Enhancing road safety through advanced predictive analytics in V2X communication networks

Road traffic accidents are a major global challenge, causing significant public health and economic burdens. In Saudi Arabia, the rapid increase in motorization and urbanization has led to one of the world's highest traffic fatality rates. This study examines the effectiveness of the SAHER traffic enforcement system in the Dammam Metropolitan Area (DMA) using insights from 23 road safety experts through structured questionnaires and interviews. The analysis highlights that SAHER has improved compliance with traffic laws and enhanced road safety perceptions but remains limited in its scope and coverage. Key findings identify driver distractions, speeding, and sudden lane changes as the leading causes of accidents, with younger drivers disproportionately involved. Experts emphasize the need for enhancements to SAHER, including increasing the number and strategic rotation of cameras, addressing additional violations such as aggressive driving, and improving coverage near schools and high-pedestrian areas. Recommendations include integrating SAHER into future urban planning, combining automated enforcement with public education campaigns, and improving traffic infrastructure, such as signage and signal operations. This study provides valuable insights into the role of automated enforcement in improving road safety and offers actionable strategies for policymakers to address traffic-related challenges and promote sustainable urban mobility in Saudi Arabia.

This study examines India's initiatives to enhance road safety via the Integrated Road Accident Database (iRAD) and the e-Detailed Accident Report (e-DAR). The Ministry of Road Transport and Highways (MoRTH) has been executing many measures to improve road safety. Road accidents have become a significant threat in India. iRAD aims to address this issue by establishing a comprehensive road accident database to assist authorities in making proactive choices and ensuring 'Better and Safer Roads for All.' The main goals were to assess the efficacy of these activities in enhancing road safety measures, enabling precise data gathering, and optimizing accident reporting systems. The initiative will provide diverse insights by studying the national road accident data using data analytics techniques. The proposed system would provide analytical data via a Monitoring & Reporting Dashboard and an Analytics Dashboard to enhance understanding and support forecasting and decision-making by Apex Authorities for the formulation of new policies and strategies. The project's outcome will be enhanced road safety, namely 'Safe Road for All' in India

This article explores the intricate relationship between mental health disorders and their potential impact on road safety, with a particular emphasis on Cameroon. Mental health conditions such as Intermittent Explosive Disorder, Bipolar Disorder, Schizophrenia, Substance Use Disorder, and borderline personality disorder are examined in the context of driving behaviors, road rage incidents, and overall road safety. The objective is not to stigmatize individuals with mental illness but to highlight the importance of screening, support, and attention to mental health in the context of increasing road accidents. The paper discusses the symptoms of these disorders and how they may increase the risk of road accidents. It also explores the roles of government, community, and individuals in enhancing road safety through mental health support. Evidence-based data specific to road safety in Cameroon and other developing countries are utilized to support the arguments. Recommendations for interventions and support mechanisms are provided, emphasizing a non-discriminatory and objective approach.

In response to escalating road safety challenges within Algeria, marked by rising traffic volumes and urban development, the "TariqAmn Algeria" initiative emerges as a groundbreaking approach in traffic management. This study delineates the development and deployment of this innovative system, which leverages intelligent traffic signs, LiDAR (Light Detection and Ranging, using laser light to measure distances) radar, and proactive brake control technologies, embedded within a robust traffic management framework. The system's dynamic adaptation to real-time traffic conditions and its enforcement of speed compliance are central to its design. Rigorous evaluations through SUMO (Simulation of Urban Mobility, a traffic simulation software) simulations and field tests in high-risk urban settings affirm its efficacy, demonstrating a 60% reduction in traffic incidents and a 50% reduction in speeding violations. By marrying local cultural insights with cutting-edge technology, "TariqAmn Algeria" not only enhances immediate road safety but also establishes a new paradigm in traffic management systems. The findings illuminate the significant scalability potential of the system, promising broader applications in enhancing road safety and adherence to traffic regulations across various regions, such as densely populated urban areas, high-traffic corridors, and accident-prone black spots. This initiative underscores the critical role of Intelligent Transportation Systems (ITS) in modern road safety strategies and sets a precedent for future technological advancements in the field.

Drowsy driving is a major concern for road safety, leading to accidents and fatalities.This paper presents a novel approach called Optimized Dual-Tree Deep Learning (ODT-DL) for real-time drowsiness detection in drivers.The model uses advanced techniques like image preprocessing, feature extraction, and feature selection.It uses Hidden Markov Models for sequence modelling and classification, enabling accurate drowsiness detection.The experimental evaluation of ODT-DL on two benchmark datasets, YAWDD and NTHU-DDD, shows outstanding performance, with accuracy, precision, recall, and F1-Score consistently exceeding 99%.The model's high discrimination capabilities and low false alarm rates ensure reliable detection.Comparative analysis against other machine learning models, such as AlexNet, ResNet, Support Vector Machine, and ensemble methods, highlights the superiority of ODT-DL.The findings suggest the model's practical implications for enhancing road safety by preventing accidents caused by driver drowsiness, with potential applications in vehicle safety systems.The proposed ODT-DL model holds promise for real-world implementation and opens avenues for future developments in road safety technology.

The problem of road safety is not a new topic in Hong Kong. Every year there are more than ten thousands road traffic accidents of different severities involving more than ten thousands injuries. The trend of accident remains constant over the recent years. Yet serious injuries and deaths due to traffic accidents are still unavoidable. Therefore, more efforts have to be spent to reduce the impact from the road safety problem.
\n
\nThe road safety problem can be measured quantitatively by the numbers of accidents, injuries, and deaths, etc. The greater the values in these numbers, e.g. more deaths or injuries, the more serious the road safety problem is. The “ultimate” goal in solving the road safety problem is to “totally” eliminate the number of accidents and casualties, i.e. setting the target to “zero”. Though it is impossible to eliminate all traffic accidents, measures and policies implemented in improving the road safety conditions are still very important in achieving the “zero” accident target.
\n
\nThis study aims to find alternative means to express the road safety problems in addition to the conventional numbers as mentioned above. This alternative means is called the Road Safety Index. Although the Road Safety Index is not a new concept, it has never been generally applied in Hong Kong. Through reviewing the road safety related indices adopted outside Hong Kong, and studying the kinds of traffic accident data available in Hong Kong, the feasibility of developing a Road Safety Index in Hong Kong has been found.

The aim of this study is to analyze the dynamics, causes, and consequences of road traffic accidents (RTAs) in the Ternopil region over the past five years. This analysis seeks to identify the primary types of RTAs, determine the most frequent causes, and propose effective measures to reduce the number of accidents and their severity. By understanding these factors, we aim to enhance road safety and minimize the socio-economic impacts associated with RTAs in this region. In this study, a comprehensive statistical analysis of RTAs in the Ternopil region was conducted. This analysis revealed a significant increase in the number of accidents, which reached 2547 cases in 2023, an 11% rise compared to 2022. The primary types of accidents identified include collisions (50%), hitting obstacles (19%), hitting stationary vehicles (16%), and pedestrian accidents (8%). The study found that the highest mortality rates occur in pedestrian accidents (34%) and vehicle collisions (33%). Key causes of RTAs were identified as violations of maneuvering rules (40%), speeding (23%), failure to maintain a safe distance (10%), driving under the influence of alcohol (7%), and violations at intersections (5%). Based on these findings, several recommendations were made to enhance road safety. These include the implementation of artificial road bumps, installation of traffic lights with adjustable timers, additional road signs, high-visibility and illuminated pedestrian crossings, and the use of surveillance cameras to monitor compliance with traffic rules and identify violators. Moreover, the study emphasizes the importance of developing and implementing methodologies for predicting accident rates using statistical methods and conflict zone analysis, as well as calculating accident-related economic and environmental losses to evaluate the effectiveness of proposed measures. This study underscores the urgent need for improved road safety measures in the Ternopil region. Implementing the recommended strategies and methodologies will significantly reduce the number of RTAs, enhance overall road safety, decrease the risks of injuries and fatalities, and lower the economic costs associated with road traffic accidents. These findings provide a clear roadmap for policymakers and road safety authorities to take actionable steps towards a safer road environment.

This research examines the utility of combined crash modification factors (CMFs) in minimizing crash severity at urban roundabouts. Conventional CMFs typically assess the influence of singular interventions on road safety. However, traffic dynamics and the interactions among multiple safety measures necessitate a more comprehensive strategy. This study introduces a novel methodology for developing integrated CMFs that accounts for the interactive effects of multiple safety measures, providing a comprehensive understanding of their collective impact on road safety. The investigation utilized data from 16 urban roundabouts in Amman, Jordan, from 2015 to 2021. Regression models have been employed to develop individual and combined CMFs for various geometric and traffic characteristics, such as entry and exit widths, speed limits, and weaving patterns. Key findings indicate that interventions, such as reducing speed limits, modifying entry and exit widths, and adjusting weaving lengths, significantly improve safety. The analysis identifies hazardous roundabouts and proposes effective single and combined treatments to mitigate crash risks. This study highlights the importance of considering multiple treatments simultaneously to improve the predictive accuracy of safety assessments and supports the development of more effective road safety strategies tailored to specific crash types and severities. The approach demonstrates a significant potential to enhance road safety analysis and decision-making, ultimately contributing to safer road environments.

Reducing vehicle lane widths has been proposed as an effective strategy to decrease vehicle speeds and enhance road safety. However, the safety benefits of narrower travel lanes remain a topic of debate due to mixed findings in the literature. This study examines the relationship between lane width, vehicle speed, and crash occurrence to comprehensively understand their impact on road safety and transportation planning. Using data from 320 urban arterial sections in Utah, the analysis reveals that narrower lane widths are associated with reduced vehicle speeds. For every additional foot of lane width, 85th and 95th percentile speeds increase by 1.012 mph and 1.088 mph, respectively. Furthermore, injury crash modeling indicates that a one-foot increase in lane width is associated with a 38.3% increase in the odds of an injury crash on a roadway section. These findings contribute to the growing evidence supporting the implementation of narrower lane widths as a strategy to improve road safety, foster multimodal infrastructure, and promote sustainable urban transportation systems. We recommend that UDOT adopt a minimum lane width of 10 or 11 feet for arterials in highly urbanized areas, such as downtowns and major activity centers.

Learning spatial patterns and temporal dependencies for traffic accident severity prediction: A deep learning approach

RTAs (Road Traffic Accidents) are therefore an emerging global issue; they not only lead to losses in terms of finance, but also cause injury and loss of life. The discussion in this article about RTA (Road Traffic Accident) injuries in India encapsulates the main ideas briefly in this abstract. The RTA injury figures for India are a worrisome factor. The fatalities due to RTA (Road Traffic Accident) account for more than 1.5 lakh scantical yearly deaths. It is, therefore, necessary that comprehensive road safety measures may be employed. It is not the fatalities alone; a large number of lakhs of persons get injured in these incidents, the severity of which may have long-term physical, mental, and financial repercussions. RTA (Road Traffic Accident) injuries affect some demographics more than others. The load falls invariably on vulnerable road users such as walkers, cyclists, and two-wheeler riders. While there are more incidents in urban regions, the safety regulations are blatantly Ignored in ignored in rural areas. There are differences in RTA (Road Traffic Accident) injuries by age and gender. This, in particular, is very susceptible to young males due to habits such as overspeeding and rash driving. However, children and older adults have a risk of experiencing experiencing devastating injuries as well. Because of this burden, RTA inflections inflict on the health system; medical emergencies, trauma centers, and rehabilitation services should be enhanced. Several services are crucial for the needs of the injured and help them recover. RTA (Road Traffic Accident) injuries entail comprehensive actions of road safety. We might definitely do so by concentrating on these facts and ensuring safeguards in order to work for the reduction of RTA (Road Traffic Accident) incidents in India, hence saving lives and guaranteeing a healthier future for all road users. Any collision between a vehicle and a pedestrian, animal, road barrier, or immovable impediment like a tree or utility pole is referred to as a traffic accident, collision.

Advancing road safety strategy development: A data-driven multi-objective optimisation integrated approach

Internet of Things (IoT) is a platform that makes a device smart such that every day communication becomes more informative. A Smart Transportation system basically consists of three components which include smart roads, smart vehicles and a smart parking system. Smart roads are used to describe roads that use sensors and IoT technology which makes driving safer and greener. Smart parking system involves an automated system model that can assist the drivers in selecting the suitable parking spot for them. The data that the system collects will be sent for some analysis. It provides real time information to drivers about various aspects of transportation like weather conditions, traffic scenario, road safety, parking space, and many other things. A well-built Smart Transportation system reduces the risk of accidents, improves safety, increases capacity, reduces fuel consumption, and enhances overall comfort and performance for drivers. Our chapter deals with the in-depth discussion of these various aspects of a smart transportation system enabled with IoT technology.

The 2022 United Nations World Population Prospects report estimated the global population to be approximately 8 billion and expected it to reach 8.5 billion by 2030, 9.7 billion by 2050, and 10.9 billion by 2100. With the increasing urban population, road infrastructure and safety have become crucial, particularly for elderly drivers and operators of agricultural machinery, who are at a higher risk of traffic accidents owing to their reduced physical abilities and reaction times. This study addresses these issues by designing and piloting a safety mobility data-collection and -sharing system. The system utilizes Global Navigation Satellite System-based positioning technology to share the real-time locations of elderly-driver vehicles and agricultural machinery. Road hazard information is collected using the YOLO V4 algorithm and verified in a situation control room. Data are collected via delivery vehicles in urban areas and citizen-participation vehicles in low-traffic suburban areas to ensure comprehensive coverage and effective road hazard management. The findings suggest three primary conclusions. First, the real-time location-sharing system enhances road traffic safety compared to existing road information systems. Second, the effective management of road hazard information through regular data collection improves road maintenance, particularly in regions facing population decline. Third, the proposed data platform enables the prompt communication of urgent maintenance needs, such as potholes and road cracks, to relevant authorities, further enhancing road safety. This study highlights the importance of integrating advanced geospatial technology with community participation to create safer and more efficient road environments for vulnerable road users.

Vehicle-to-Everything (V2X) communication is an emerging technology, a vital enabler in realizing the concepts of smart cities, which helps in autonomous driving and intelligent transportation systems. This paper focuses on the performance comparison of Wi-Fi, Dedicated Short Range Communications (DSRC), and modified DSRC when used in a V2X communication network. The communication scenario considered includes message passing for V2X applications like emergency brake assist, intersection collision warning, lane departure warning, signal status display in-vehicle, school zone warning, and, parking slot status display as per the SAE J2735 standard. The communication network under various operational scenarios is implemented in NetSim, a licensed network simulator. The performance evaluation is done based on metrics like packet delivery ratio (PDR), MAC layer, and physical layer processing time, link throughput, and delay. Performance analysis based on unicast messages of DSRC and Wi-Fi shows that Wi-Fi gives better performance. A modified version of DSRC using variants of Wi-Fi can improve the performance of DSRC as evident from the performance analysis of modified DSRC.