognition of abortion, the mandates by Safe Motherhood and Reproductive Health Rights Act, and free-of-cost abortion services in government health facilities. This review study delves into exploring the contemporary abortion policies and its implications on women's access to safe abortion services as well as the factors that affect the access. This study incorporates findings from extensive desk review of abortion services in Nepal. The 2021 safe abortion services Program Implementation Guideline aims to expand safe abortion sites; however, the Nepal's challenging geography ensues its inequitable distribution, especially in mountainous area. Policy provisions on information and financial accessibility to abortion are well navigated by the Safe Motherhood and Reproductive Health Rights Act and regulation but consistent to sporadic gaps in its implementation were comprehended in this study. This paper further discussed the Safe Motherhood and Reproductive Health Rights Act's regressive mandate of 28-week gestational limit at any condition and the role of gender in abortion decision-making under the pretext of factors influencing safe abortion services. The review study recommends strategies: improving capacity for abortion services under federalism, combating stigma, improving the private sector's readiness, and building a resilient health system.

Towards Safer Streets: A Comprehensive Approach to Safeguarding Lives at Surajpur\'s Roundabouts in Greater Noida

Towards Safer Streets: A Comprehensive Approach to Safeguarding Lives at Surajpur\'s Roundabouts in Greater Noida

Birth preparedness and pregnancy complication readiness and associated factors among pregnant women in Ethiopia: A multilevel analysis.

Maternal and child deaths occur during pregnancy and delivery. Timely information on signs of pregnancy complications and ways to plan for normal birth is a strategy to reduce maternal and child deaths. The purpose of this study was to assess birth preparedness, and pregnancy complications readiness and identify associated factors in Ethiopia. A cross-sectional study design was used. A total of 1635 weighted samples of pregnant women were included for analysis from the 2016 Ethiopian demographic and health survey data set. Multilevel mixed-effect logistic regression was used to estimate the effects of potential variables on birth preparedness and complication readiness. STATA version 15 software was used for data processing and analysis. A variable with a p-value < 0.05 with a 95% confidence interval was considered a significant factor. Pregnant women were informed about convulsions (8.02%), fever (35.95%), abdominal pain (28.92%), leaking fluid from the vagina (28.21%), and blurred vision (17.98%). Pregnant women prepared for supplies needed for birth (38.70%), transportation (20.04%), money (18.97%), people's support for birth (5.03%), and blood donors (3.11%). Only 56% and 44.91% of pregnant women had good birth preparedness and were informed about pregnancy complications respectively. Educational status, antenatal care visits, and region were significant factors associated with birth preparedness and complication readiness. Distance to health facility and residency were significantly associated with birth and complication readiness, respectively. Birth preparedness and complication readiness among pregnant women were low in Ethiopia. Empowering women with education, installing safe roads, building accessible health facilities, and emphasizing pregnancy complications and birth preparedness plans during antenatal care visits are important interventions to enhance birth preparedness and pregnancy complication readiness.

Towards Safer Roads: A Deep Learning Approach to Driver Distraction Detection in Four-wheeler Cars

The fact that there are more than 50 million cars sold annually and more than 1.3 million fatal motor vehicle accidents each year indicates the urgent need for stronger road safety regulations. Driver behaviour needs to be addressed, especially in emerging nations like India, which is responsible for 11% of all road fatalities worldwide. Diversion has been identified as the leading cause of 78% of accidents involving drivers. Distractions can take many different forms, from using a phone to interacting with others, and they greatly hinder road safety. This work aims to address this important problem by creating a highly effective machine learning (ML) model that employs computer vision techniques to classify various driver distractions in real-time. Utilising cutting-edge models, such as an ensemble of CNNs and convolutional neural networks (CNNs) like ResNet50. Our goal is to effectively identify and categorise distractions through the use of deep learning and picture recognition, allowing for proactive intervention to avert mishaps. Beyond classification accuracy, this study evaluates the model’s overall speed and scalability, which are critical for deployment on edge devices. We assess the practical practicality and wide-spread adoption of our approach by analysing performance parameters like inference time and resource utilisation

Prediction of the Skid Resistance of Recycled Hot Mix Asphalt Mixture

Typically, the skid resistance of pavement surfaces is used to indicate road safety levels. The initial value and long-term behavior of skid resistance can be reportedly determined by modulating asphalt mixture properties during their mix design process. In this study, a skid-resistance prediction equation for recycled asphalt concrete was formulated based on ANOVA test results. An accelerated polishing device was used to replicate the abrasion encountered by road surfaces owing to vehicle–tire interactions for characterizing the skid resistance of recycled asphalt concrete. Notably, key parameters, including initial and terminal values and reduction rate of skid resistance induced by vehicle-related abrasion, were determined. These findings suggest that the initial values and reduction rate of skid resistance can be estimated from the mixed binder properties and initial MTD value. However, the final/terminal skid resistance predicted using aggregate gradations has a limited degree of confidence. To further improve the accuracy of the prediction equation, additional data collection and validation tests are required. In addition, field tests using recycled asphalt concrete are necessary to refine the accuracy and precision of the prediction equations for practical road construction scenarios. This study represents a significant step toward the development of safe and effective road infrastructure.

Technological processes for ensuring road safety on the roads of St. Petersburg and the Leningrad region

Background. The main objective of the implementation of the National Project “Safe high-quality roads” of the Russian Federation is to improve the safety and quality of highways. This study provides a justification for the methods of installing longitudinal noise strips and an assessment of their impact on overall road safety. The results of this study can be used to develop effective recommendations for integrating these measures into the processes of designing and building highways.&#x0D; Aim. The purpose of this study is to select and substantiate the most rational way of installing longitudinal noise strips on highways. This study analyzes the relationship between the longitudinal noise band and the frequency of traffic accidents. The main purpose of this study is to study the relationship between the presence of a noise lane on the road and the frequency of traffic accidents. This study evaluates the impact of the noise band on the change in the risk of accidents and traffic violations.&#x0D; Methods. Various sections of the concentration of road accidents are considered on the basis of statistical data on highways of the technical category, on which measures have been implemented for the installation of longitudinal noise strips, allowing an analysis of their effectiveness. The features and recommendations regarding longitudinal noise strips on newly erected sections of highways are highlighted.&#x0D; Results. Taking the highways in the North-Western region as an example, the technology of installing longitudinal noise strips on highways has a benefit for road safety and economic efficiency.

CORRELATION ROAD GEOMETRIC ON ROAD DAMAGE

The implementation of safe road infrastructure can be defined by the minimum number of traffic accidents that occur on these roads. Traffic accidents can caused by several factors, encompassing the geometric attributes of the road, characteristics of vehicles, human elements, and environmental conditions. Previous studies have identified specific geometric features, including superelevation, degree of curvature, road slope, and shoulder type, as influential factors in traffic accidents. Simultaneously, the state of the pavement, characterized by issues such as rutting, potholes, IRI value, and skid resistance, also plays a crucial role in traffic accident. The interplay between road geometric and pavement conditions underscores the importance of effective road management for ensuring safety. Existing research highlights a correlation between road geometric and road damage, with the Pavement Condition Index (PCI) directly correlating with road and shoulder width, and inversely correlating with transverse and longitudinal road slope. This phenomenon is, in part, attributed to heightened shear strain on narrow road shoulders and a reduction in the resilience modulus value of the subgrade on sloped roads.

Advancing traffic safety through the safe system approach: A systematic review

The Safe System Approach (SSA) has emerged as a comprehensive framework for enhancing traffic safety through system-wide interventions. This systematic review, conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzes 82 relevant studies categorized based on the SSA pillars: safe road users, safe vehicles, safe speeds, safe roads, and post-crash care. The review provides insights into SSA's effectiveness in reducing road traffic fatalities and injuries, exploring implementation challenges and opportunities, including policy initiatives, institutional frameworks, and stakeholder collaborations. The findings highlight the potential for SSA to create a more forgiving and resilient transportation system, offering valuable guidance for policy decisions, future research, and interventions aimed at promoting safer road environments. SSA's comprehensive strategy for Safe Road Users encompasses considerations of road system design, behavior modification, and tailored measures for vulnerable users, showcasing its versatility in addressing diverse challenges. In the realm of Safe Vehicles, SSA actively involves manufacturers in a cycle of continuous improvement, rigorous testing, and collaborative efforts to establish new safety regulations. The emphasis on managing Safe Speeds, aligning with human parameters, and involving communities reflects SSA's adaptable nature and provides insights for establishing context-specific speed limits. SSA contributes significantly to Safe Roads through its implementation of innovative countermeasures, forgiving road designs, and the integration of emerging disciplines, resulting in a notable reduction in fatalities and injuries. In the domain of Post-Crash Care, SSA's integrated perspective fosters collaboration among emergency services, medical professionals, and the justice system. It addresses challenges through standardized approaches and information sharing, ensuring a comprehensive and unified approach to road safety. This review contributes to the ongoing efforts to prioritize safety and transform the transportation landscape on a global scale.