Improve Road Safety Research Articles

Ефективність заходів щодо запобігання дорожньо-транспортним пригодам: огляд сучасних стратегій та підходів

This article investigates the problem of road traffic accidents (RTA) and reviews current strategies and approaches to prevent and reduce them. The significance of this problem is that the health of road users and the safety of vehicle traffic on roads is one of the most important global societal issues, especially with the continuous growth in the number of vehicles and the pace of urban activity. The article examines various aspects of road traffic accident prevention, including legal, technical, psychological and educational measures. It analyzes current trends in the development and implementation of road safety programs, as well as successful practices to reduce the number of accidents and injuries on the roads. Based on a study of the effectiveness of various strategies and programs aimed at reducing the number of accidents and casualties in traffic accidents, the article provides information on which measures are most effective and how they can be implemented in different contexts. In addition, it emphasizes the importance of cooperation between the authorities, public organizations and citizens in implementing road safety. The article also discusses in detail the importance of preventive measures, which may include educational campaigns and awareness-raising activities on vehicle safety on the roads. It also analyzes the legal norms and penalties for violations of traffic rules and the possibility of studying and using the experience of other countries, in particular Poland, in ensuring the safety of vehicles on the roads. The concluding part of the article discusses the prospects for the future development of road safety systems and the challenges that may arise in the future. The findings of this study can be useful for policy makers, researchers, and road safety professionals to improve road safety and reduce the number of road traffic accidents.

Fatigue Driving Detection and Early Warning System based on EEG Signals

The fatigue driving detection and warning system based on EEG signals aims to use EEG technology to monitor the mental state of drivers in real time, so as to identify and warn fatigue driving behavior in time, so as to improve road safety. The design of the system is based on a deep understanding of the connection between EEG signals and the fatigue state, by analyzing the changes in frequency bands such as α wave, β wave, θ wave and δ wave, which are closely related to the alert state and fatigue in humans. The development of the system first considers the main factors induced by fatigue, such as sleep deprivation, long work hours and environmental monotony, which can significantly affect the activity of brain waves. From these physiological and environmental factors, the system requirements analysis emphasizes the need for real-time data processing and high accuracy, as well as a user interface design that provides clear and intuitive feedback to users (drivers and regulators). In terms of implementation, the system integrates EEG monitoring equipment for data acquisition, uses advanced signal processing algorithm to extract fatigue-related features, and uses machine learning model for real-time analysis and fatigue state determination. In addition, the system design includes an emergency response mechanism, once signs of fatigue are detected, it can automatically trigger an alarm, issue a warning to the driver, and if necessary through the vehicle control system to ensure driving safety. This comprehensive system not only improves driving safety, but also provides strong data support for traffic management departments.

The importance of road safety inspection for the improvement of pedestrian safety in traffic

Road safety inspection is a modern tool for improving road safety, that is, a preventive tool that is applied on roads in use. The goal of preventive action in road safety is to improve road safety, through reducing the number and consequences of traffic accidents. The road safety inspection involves the analysis of general data about the section, the analysis of traffic accidents, but also the analysis of specific road defects. 11 specific elements are analyzed, including vulnerable road users, including pedestrians, cyclists and motorcyclists. Vulnerable road users are particularly at risk because in the event of a traffic accident, they suffer more severe consequences because their body is not physically protected. This paper shows the importance of road safety inspections for the improvement of pedestrian safety in traffic.

Traffic situation in Yerevan and ways to modernize with the help of current problem of traffic congestion

Introduction. This article addresses the pressing issue of road traffic congestion in the capital of Armenia, Yerevan, and proposes a solution using the Automated Traffic Management System (ATMS). The introduction underscores the significance of effective traffic management in ensuring the safety and comfort of urban residents.Materials and methods. The analysis of the current situation raises concerns about traffic density, congestion, inefficient use of road resources, and their impact on the environment and citizens’ quality of life. In turn, the exploration of ATMS as a solution includes an explanation of the system’s concept, its benefits, and successful global implementation examples. The implementation plan for ATMS encompasses stages from data collection to traffic management, along with necessary technical and infrastructural changes. The assessment of costs and benefits underscores the economic and social components of anticipated outcomes.Conclusions. The introduction of automated traffic management systems (ATMS) leads to a significant reduction in transport delays at intersections by 10–20%, which reduces gasoline consumption for movement by 30%. The number of traffic accidents at intersections is reduced by 10–15%. This leads to economic benefits, improved road safety and improved environmental conditions in cities.

Representations of truck platooning acceptance of truck drivers, decision-makers, and general public: A systematic review

Truck platooning, involving two or more automated trucks virtually linked in a convoy through vehicle automation and communication technologies, has become a core topic in the long-haul freight transport industry. Despite its potential benefits – fuel efficiency, reduced carbon emissions, operation cost savings, improved road safety, and alleviated traffic congestion – further research is required to understand the representations of technology acceptance that will mediate its adoption across different stakeholders. This study presents a systematic review of the representations of decision-makers, truck drivers, and the general public on truck platooning acceptance. A total of 35 papers were included in the review and grouped into (i) studies with no platooning experience, (ii) studies with a simulated platooning experience, and (iii) studies with an on-road platooning experience. Representations were extracted using thematic analysis to synthesize the perspectives of each stakeholder and organized in themes. Even when similar themes emerged, representations highlight each stakeholder singular perspective. Although decision-makers have a more positive outlook on the potentialities of the technology, they are concerned about several obstacles related to its implementation and risks that may undermine the promised efficiency benefits of truck platooning. Regarding general public, peripheral drivers are mainly concerned about the reliability and safety of truck platooning and the potential traffic conflicts. Truck drivers denote the potential advantages in driving comfort and road safety, but highlight their concerns about employment, the reliability of the automation, loss of driving pleasure, trust in the platooning systems and their elements, and additional stress associated with the reconfiguration of their activity by the technology. Meanwhile, considering the role of experience, when the technology was experimented on-road, representations became more positive. Still, although these experiments are closer to real-world context, they only focused on basic driver-truck interactions and did not account for multitask driving scenarios, nor explored truck drivers’ employment concerns.

Self-Reported Speed Compliance and Drivers Speeding Behaviour in Cameroon

Speeding is a significant global issue, with disparities in speed and safety outcomes between low- and middle-income countries (LMICs) and high-income countries (HICs). This study aims to address speed research gaps in LMICs by examining the prevalence of self-reported speeding and factors influencing drivers’ speeding behavior using the Theory of Planned Behavior (TPB). An online survey involving 387 anonymous drivers was conducted in Yaoundé. Results showed that all drivers reported exceeding speed limits, with 81% exceeding them by 5 km/h, 12% by 10 km/h, and 7% by more than 10 km/h in urban areas. On highways, 54% reported exceeding the limit by more than 10 km/h. Age, driver’s license, and gender showed significant associations with speed limit non-compliance. This study’s results suggest that the TPB can be useful in assessing speeding behavior, as the inclusion of TPB variables led to a more than 50% proportionate increase in speeding behavior variance. Structural equation modeling revealed attitude towards speeding as the strongest predictor of speeding intention, along with certain demographics indirectly influencing speeding behavior through speeding intention. Perceived behavioral control and speeding intention directly influenced speeding behavior. These findings emphasize the potential of interventions targeting attitude, intentions, and perceived behavioral control to modify speeding behavior and improve road safety.

Children as Key Actors in Participatory Planning: Co-Working Experience of Community Planning for Walking Safety Around Bongrae Elementary School in South Korea

This study conducted a participatory planning process with children and applied it to a case study aiming to improve road safety in a school zone in Busan, South Korea. The planning project was conducted with children in a three-step planning process: field work, solution and location proposals, and plan construction. During the participatory planning process, children presented various opinions based on their experiences and selected preferred solutions and locations. The results show that children can serve as key actors in community planning. Planners must reinterpret planning problems based on children’s characteristics to ensure their active participation.

Investigating the spatial heterogeneity of drunk-driving events in Beijing based on a hybrid method with LISA and GeoDetector

Drunk-driving events can be divided into drunk-driving crashes (DDCs) and non-crash drunk-driving events (NDDEs). Strong spatial heterogeneity, which consists of spatial local heterogeneity and spatial stratified heterogeneity, exists in drunk-driving events. Therefore, this article proposed a hybrid method with LISA and GeoDetector to investigate the spatial heterogeneity of drunk-driving events. First, an integrated method of Getis-Ord Gi* and Anselin Local Moran’s I, which belong to LISA, was applied for the analysis of spatial local heterogeneity. Then, based on the improved GeoDetector with a highest q value, this article explored the main factors contributing to spatial stratified heterogeneity. Results show that DDCs are concentrated in the urban areas and the sub-centres located in the east of suburban, while NDDEs are predominantly found in the urban areas and the sub-centres located in the northwest and southwest of suburban. The spatial stratified heterogeneity analysis, conducted using the Factor Detector, reveals the pivotal factors influencing the spatial distribution of DDCs. Entertainment venues, alcohol outlets, and population density exhibit significant impacts, with q-values of 0.233, 0.227, and 0.203, respectively. Similarly, for NDDEs, the most influential factor is the density of entertainment venues, with a q-value of 0.243. When exploring the interaction effects between factors, the study emphasises that the interaction between population density and alcohol outlet density shows the highest q-value of 0.787. This highlights a substantial impact on the spatial formation of drunk-driving events. These findings offer valuable insights for implementing targeted measures to improve road safety and reduce the occurrence of drunk-driving events in Beijing.

Speed Management Blueprint: Conception of an IoT-Based Electric Vehicle Speed Limiter Monitoring System for Kigali City Vehicles

The Electric Vehicle Speed Limiter Monitoring System is an Internet of Things (IoT)-based solution to prevent traffic accidents in Kigali, Rwanda. The system utilizes advanced technology to monitor vehicle speed, identity, and position, including GPS, RFID cards, RFID 522 modules, and IR sensors. While RFID cards facilitate easy vehicle identification, RFID 522 modules identify speed infractions, IR sensors guarantee precise speed measurement, and GPS tracking offers real-time monitoring capabilities. Campaigns to raise public awareness will assist the system, which will be smoothly incorporated into Kigali’s road network and examined for patterns and trends. Ensuring seamless implementation and enforcement will require collaboration with municipal authorities, law enforcement agencies, and transportation authorities. Anticipated results include a significant decrease in traffic accidents, increased traffic safety precautions, and improved traffic management techniques. Advanced technology like IR sensors, RFID cards, RFID 522 modules, and GPS are used by Kigali, Rwanda’s IoT-based Electric Vehicle Speed Limiter Monitoring System, to track vehicle position, identification, and speed. It conducts public awareness campaigns, analyses speed-related accidents, and integrates them with Kigali’s road network. Smooth implementation is ensured by cooperation with local law enforcement and authorities, which lowers accident rates and improves traffic management techniques. The system aims to improve road safety and foster a safer atmosphere for all Kigali drivers.

Boundary Layer Modelling of Flow Acceleration and Energy Transfer Effects in Smart Pavement Design

The integration of remote technology into the construction industry has advanced the emergence of smart, self-learning infrastructure across all levels of land transportation design and development. However, energy harvesting capabilities for smart road ventilation purposes have not yet been fully researched as the world gravitates towards energy sustainability. This study thus presents a mathematical investigation of the self-draining design potentials of road pavements, leveraging on energy absorption and conversion to accelerate conductive and convective ventilation of these pavements during wet conditions, as a means of ensuring the skid resistance and safety of such pavements are not compromised. Applying numerical methods with the aid of commercial software code MATLAB®, the classical physics of fluid-surface interaction was used to model accelerated drainage of microflood off paved surfaces through methodical modification of the thickness of the boundary layer associated with the flow regime over the flat road surface. Results indicate significant influences of energy exchange, thermal, and viscous diffusivity on flow acceleration. Alterations in parameters such as the slip factor, thermally induced Brownian motion or phoretic characteristics of the fluid particles at the boundary induce accelerated flow at the surface of the pavement. The findings contribute to the advancement of smart infrastructure by offering practical insights into the potential benefits of integrating energy-harvesting technologies into road pavement systems. By optimizing flow acceleration mechanisms, smart pavements can play a crucial role in improving road safety and sustainability in urban environments.

Evaluation of Radio Access Protocols for V2X in 6G Scenario-Based Models

The expansion of mobile connectivity with the arrival of 6G paves the way for the new Internet of Verticals (6G-IoV), benefiting autonomous driving. This article highlights the importance of vehicle-to-everything (V2X) and vehicle-to-vehicle (V2V) communication in improving road safety. Current technologies such as IEEE 802.11p and LTE-V2X are being improved, while new radio access technologies promise more reliable, lower-latency communications. Moreover, 3GPP is developing NR-V2X to improve the performance of communications between vehicles, while IEEE proposes the 802.11bd protocol, aiming for the greater interoperability and detection of transmissions between vehicles. Both new protocols are being developed and improved to make autonomous driving more efficient. This study analyzes and compares the performance of the protocols mentioned, namely 802.11p, 802.11bd, LTE-V2X, and NR-V2X. The contribution of this study is to identify the most suitable protocol that meets the requirements of V2V communications in autonomous driving. The relevance of V2V communication has driven intense research in the scientific community. Among the various applications of V2V communication are Cooperative Awareness, V2V Unicast Exchange, and V2V Decentralized Environmental Notification, among others. To this end, the performance of the Link Layer of these protocols is evaluated and compared. Based on the analysis of the results, it can be concluded that NR-V2X outperforms IEEE 802.11bd in terms of transmission latency (L) and data rate (DR). In terms of the packet error rate (PER), it is shown that both LTE-V2X and NR-V2X exhibit a lower PER compared to IEEE protocols, especially as the distance between the vehicles increases. This advantage becomes even more significant in scenarios with greater congestion and network interference.

Multigroup invariance of measure for angry drivers (MAD) scale using a representative sample of drivers in Australia

Introduction: Driver anger and aggression have been linked to crash involvement and injury outcomes. Improved road safety outcomes may be achieved through understanding the causes of driver anger, and interventions designed to reduce this anger or prevent it from becoming aggression. Scales to measure anger propensities will be an important tool in this work. The measure for angry drivers (MAD; Stephens et al., 2019) is a contemporary scale designed to measure tendencies for anger across three types of driving scenarios: perceived danger from others, travel delays, and hostility or aggression from other drivers. Method: This study aimed to validate MAD using a representative sample of Australian drivers, stratified across age, gender, and location. Participants completed a 10-minute online survey that included MAD, sought demographic information (age, gender, driving purpose, crash history), as well as the frequency of aggressive driving. Multigroup confirmatory factor analyses (MGCFA) assessed how stable the structure of the MAD was across drivers of different ages, gender, purposes for driving and those who do or do not display anger aggressively. MAD was invariant across all groups, showing that all drivers interpreted and responded to MAD in the same way. Results: A comparison of latent means showed anger tendencies were higher for men compared to women, for younger drivers compared to older drivers, and for those who drive mainly for work compared to those who mainly drive for other reasons. When controlling for driver factors, driving anger was associated with increased odds of being aggressive while driving. Practical Applications: Overall, this study demonstrated that MAD is an appropriate scale to measure anger tendencies and can be used to support interventions, and evaluation of interventions, to reduce anger and aggressive driving.

Accident black spots identification based on association rule mining

This paper presents an analytical approach to identifying the important characteristics of accident black spots on Moroccan rural roads. An association rule mining method is applied to extract road spatial characteristics associated with fatal accidents. The weighted severity index was calculated for each section, which was then used to determine the severity levels of black spots. The apriori algorithm is applied to find the correlation between road characteristics and the severity levels of black spots. Then, a general rule selection method is proposed to identify the rules strongly associated with each severity level. The results show that the proposed approach is effective in identifying the most important factors contributing to accidents. Furthermore, it shows that the combination of several road characteristics, such as road width, road surface, and bridge presence, may contribute to fatal accidents. The general rule selection found that wet, bad surfaces, and narrow shoulders were significantly associated with accidents on rural roads. The findings of the present study can help develop effective strategies to reduce road accidents and thus improve road safety in the country.

Enhancing Road Safety: A Driver Fatigue Detection and Behaviour Monitoring System using Advanced Computer Vision Techniques

Driver drowsiness is a major hazard to road safety, necessitating the development of reliable detection technologies. This study describes a revolutionary driver fatigue detection system that uses cutting-edge computer vision technologies. This system uses the MediaPipe framework for accurate face and hand detection and the Eye Aspect Ratio (EAR) for drowsiness detection. Furthermore, it uses the OpenCV solvePnP function for estimating rotation vector, and converting it to a rotation angle of the head, to check driver attention. By continuously monitoring these indicators, when the system successfully detects any instances of driver tiredness or inattention, it records his or her behavior and delivers notifications to help prevent accidents. This study helps to improve road safety by utilizing cutting-edge computer vision techniques to prevent driver fatigue and boost attentive driving practices.

Pavement maintenance and surface treatment: Evaluation and prevention towards road safety aspect along Ampang Jaya road

Abstract The severity of road damage, such as surface cracking or potholes putting drivers of moving vehicles at greater risk of collisions. This study aims to determine and propose the maintenance procedure and surface treatment activity carried out along Jalan Taman Putra, Jalan Bunga Raya and Jalan Perdana Utama in Ampang Jaya, Selangor. From a visual inspection, Jalan Bunga Raya and Jalan Perdana Utama was rated as B and required immediate repairs, while Rating C required to be fixed for user comfort on Jalan Taman Putra. Patch defects account for 45 % and 46 % of all defects on Jalan Taman Putra and Jalan Bunga Raya routes, respectively. Meanwhile, most defects on Jalan Perdana Utama were delamination, accounting for 38 % of all defects. The statistics on the number of road accidents show that pavement restoration work is effective when it results in a decrease in accidents between 2015 and 2021 by 42 % in Jalan Taman Putra, 33 % in Jalan Bunga Raya, and 12 % in Jalan Perdana Utama. Rehabilitating the roads had a favourable impact on Ampang Jaya, Selangor's low traffic accidents rate and raised levels of road safety. To improve road safety features, efficient supervision and maintenance efficiency are required.

Developing improved crash prevention approaches through in-depth investigation of motorcycle crash causation patterns

Despite advancements in road safety, Powered Two-Wheelers (PTWs) remain a vulnerable group with disproportionately high crash rates. This paper presents an in-depth analysis of PTW crashes in six European countries, with a case study of Loss of Control in Curves (LoCC), to address the gap between crash causation and prevention. By examining crash causation factors and their linkage to prevention strategies, the study illustrates various approaches for connecting causes and countermeasures. These approaches, which are applicable to different crash scenarios, include looking forward in the crash causation chains, looking backward, looking at only the last cause (critical events), or the first cause, or following a systemic approach. The research introduces a set of guidelines following the safe system approach, aiming to enhance the understanding of crash prevention among policymakers. The systemic approach to countermeasures, bridges the shortcomings of traditional crash causation studies that may exhibit bias or a narrow focus on “root causes”. The proposed approach emphasizes the need for a comprehensive view of crash scenarios (i.e., considering the entire crash causation chain or multiple causation chains) and ensuring that preventive measures address the full spectrum of the system. It also takes in to account external factors such as cost, benefits, and politics, leading to improved road safety outcomes. The study findings are significant for researchers, since it is a step forward in in-depth crash causation studies, as well as road practitioners and policymakers, in providing a strategic framework for more effective and efficient road safety interventions.

Analysis of Road Traffic Accidents in Dense Cities: Geotech Transport and ArcGIS

Road Traffic Accidents (RTAs) provide a substantial risk to both public safety and infrastructure within severe earthquake zones. This research aims to provide a thorough analysis of the many elements that contribute to RTAs in the area. Although research on RTAs has been conducted in numerous settings, there is a lack of localized research specifically focused on developing countries. Therefore, this study is necessary to address this gap in literature. Our study focuses on examining the distinct issues and elements that are particular to this specific location. The objective is to provide customized solutions that effectively reduce the occurrence of accidents. The objective of this study is to examine the status of RTAs, ascertain the main factors contributing to RTAs, and provide viable approaches to improve road safety. A comprehensive study of data was undertaken, which included the examination of key highways, accident-prone areas, and causes contributing to accidents. Insights were derived by the use of statistical analysis, hotspot mapping, and the categorization of incidents. The results revealed a distressing roadway are afflicted by a significant frequency of accidents, even those occurring on prominent thoroughfares. Excessive speed has been revealed as the primary contributing factor, closely followed by negligence and recklessness. The common factor seen in these events was the insufficient enforcement of traffic regulations. The research highlights several practical consequences. Firstly, it is advised to implement urgent actions such as the upgrading of the traffic database, the adoption of contemporary traffic management software, and the enforcement of rigorous traffic laws. The establishment of a comprehensive centralized database system is crucial in order to enhance the documenting and management of accidents. Furthermore, the use of cost-benefit analysis provides justification for the adoption of traffic calming measures in locations with a high incidence of accidents. Furthermore, the implementation of continuous road safety awareness efforts and the establishment of clear legislative guidelines pertaining to fatal accidents are imperative measures in promoting enhanced road safety.

Advancing Road Safety through Cloud Based RSU Solutions for Smart Internet of Vehicles

This research proposes a system to improve road safety by detecting and alerting drivers about hazardous road conditions in real-time. Vehicles equipped with sensors, GPS, and communication devices can autonomously detect hazardous road conditions and transmit alerts to a central cloud system through IoT MQTT protocol. Upon receiving alerts, vehicles can dynamically adjust their routes to avoid hazardous areas, reducing the risk of accidents. By utilizing IoT technology, the system ensures the reliability and authenticity of shared information, enhancing trust and overall road safety awareness. The proposed system architecture leverages cloud-based Roadside Units (RSUs) to facilitate communication among smart vehicles, providing real-time road condition information and ensuring secure and efficient data exchange. The system aims to address the challenges of existing IoV frameworks, such as data protection, key management, storage, concurrency performance, and response speed, by implementing robust security measures and efficient data management strategies.

Enhancing Bangladeshi License Plate Recognition: A YOLOv8 Approach with Roboflow Integration for Accuracy and Speed Optimization

Abstract: Automatic License Plate Recognition (ALPR) systems are indispensable in modern transportation services, offering crucial benefits in traffic management, parking, toll collection, and surveillance. However, implementing ALPR in Bangladesh presents challenges due to the intricacies of Bangla characters and low-resolution CCTV images. Despite ALPR's potential to enhance safety and security, issues such as license plate variations and image quality hinder its effective implementation. This research addresses these challenges by developing a customized YOLOv8 model tailored for recognizing Bengali license plates, with a focus on precise localization, character segmentation, and script deciphering. Leveraging advanced deep learning techniques, the study aims to enhance efficiency and accuracy for applications in law enforcement and traffic management. Through a comprehensive workflow integrating Roboflow and YOLOv8, effective dataset collection, annotation, augmentation, and model training are demonstrated. System evaluation on diverse test datasets confirms the reliable detection and recognition of Bangladeshi license plates, underscoring the model's practical utility and robustness in real-world scenarios. Additionally, the research showcases notable advancements in ALPR technology specific to Bangladesh's unique context. Noteworthy is the model's exceptional performance in detecting license plates from corner angles, even across various vehicle types, as well as its ability to accurately identify plates amid challenging conditions such as broken or obscured plates and low-resolution images. Moreover, the system's proficiency in scenarios with multiple license plates, like those on buses adorned with banners, contributes significantly to improved road safety and traffic management.

Implementation Paper on Vehicle Accident Detection System

Road traffic accidents pose a serious threat to public safety and cause a large number of victims every year. Of course, in order to reduce these risks, the demand for traffic accidents must increase. This brief presents a new solution using technology to improve road safety. The collision detection system is designed to detect and alert police and emergency services in the event of a collision. The system uses a combination of sensors, cameras, and machine learning algorithms to accurately identify accidents, analyze their severity, and send important information to first responders; thus, reducing response time and potentially saving lives. Key Words: public safety, emergency services, real-time, sensors