Road Safety Regulations Research Articles

Low and intermediate temperature fracture performance of hot mix asphalt (HMA) reinforced with nano-reduced graphene oxide (NRGO) under freeze-thaw cycles and aging conditions

ABSTRACT This study evaluates the effect of nano-reduced graphene oxide (NRGO) and conditions of short- and long-term (three freeze-thaw cycles (3 FTC)) failure and six days of ageing (6 AM) on bituminous mixtures from the viewpoint of fracture toughness (KIIC and KIIIC) and fracture energy (FEII and FEIII) at ±15 °C. The results showed the fracture stiffness was improved at ±15 °C (under three conditioning states). However, the fracture flexibility was decreased and increased at low and medium temperatures, respectively. At low temperatures, the results indicated that FEIII and KIIC had the lowest resistance under unconditioned and AM conditions, while FEII and KIIC had the lowest resistance under FTC. At intermediate temperatures, FEIII and KIIC under non-conditioned and FTC conditions and FEII and KIIC under AM conditions obtained the lowest resistance. Therefore, it was recommended to check both loading states in determining the shear performance (in-plane and out-of-plane) of asphalt mixtures. Finally, by providing the minimum short- and long-term properties required to control shear and tear cracks, reinforced mixtures lead to increased service life, reduced maintenance costs and improved road safety in cold and tropical regions.

Optimizing road safety: integrated analysis of motorized vehicle using lattice ordered complex linear diophantine fuzzy soft set.

In this manuscript, we delve into the realm of lattice ordered complex linear diophantine fuzzy soft set, which constitutes an invaluable extension to the existing Fuzzy set theories. Within this exploration, we investigate basic operations such as and , together with their properties and theorems. This manuscript is more amenable in two ways, i.e., it enables real-life problems involving parametrization tool and applications with an existing order between the components of the parameter set based on the preference in the complex frame of reference. Adaptive cruise control (ACC) is a system designed for maintaining distance between two vehicles and to sustain a manually provided input speed. The purpose of cars with ACC is to avoid a collision that frequently happens nowadays, thereby improving road safety regulations amidst rising collision rates. The fundamental aim of this manuscript is to prefer an applicable car with ACC together with its latest model by defining a peculiar postulation of lattice ordered complex linear diophantine fuzzy soft set . Emphasizing real-life applicability, we illustrate the effectiveness and validity of our suggested methodology in tackling current automotive safety concerns, providing useful guidance on reducing challenges related to contemporary driving conditions.

Awareness, Attitude, And Practices toward Road Safety Rules and Regulations among the Graduate School Students of ILOCOS Sur Polytechnic State College Tagudin Campus

Awareness, Attitude, And Practices toward Road Safety Rules and Regulations among the Graduate School Students of ILOCOS Sur Polytechnic State College Tagudin Campus

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

A Review of Electrically Conductive Cement Concrete Pavement for Sustainable Snow-Removal and Deicing: Road Safety in Cold Regions

Within cold U.S. regions, winter storms can cause interruptions in transportation networks, affecting transportation entities’ revenue streams. Conventional snow-removal methods on roadways efficiently remove snow and ice, yet their adverse environmental impacts further make winter maintenance more challenging. In response to these concerns, electrically conductive cement concrete (ECCC) pavement has become an effective alternative for deicing and snow melting on road surfaces. ECCC utilizes the Joule heating principles to effectively melt snow and ice by incorporating conductive elements into conventional concrete. This paper comprehensively reviews the current literature on ECCC pavement. Previous studies have diligently explored various aspects of ECCC pavement, including concrete conductivity enhancement, heat transfer processes, and meticulous performance assessments, ranging from controlled laboratory scale experiments to small-scale field evaluations. The conclusions drawn from these investigations highlight the potential of ECCC pavement to considerably enhance winter road maintenance, consequently improving road safety and minimizing traffic interruptions during winter storms. The present review emphasizes ECCC pavement as a promising paradigm for effectively addressing the complexities associated with winter road maintenance in colder regions. Moreover, its environmentally friendly deicing capabilities present a sustainable departure from conventional methodologies. However, certain limitations currently impede widespread adoption of ECCC pavement, mainly concerning optimizing cost-effective construction techniques, ensuring long-lasting durability, and enhancing energy efficiency. Addressing these limitations could accelerate the broader adoption of ECCC pavement, promoting safer and more sustainable winter transportation practices.

Safety evaluation for regulation changes on commercial vehicle operation using multilevel Bayesian methods

Truck-involved crashes cause serious social and economic losses, and the crash severity is higher compared to ordinary vehicles. Driver fatigue is a major crash causation, especially have a larger impact on truck drivers. Therefore, mandating a truck rest-break is one of the road safety management strategies for reducing both truck-involved crash frequency and severity. This paper analyses the change in safety performance according to the revision of the truck rest-break time on highways. Because spatial heterogeneity and homogeneity should be considered to estimate safety effects precisely after executing crash countermeasures, this study estimates the safety effect after the revision of the Trucking Transport Business Act, by developing a model based on the Multilevel Full-Bayes before and after the study. The safety performance function was developed in different section units and distributions, and the crash modification factor was calculated for the most suitable model. As a result of the analysis, the performance of the Multilevel Bayesian Poisson-gamma model was the highest, and it was found that there was a crash reduction effect after the revision of the truck break time. The results of this study can be referred to when preparing measures for road safety regulations.

Perception and Practices Towards Road Safety Rules and Regulations Amongst Pakistani Population

Background: Road traffic accidents (RTAs) remain a critical public health issue in densely populated urban areas like Latifabad, Hyderabad. Understanding the community's perception and practices towards road safety can inform targeted interventions to reduce RTAs. Objective: This study aims to explore the perception and practices regarding road traffic rules and regulations among residents of various units in Latifabad, Hyderabad, Pakistan. Methods: A cross-sectional descriptive study was conducted from March to August 2023 in Latifabad, Hyderabad. It involved 400 commuters aged 18 years and above, from either gender, residing in the area. Participants were included irrespective of their possession of a driving license, provided they consented to participate. Data on their perceptions and practices concerning road traffic were collected using a structured written questionnaire. Results: The participant demographic included 64.7% males and 35.3% females with an average age of 22.6 ± 1.2 years; the majority were students. Nearly half (48.0%) attributed poor road conditions as a primary cause of RTAs. Over half (57.0%) recognized driving without a valid license as an offense, with females significantly more aware (p<0.05) of the risks of improper overtaking and the links between illiteracy, irresponsible behavior, and increased RTAs. There was no significant difference in the scores for violations and lapses related to injury exposure, though a significant association was found between errors and injury exposure. Conclusion: Despite possessing adequate knowledge, participants frequently displayed a dismissive attitude towards compliance with traffic laws, normalizing risky behaviors and potentially increasing the risk of road traffic accidents and injuries.

Enhanced Road Safety with Photoluminescent Pedestrian Crossings in Urban Contexts

The safety of roads in urban areas is a major concern for governments, demanding innovative solutions to enhance pedestrian safety. This paper introduces a novel approach to crosswalks by integrating resin with photoluminescent additives, offering a significant boost to road safety. A thorough methodology was employed to assess its effectiveness, covering mechanical, lighting, and vibroacoustic aspects, alongside a photogrammetric analysis of real-world experiments. The material exhibited noteworthy mechanical properties, displaying consistent tensile strength, load capacity, and strain values with a remarkable Shore A hardness. After 20 min, luminance values peaked at 68 mcd/m2, surpassing standard vehicle headlights at 100 m. Additionally, vibroacoustic analysis highlighted a noticeable relationship between vehicle speed and sound bandwidth, indicating the system’s potential to alert pedestrians. Tests revealed that the proposed system significantly decreased the average vehicle speed by 36.96% compared to conventional crosswalks, with a 27.80% reduction when drivers yielded to pedestrians. Furthermore, a survey involving 35 participants, focusing on the knowledge of road safety regulations, behavior, signage, and visibility, found positive results regarding accident reduction. The estimations indicate potential decreases of 26.26% in injuries and 35.4% in fatalities due to improved road conditions, 26.58% in injuries and 53.16% in fatalities resulting from reduced average speeds, and 52.56% in injuries and 79.91% in fatalities through enhanced road education. This underscores the multifaceted impact of the system on urban road safety.

Evaluating the Impact of Autonomous Vehicle Technologies on Singapore’s Road Safety Regulations

This paper investigates the impact of autonomous vehicle (AV) technologies on Singapore’s road safety regulations, leveraging a mix of literature review, policy analysis, and data evaluation. Singapore, a frontrunner in smart transportation, faces unique challenges and opportunities in integrating AVs into its urban fabric. Our analysis highlights the adaptability and responsiveness of Singapore’s regulatory framework to the emerging AV landscape, pinpointing areas where further adjustments are necessary. The findings suggest that while Singapore has made strides in accommodating AVs, ongoing regulatory evolution is essential to harness their full potential for enhancing road safety and mobility. This study offers insights into the complexities of regulating AVs, emphasizing the need for a balanced approach that supports innovation while ensuring public safety.

E-Challan Based Intelligent Assistance System for Smart City

Traffic management and enforcement of road safety regulations have become increasingly crucial in modern urban environments, as the number of vehicles on the roads continues to surge. With the rise in traffic violations, particularly signal disobedience, there is a pressing need for innovative solutions that can efficiently detect and penalize offenders while reducing the burden on law enforcement personnel. The E-Challan System harnesses the power of cutting-edge computer vision techniques, most notably Automatic Number Plate Recognition (ANPR), to accurately capture and decipher vehicle number plates in real- time. High-resolution cameras strategically positioned at traffic junctions and signal intersections actively monitor passing vehicles, automatically detecting those that violate traffic signals. Upon successful identification of a signal violation, the E-Challan System promptly generates an electronic challan, or e-challan, containing detailed information about the violation, such as the date, time, and location of the incident. This comprehensive documentation serves as indisputable evidence of the offense and provides an essential legal foundation for imposing fines on violators. with an Auto Debit Fine mechanism.

Overview of road traffic injuries among migrant workers in Guangzhou, China, from 2017 to 2021

IntroductionThere are many migrant workers in China’s first-tier cities, but little is known about road safety. This paper systematically analysed road traffic injuries and risk factors among migrant workers in...

Time series based road traffic accidents forecasting via SARIMA and Facebook Prophet model with potential changepoints

Road traffic accident (RTA) is a critical global public health concern, particularly in developing countries. Analyzing past fatalities and predicting future trends is vital for the development of road safety policies and regulations. The main objective of this study is to assess the effectiveness of univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) and Facebook (FB) Prophet models, with potential change points, in handling time-series road accident data involving seasonal patterns in contrast to other statistical methods employed by key governmental agencies such as Ghana's Motor Transport and Traffic Unit (MTTU). The aforementioned models underwent training with monthly RTA data spanning from 2013 to 2018. Their predictive accuracies were then evaluated using the test set, comprising monthly RTA data from 2019. The study employed the Box-Jenkins method on the training set, yielding the development of various tentative time series models to effectively capture the patterns in the monthly RTA data. SARIMA(0,1,1)×(1,0,0)12 was found to be the suitable model for forecasting RTAs with a log-likelihood value of −266.28, AIC value of 538.56, AICc value of 538.92, BIC value of 545.35. The findings disclosed that the SARIMA(0,1,1)×(1,0,0)12 model developed outperforms FB-Prophet with a forecast accuracy of 93.1025% as clearly depicted by the model's MAPE of 6.8975% and a Theil U1 statistic of 0.0376 compared to the FB-Prophet model's respective forecasted accuracy and Theil U1 statistic of 84.3569% and 0.1071. A Ljung-Box test on the residuals of the estimated SARIMA(0,1,1)×(1,0,0)12 model revealed that they are independent and free from auto/serial correlation. A Box-Pierce test for larger lags also revealed that the proposed model is adequate for forecasting. Due to the high forecast accuracy of the proposed SARIMA model, the study recommends the use of the proposed SARIMA model in the analysis of road traffic accidents in Ghana.

Evaluation of Regional Road Transport Safety Service Level With Edge Computing in Scalable Sensor and Actuator Networks

In contemporary society, road transport has assumed a pivotal role in the daily lives of individuals, with virtually everyone being a road user in one capacity or another. Unfortunately, a significant portion of road accidents stem from user negligence and a lack of awareness regarding road safety principles and regulations. The majority of these accidents are fundamentally a result of human errors, errors that could potentially be averted through the comprehensive utilization of technology. The integration of edge computing within actuator networks, combined with the scalability of sensors, holds the potential to proactively detect and prevent impending collisions. This cutting-edge approach not only accumulates and processes sensor-generated data but also employs it to mitigate the occurrence of road accidents. This discussion delves into the potential advantages offered by smart sensors and advanced technology, focusing on the enhancement of safety within transportation networks. The core attributes of these intelligent technologies facilitate the establishment of robust connectivity and the effective implementation of processes, ushering in an innovative era in the realm of road safety through smart technology.

Rancang Bangun Aplikasi Aksi Sadar Keselamatan Jalan Berbasis Android

High school students understanding of road safety regulations is still lacking causing traffic violations to increase among teenagers. This is due to the lack of road safety learning media. The android-based road safety action awareness application (SIAR SENJA) was built using app inventor software to increase understanding of road safety. SIAR SENJA application development uses the agile development method consisting of ideas, planning, development and evaluation. The SIAR SENJA application helps high school students understand road safety and be aware of the hazards involved, so that they can become pioneers of road safety in everyday life. SIAR SENJA application development can be done by adding animated video features and improving the display quality of the application.

English

English

Hot spot analysis and evaluation of influencing factors on regional road crash safety and severity indices: insights from Iran

Inadequate regional road safety studies have been conducted in developing countries like Iran. Regarding regional road safety indices (RSIs), a significant disparity between Iranian provinces was observed. Thus, it was aimed to evaluate the regional RSIs in Iran and identify their influencing factors and potential hot spots. Data on regional road crashes, fatalities, demographics, transportation, health institutions, economics, education, and fuel consumption rates were collected. The association between the variables was evaluated using correlation analysis. Using Moran’s I and local Moran indices, provinces’ spatial distributions were evaluated. Hot spot analysis was used to identify factors influencing RSIs. Significant correlations between the variables were detected. A vast local cluster in terms of fatality per injury (as a crash severity index) was identified in the country’s southeast. The distribution patterns of provinces in terms of seven RSIs were cluster-like. Variable groups, including road length, demographic, income, education, and geographic, influence RSIs in hot or cold spot regions. Crashes were severe in underdeveloped and remote provinces. Increasing income and education levels make it possible to reduce crash severity indices in this country. A positive Moran’s I index does not guarantee the existence of significant local cluster cores in a country.

Experimental Study of the Correlation between the Wear and the Braking System Efficiency of a Vehicle

The growing number of vehicles leads to increasingly intense and congested traffic, which will additionally put demand on the braking system, and drivers behave more aggressively. As a result, the braking system must be durable and efficient. For this purpose, the study of the braking system behavior in conditions of intense and moderate traffic is required to increase the safety of traffic participants—respectively, to demonstrate the need for more frequent replacement of some braking system elements. Thus, a series of successive tests were performed on a vehicle, through which the degree of wear of the brake pads and discs was monitored periodically, resulting in the efficiency evolution of the braking system. The tests were carried out both in the laboratory (on a dynamometer) and in traffic to establish the efficiency of the braking system according to some parameters considered essential. The experimental tests showed that the recommendations regarding the frequency of replacement of brake pads and discs are inconsistent with their actual wear. Therefore, the aim of this paper is to establish the braking system efficiency of a vehicle, subject to testing depending on the vehicle mass, travel speed, distance driven, and braking time, based on experimental tests, on stand and in-traffic, according to the road safety regulations. The validation of the braking system efficiency of the tested vehicle was done by measuring the stopping distance and time in relation to the initial speed, the mass of the vehicle, the number of kilometers traveled, and conditions of intense and moderate traffic.

Information and Entropy Aspects of the Specifics of Regional Road Traffic Accident Rate in Russia

The aim of this research is to study the specifics of the road accident rate formation processes in regions of the Russian Federation (2021) using information-entropic analysis. The typical research approaches (correlation-regression, factorial analyses, simulation modelling, etc.) do not always allow us to identify its specificity. It is impossible to evaluate the quality of the researched process’s structure using these methods. However, this knowledge is required to understand the distinctions between high-quality road safety management and its opposite. In order to achieve the goal of the research methodology based on the use of the classical approaches of C. Shannon, the quantitative value of information entropy H was elaborated. The key components of this method are the modelling of the cause-and-effect chain of road accident rate formation and the consideration of the relative significances of individual blocks of the process in achieving the final result. During the research the required statistical data were collected and the structure of the road accident rate formation process in 82 regions of the Russian Federation in the format “Population P—Fleet of vehicles NVh—Road Traffic Accidents NRA—RTA Victims NV—Fatality Cases ND” was analyzed. The fact that the structure of the road accident rate formation process is extremely specific in different Russian regions was shown. Exactly this specificity forms the degree of ambiguity in the state of Russian regional road safety provision systems in terms of the probability of death in road accidents. The main conclusion of this research is that information-entropic analysis can be successfully used to assess the structural quality of road safety systems.

Human capacity development and Performance: A focus on the Abia sector command of Nigeria federal road safety corps, 2010-2020

This study examined the impact of human capacity development on staff performance in the Federal Road Safety Corps, Abia State Sector Command. Anchored on human capital theory and qualitative and quantitative date generated through documentary and survey methods, the study observed that the regular training and re-training FRSC periodically conducts for its Officers and Marshals enhanced the capacity of the Command to enforce road safety regulations as evident in increased compliance to road safety regulations by road users. The study thus posits that human capacity development enhanced staff performance in the Federal Road Safety Corps, Abia State Sector Command, between 2010 and 2020. Among others, the study recommends increased awareness campaign among commercial drivers to ensure continued compliance with road safety regulations. Keywords: Human capacity training, development, staff performance, organizational growth, road safety management

Road Safety Legal Compliance among Malaysian Motorcyclists

With the ever increasing popularity of motorcycles as a mode of transport in Malaysia and several other countries in Southeast Asia, numerous of legislation, enforcement and other legal compliance are available to improve their effectiveness toward motorcyclists. The awareness level among motorcyclist toward legal compliance is very important to the motorcyclist to check and balance their knowledge and attitude toward road safety. The main objective on this study is to measure the level of compliance among motorcyclist towards Road Safety Legislations. Furthermore, the common errors done by motorcyclist during riding, and also legal compliance related to motorcycle are discussed in this study. A descriptive and cross sectional study was used to identify the level of awareness among motorcyclist towards legal compliance by using observation and a set of questionnaire. The result from the study revealed that knowledge and attitude among motorcyclists were good toward legal compliance of Road Safety regulations.