Improve Road Safety Research Articles

The Classification of Work and Offenses of Professional Drivers from Slovakia and the Czech Republic

To achieve the elimination of the negative impacts of transport on road safety, the European Union is taking various measures resulting from its commitment to improve road safety. The main objective of this paper is to assess the impact of social legislation on road transport safety using real research and to identify the factors influencing the violation of the legislation depending on the type of transport carried out in domestic or international road freight transport. Answers from the respondents are used in the segmentation of truck drivers based on input data describing the factors influencing the violation of traffic rules. Two-step cluster analysis is a suitable method for the segmentation of truck drivers based on input data. These data are obtained through a questionnaire from a total of 472 Czech and Slovak truck drivers. The results show that the optimal number of types of truck drivers with different characteristics is four. All of the clusters are described and compared. The majority of truck drivers do not violate social legislation for several reasons, such as traffic accidents (almost 67%), complications in meeting the loading or unloading deadline (less than 88%), poorly planned transport routes (more than 90%) and driving home (almost 80%).

Android Accident Detection and Alert System

Increasing concern for road safety has led to the search for new technological methods to reduce the incidence of traffic accidents. This article describes the design, development, and evaluation of an Android-based incident detection and notifi- cation system that aims to improve emergency response capabili- ties and reduce incident severity. The system leverages ubiquitous smartphones and their built-in sensors by combining accelerom- eters, GPS, and image processing technology to instantly capture and share events. When detected, the system triggers an alarm to notify emergency services and first responders of the incident’s location and important information. The system also has features such as automatic emergency call initiation to provide immediate assistance to victims. The effectiveness of the system has been evaluated through various tests conducted in various simulated accident situations, demonstrating its accuracy, reliability and ability to shorten time in emergency situations. This research contributes to the advancement of smart transportation by using mobile technology to improve road safety and reduce the risk of accidents. Index Terms—Android-based system, accident detection, alert- ing, real-time monitoring, integrated sensor, accelerometer, GPS, imaging, emergency response, machine learning, automatic emer- gency calls, security, smart transportation, ubiquitous computing, smart mobile applications.

A YOLOv8-based approach for multi-class traffic sign detection

Recognizing traffic signs is a crucial task in autonomous vehicles to improve road safety. In this study, we propose a novel You Only Look Once version 8 (YOLOv8) model for identifying traffic signs. The model has been trained on the Kaggle dataset, which contains multiple traffic signs under different environments. The YOLOv8 model achieved an accuracy of 80.64% and a recall of 65.67% on test data. These metrics highlight the model's capability to recognize traffic signs. Notably, YOLOv8 brought about some notable improvements over earlier versions, such as enhanced detection in difficult situations and enhanced small-scale sign identification. The paper also explores the difficulties that arose throughout the model's training phase and provides workable solutions that were used to overcome these difficulties, improving the model's performance. The encouraging outcomes demonstrate the viability of implementing the YOLOv8-based strategy in practical traffic management systems, which is a positive advancement towards the creation of more sophisticated and dependable traffic sign recognition technology.

Модель информационно-телекоммуникационного взаимодействия между автоматизированными транспортными средствами в условиях неполного покрытия мобильными сетями

Objective: in order to improve safety, as well as optimize the path of movement in everyday life to reduce the time on the road, there are now more and more prerequisites for the introduction of highly automated and fully automated vehicles into various spheres of human life. This will optimize and improve the road system of the country as a whole, and most importantly — reduce accidents and, consequently, improve road safety. When implementing these vehicles, it is necessary to develop an algorithm for communication between different road users under different conditions and distances between objects. Methods: this study uses simulation modeling methods, the method of tabular and graphical data display, the method of factor analysis, methods of model experiment, as well as the method of expert assessments. Results: within the framework of the work, an algorithm has been developed for transmitting messages with different content and priority information between two highly automated / fully automated vehicles or a vehicle with an automated state infrastructure using various communication channels, as well as a simulation model based on it, which was created in the AnyLogic modeling environment. The use of different priorities allows you to guarantee the minimum delivery time of the most important messages containing information for vehicle management. As a result of the simulation, histograms of the probabilistic-temporal characteristics of the transmission of messages of various priorities and messages confirming their delivery were obtained using various communication channels. Practical significance: the simulation results make it possible to assess the quality and time of message delivery under specified conditions and to ensure effective transmission of information by changing the intensity and size of messages, the use of various communication channels and their speeds, and so on.

Advanced Privacy Scheme to Improve Road Safety in Smart Transportation Systems

In -Vehicle Ad-Hoc Network (VANET), vehicles continuously transmit and receive spatiotemporal data with neighboring vehicles, thereby establishing a comprehensive 360-degree traffic awareness system. Vehicular Network safety applications facilitate the transmission of messages between vehicles that are near each other, at regular intervals, enhancing drivers' contextual understanding of the driving environment and significantly improving traffic safety. Privacy schemes in VANETs are vital to safeguard vehicles’ identities and their associated owners or drivers. Privacy schemes prevent unauthorized parties from linking the vehicle's communications to a specific real-world identity by employing techniques such as pseudonyms, randomization, or cryptographic protocols. Nevertheless, these communications frequently contain important vehicle information that malevolent groups could use to Monitor the vehicle over a long period. The acquisition of this shared data has the potential to facilitate the reconstruction of vehicle trajectories, thereby posing a potential risk to the privacy of the driver. Addressing the critical challenge of developing effective and scalable privacy-preserving protocols for communication in vehicle networks is of the highest priority. These protocols aim to reduce the transmission of confidential data while ensuring the required level of communication. This paper aims to propose an Advanced Privacy Vehicle Scheme (APV) that periodically changes pseudonyms to protect vehicle identities and improve privacy. The APV scheme utilizes a concept called the silent period, which involves changing the pseudonym of a vehicle periodically based on the tracking of neighboring vehicles. The pseudonym is a temporary identifier that vehicles use to communicate with each other in a VANET. By changing the pseudonym regularly, the APV scheme makes it difficult for unauthorized entities to link a vehicle's communications to its real-world identity. The proposed APV is compared to the SLOW, RSP, CAPS, and CPN techniques. The data indicates that the efficiency of APV is a better improvement in privacy metrics. It is evident that the AVP offers enhanced safety for vehicles during transportation in the smart city.

DEVELOPMENT OF SIMULATION MODEL OF THE ROAD NETWORK SECTION OF THE CITY OF KAZAN

The article considers the process of developing a simulation model of the road network section of the city of Kazan in order to improve road safety. The choice of modelling tools is carried out and the load of Kazan city roads is studied.

Android Accident Detection and Alert System

Increasing concern for road safety has led to the search for new technological methods to reduce the incidence of traffic accidents. This article describes the design, development, and evaluation of an Android-based incident detection and notifi- cation system that aims to improve emergency response capabili- ties and reduce incident severity. The system leverages ubiquitous smartphones and their built-in sensors by combining accelerom- eters, GPS, and image processing technology to instantly capture and share events. When detected, the system triggers an alarm to notify emergency services and first responders of the incident’s location and important information. The system also has features such as automatic emergency call initiation to provide immediate assistance to victims. The effectiveness of the system has been evaluated through various tests conducted in various simulated accident situations, demonstrating its accuracy, reliability and ability to shorten time in emergency situations. This research contributes to the advancement of smart transportation by using mobile technology to improve road safety and reduce the risk of accidents. Index Terms—Android-based system, accident detection, alert- ing, real-time monitoring, integrated sensor, accelerometer, GPS, imaging, emergency response, machine learning, automatic emer- gency calls, security, smart transportation, ubiquitous computing, smart mobile applications.

Maturity measurement in road traffic injury prevention

Road traffic related death and injury continues to be a major challenge globally. Unsafe road use is particularly evident in low- and middle-income countries while also being a growing concern for private sector organisations. The Safe System approach is recognized internationally as the leading approach to improving road safety and previous work has codified the essential management functions and interventions evident in its successful implementation. Tracking the development of Safe System adoption within the public and private sectors is of interest for several reasons. This paper presents recent development and use of road safety maturity frameworks and discusses the utility of these approaches for road safety practitioners and researchers.

A Two-Phase Approach for Efficient Traffic Sign Detection and Recognition

Objectives: The objective of this study is to enhance the accuracy of traffic sign detection and recognition in modern intelligent transport systems, addressing real-time challenges under varying conditions. Methods: A two-phase approach is adopted. The first phase employs the You Only Look Once version 8 (YOLOv8) architecture to efficiently detect traffic signs under real-time conditions, considering variables like adverse weather and obstructions. Subsequently, the second phase employs a sequential convolutional network for precise recognition, utilizing the output from the first phase. This integrated method enhances traffic sign detection and recognition, contributing to road safety and efficient traffic management in complex transportation scenarios. Findings: The YOLOv8 architecture, utilized in Phase 1, demonstrated exceptional performance with a mean Average Precision (mAP) of 0.986 during validation. In Phase 2, the Convolutional Neural Network (CNN)-based recognition model achieved an impressive test accuracy of 98.7% on 463 test images, with a low-test loss of 0.1186, indicating consistent accuracy. The robustness of both models is confirmed by successful testing with three unseen images. YOLOv8 accurately detected and classified these images, while the CNN model correctly recognized them. These findings underscore the effectiveness of the two-phase approach in enhancing traffic sign detection and recognition, with significant implications for improving road safety and traffic management in real-world scenarios. Novelty: The novelty of this approach lies in its seamless integration of YOLOv8 for efficient traffic sign detection and a sequential convolutional network for accurate recognition, offering a significant advancement in addressing real-time challenges and contributing to enhancing road safety and traffic management in an increasingly complex transportation landscape. Keywords: Traffic sign detection, Traffic sign recognition, Convolutional Neural Networks, YOLOv8, Object detection

Pattern of Intra-Abdominal Injuries among Patients Presented with Blunt Abdominal Trauma

Background: Blunt abdominal trauma (BAT) represents a significant challenge in trauma care, particularly in developing countries where road traffic accidents (RTA) predominate as a cause. Understanding the demographic characteristics, etiology, and injury patterns associated with BAT is crucial for effective management and preventive strategies. Objective: This study aims to evaluate the patterns of BAT in patients presenting to Lady Reading Hospital, Peshawar, focusing on identifying the leading causes, affected demographic groups, and organ-specific injuries to inform better clinical practice and preventive measures. Methods: Conducted from January 1, 2023, to June 1, 2023, this cross-sectional study included 60 patients with BAT. Participants underwent comprehensive evaluations involving clinical assessments, radiological imaging, and laboratory tests, following Advanced Life Trauma Support guidelines. Data analysis utilized SPSS version 25, focusing on categorical and numerical variables to assess injury etiology, demographics, and organ involvement. Results: The majority of BAT patients were male (percentage provided in the full study), with a mean age of 35.42±11.31 years. RTAs were identified as the leading cause of BAT, contributing to (specific percentage) of cases. The liver (48.3%) and spleen (36.7%) were the most commonly injured organs. Other significant findings included injury rates for the intestine (13.3%), mesentery (10%), and kidney (5%). Conclusion: BAT predominantly affects younger males, with RTAs serving as the principal etiological factor. The high incidence of liver and spleen injuries necessitates prompt and precise diagnostic approaches. The study underscores the need for improved road safety measures and public health interventions to reduce BAT incidences, alongside enhancing emergency and trauma care capabilities.

Proactive safety assessment of urban through-roads based on GPS data

Road safety is a worldwide issue, while urban roads account for a high share of serious road injuries, especially involving vulnerable road users, such as pedestrians or cyclists. Specifically, the safety of major roads through built-up areas (through-roads) is insufficient due to mixed traffic conditions including vulnerable road users, varying driving behaviour, and many disruptions, which are combined with excessive speed.. In this context, various traffic calming measures have been implemented to improve road safety, such as gateways or pedestrian refuge islands. However, the specific safety impacts of traffic calming combined with specific characteristics of through-roads are often unknown, since most traditional evaluations have been limited by small sample sizes of crash data, as well as wide variations in physical and road characteristics. To overcome the limitations of crash-based evaluations, we used the GPS-based data from a sample of 21 Czech and 12 Polish through-roads to develop the Speed-Safety Index, which combines speed, speed variance, and traffic volume. Our study has three novelty features: (1) To assess safety, we used speed and speed variance simultaneously. (2) To complete the missing link between specific traffic calming measures and safety, we validated the statistical relationship between the developed Speed-Safety Index and crash history. (3) To prove the usefulness of the developed index, we also showed its practical interpretation by proving the effect of spacing between traffic calming measures on safety. The index proved to be well correlated to crash frequency and it also proved the effect of spacing between traffic calming measures: the longer spacing, the smaller speed-reducing effect. The paper concludes with a discussion on the limitations, which we plan to address in further research, by moving from the current macro-perspective (Speed-Safety Index on the level of through-roads) to the micro-perspective (focusing on individual directions, locations, and traffic calming measures). We also plan to investigate the method’s applicability in different contexts. If the approach proves feasible, with reliable and valid results, it may become an alternative for a proactive network-wide road assessment, as called for by the European Road Infrastructure Safety Management Directive.

Navigating the future: Autonomous vehicle integration challenges and opportunities in Malaysia

As autonomous vehicles (AVs) revolutionize the global transportation landscape, their implications for emerging economies like Malaysia remain a subject of significant interest. This study delves into the multifaceted world of AV technology, focusing on Malaysia’s unique transportation challenges and opportunities. Through interviews with key stakeholders and experts, the research uncovers valuable insights into AV technology’s awareness, regulatory landscape, integration hurdles, potential benefits, and inclusivity impact in the Malaysian context. The study finds that while AVs hold the promise of improved road safety, reduced traffic congestion, and enhanced environmental sustainability, addressing challenges related to regulation, infrastructure, and public acceptance is imperative for successful integration. Additionally, AV technology has the potential to significantly enhance inclusivity in transportation, benefiting individuals with disabilities. The study underscores the need for holistic policy and infrastructure development to leverage the benefits of AV technology and pave the way for a sustainable and inclusive transportation future in Malaysia.

Mega-Events: Assessing Road Safety through an Operating Framework. An Application for the Milano–Cortina 2026 Winter Olympic Games

To meet the United Nations and European Union goals of reducing road crash fatalities and injuries, it is also relevant to address the negative externalities due to mega-events on the road network and the local communities, to assess the safety of the road network involved, and to implement appropriate measures for different road environments. Despite their relevance, the literature often overlooks social costs and risks associated with mega-events. This study presents an operating framework for rapidly assessing the safety of the Milano–Cortina 2026—“Via Olimpica” road—which will host a significant proportion of the traffic during the Winter Olympic Games in 2026. The framework proposes a simplified Road Infrastructure Safety Management (RISM) to address the unique challenges posed by the limited time available for screening and implementation by local authorities. The framework integrates four data sources and follows a seven-step procedure. It provides recommendations for improving road safety by identifying critical road sections and blackspots. Road authorities, practitioners, and public administrations may all benefit from the framework, as it makes it easier to prioritise safety improvements within time constraints.

Use of Technology to Promote Road Safety: Public Health Perspective.

Road safety has been acknowledged as a public health concern of global significance owing to its direct association with human lives and their well-being. Road safety is a much broader concept than measures that are aimed to just prevent accidents, but it extends to improving the quality of life of members of the community, safeguarding environmental health, and improving the economy of the nation. With the passage of time, several technologies in road safety have surfaced, and their collective employment can aid in the establishment of a smart, safe, and efficient transportation system in nations. In fact, these technologies will account for a significant reduction in the number of accidents, better management of traffic, and improved road safety. In conclusion, ensuring road safety can save lives, promote responsible behavior, and create secure transportation system. There is an immense need to create awareness, engage the community, and embrace technology to minimize the possibility of road accidents and ensure safe journeys for all.

Behind the wheel: Probing into personality, skills, and driving behavior’s role in bus rapid transit crashes

Objective Personality traits and driving skills are significantly associated with driving behaviors and crashes. In the case of professional bus drivers, the relationships amongst these variables have not been sufficiently examined in terms of road crashes. Therefore, this study seeks to examine the relationship between personality traits, driving skills, driving behaviors, and crash involvement among Bus Rapid Transit (BRT) drivers. Methods The study employed a comprehensive data collection strategy involving self-reported questionnaires, including the driver behavior questionnaire, driver skill inventory, and Big Five inventory, alongside Global Positioning System (GPS)-extracted speeding data from a sample of 166 drivers. To explore the relationship between variables, the study utilized the Partial Least Squares Structural Equation Model (PLS-SEM) as the analytical method. Result The findings reveal that self-reported violations and actual speeding performed by drivers were positively associated with crash involvement, whereas positive driving behavior negatively influences violation, errors, speeding and crash involvement. The study also found that the safety skills were negatively associated with violations, errors, and speeding, while higher perceptual-motor skills were associated with higher instances of speeding violations, resulting to a higher possibility of getting involved in a crash. Finally, the study reveals that certain personality traits (extraversion and neuroticism) were positively associated with violations, errors, and speeding, leading to a higher risk of getting involved in crashes, whereas certain personality traits (conscientiousness and agreeableness) were associated with safe driving. Conclusion The study findings offer valuable insights into the predictors of crashes among professional BRT drivers, which can be used to enhance driving practices, ensuring the safety of the public. Moreover, these findings provide transportation agencies with better management and decision-making capabilities to implement effective interventions to improve road safety.

ANALYSIS OF THE CAUSES OF TRAFFIC ACCIDENTS ON SIMÓN BOLÍVAR AVENUE

Simón Bolívar Avenue, located in the city of Quito-Ecuador, has been identified as an area with a high rate of traffic accidents. To establish the main causes of traffic accidents, this study investigated a specific 15.4 km stretch extending from Guajaló to the Ruta Viva interchange on Simón Bolívar Avenue. The aim of the research is to analyze factors involved in traffic accidents and propose actions to mitigate them. To carry out the aim, data obtained from the Visor de Siniestralidad Nacional of the Agencia Nacional de Tránsito (ANT) on accidents occurring between 2019 and 2023 were compiled. These data made it possible to identify the most common causes of traffic accidents in this particular area. Upon knowing the reasons for these incidents, it was possible to determine the reasons that significantly influence traffic accidents on the avenue. Besides, an analysis was made of the critical points where most traffic accidents are concentrated. These critical points provided valuable information to generate and make proposals to reduce the accident rate and improve road safety. The implementation of these proposals and improvements can in turn contribute to reduce the accident rate and increase safety on the road section studied. Therefore, these findings will serve as a starting point to generate proposals to reduce the accident rate and improve road safety on Simón Bolívar Avenue and thus fulfill the aim of understanding the main causes of traffic accidents in order to mitigate the incidence of accidents in the area studied. Therefore, these findings will serve as a starting point to generate proposals to reduce the accident rate and improve road safety on Simón Bolívar Avenue and thus fulfill the aim of understanding the main causes of traffic accidents in order to mitigate the incidence of accidents in the area studied.

Methodology for assessing the road traffic risk in urban areas

In the assembly of concerns for reducing the negative effects of urban road traffic, special consideration is paid - both at the local and global level - to issues related to road traffic safety. Spotting urban zones vulnerable to traffic crashes, understanding the causes of crashes, identifying some directions of action to reduce the traffic risk and their application from the urban planning stage can improve road safety. In this framework, the paper presents a methodology for estimating the intrinsic risk linked to road traffic. The main objective is identifying the relationships between the crash frequency and the intrinsic factors contributing to their occurrence. The proposed methodology is particularized for Bucharest based on the disaggregated statistical data analysis on severe accidents recorded in 2017-2022. The resulting model allows the classification of risk levels and, further, identification, planning and scheduling measures to improve urban road safety.

Enhancing Road Safety: Machine Learning-Driven Vehicle Speed Monitoring and Alerting in VANET Environments-A Review

Inexperienced and fast driving poses a significant threat to the safety of innocent people, resulting in severe automobile accidents. Presently, most efforts have been made in detecting the driver behavior, as traditional methods show limited success the researchers have delved into the machine learning and the deep learning methods for predicting the vehicle speed and as well as altering. This review explores at the manner in which machine learning and the deep learning can be used to improve road safety using Vehicle Ad-hoc Networks. The primary objective revolves around a Machine Learning-Driven Vehicle Speed Monitoring and Alerting System, which is intended to reduce the dangers associated with variable speeds in VANET environments. The paper reviews the existing research, approaches, and breakthroughs in the use of machine learning algorithms for real-time vehicle speed monitoring. This analysis intends to provide insights into the emerging environment of intelligent transportation systems, with a focus on the role of artificial intelligence in identifying and responding to potential risks. It presents an in-depth review of the challenges, opportunities, and future prospects for using machine learning to improve road safety within the VANETs.

CONVOLUTIONAL NEURAL NETWORKS: AN APPROACH FOR VISUAL OBSTRUCTION DETECTION IN AUTOMOTIVE REVERSING CAMERAS

In recent years, the study of Artificial Intelligence in the automotive industry has led to the design of intelligent systems applied to road safety, highlighting the importance of improving road safety worldwide, and thus reducing the number of accidents annually. One of the main functions of these systems is, for example, pedestrian detection, which is performed by cameras and radar-type sensors, among others. However, environmental factors cause visibility problems and obstructions that make pedestrian detection difficult and lead to collisions. With the purpose of contributing to the solution of the exposed problem, two case studies using Convolutional Neural Networks are applied in this research. The first using a pre-trained model (Inception V3) and the second, a proposed model (RvlsNet) to detect dirt on the lens of a vehicle's reverse camera. These types of factors directly affect visibility, which leads to an increased risk of collision when reversing the vehicle. Applying a general data mining methodology, we obtained a result of 0.9549 and 0.9416 accuracy, respectively, for the models used. Keywords: Convolutional Neural Networks, Classification, Obstruction, Detection, Reversing camera, Inception V3

Analysis of bicycle crash characteristics and typical scenarios in France: A descriptive epidemiology and clustering approach

Bicycle crashes can result in severe injuries and fatalities, emphasizing the importance of identifying typical scenarios and characteristics of these crashes to develop effective prevention and mitigation strategies. This study analyzed datasets of bicycle crashes in France from 2005 to 2021 extracted from the French national road traffic accident database, evaluating bicycle accident characteristics through conventional descriptive epidemiology approaches and using the K-medoids clustering method to identify typical scenarios and their associated characteristics. Among 1,121,571 road traffic accidents in France, 80,851 were bicycle crashes, with a decline between 2005 and 2013 but a steady increase since then, peaking in 2020 at approximately 1.74 times that of 2005. Victims of bicycle crashes were primarily between 11 and 62 years old, with the largest population among 14–33 years old. Male victims were more involved in bicycle crashes than female victims, with the majority of crashes involving a moving vehicle. The majority of bicyclists involved in accidents were found not wearing helmets, and even bicyclists wearing helmets still sustained injuries. Typical bicycle crash scenarios included four bicycle-vehicle head-on-side collisions, one bicycle-obstacle collision, and two bicycle-pedestrian collisions, with bicycle-obstacle collisions often resulting in serious injuries similar to bicycle-vehicle collisions. Pedestrians were more likely to sustain injuries than bicyclists during bicycle-pedestrian collisions, and their injuries were often more severe than those of bicyclists. The study provides insights into the characteristics and typical scenarios of bicycle crashes in France and can be used to develop targeted interventions to improve road safety for cyclists.