Improve Road Safety Research Articles

Exploring Factors Influencing Speeding on Rural Roads: A Multivariable Approach

Speeding is one of the main contributing factors to road crashes and their severity; therefore, this study aims to investigate the complex dynamics of speeding and uses a multivariable analysis framework to explore the diverse factors contributing to exceeding vehicle speeds on rural roads. The analysis encompasses diverse measured variables from Croatia’s secondary road network, including time of day and supplementary data such as average summer daily traffic, roadside characteristics, and settlement location. Measuring locations had varying speed limits ranging from 50 km/h to 90 km/h, with traffic volumes from very low to very high. In this study, modeling of influencing factors on speeding was carried out using conventional and more advanced methods with speeding as a binary dependent variable. Although all models showed accuracy above 74%, their sensitivity (predicting positive cases) was greater than specificity (predicting negative cases). The most significant factors across the models included the speed limit, distance to the nearest intersection, roadway width, and traffic load. The findings highlight the relationship between the variables and speeding cases, providing valuable insights for policymakers and law enforcement in developing measures to improve road safety by determining locations where speeding is expected and planning further measures to reduce the frequency of speeding vehicles.

Development of Tools for the Automatic Processing of Advanced Driver Assistance System Test Data

Advanced driver assistance system (ADAS) technologies are key to improving road safety and promoting innovation in the automotive sector. The approval and analysis of ADAS systems, especially automatic emergency braking (AEB) tests, require complex procedures and in-depth data management. This work presents innovative tools developed to facilitate the post-processing of ADAS AEB test data, created in collaboration with Nardò Technical Center. The tool, called Autonomous Code Generation Intelligence (ACGI), introduces an intuitive and intelligent user interface that helps analyze and interpret ADAS test approval regulations. ACGI automates the generation of code sections within a data analytics framework, streamlining the compliance process and significantly reducing the time and programming skills required. This tool allows engineers to focus on high-value tasks, improving overall process efficiency. To achieve this objective, the tool encodes the DAART code framework (Data Analysis and Automated Report Tool) which allows users to carry out real post-processing of the tests conducted on the track. The results demonstrate that both tools simplify and automate critical steps in the ADAS automatic emergency braking test data analysis process. In fact, the tool not only improves the accuracy and efficiency of the analyses but also offers a high degree of customization, making it a flexible and adaptable tool to meet the specific needs of users. In future developments, ACGI could be extended to cover additional ADAS tests and could be equipped with artificial intelligence to suggest configurations based on new regulations.

Statistical Modeling of Space Mean Speed for Accurate Traffic Volume Estimation on Two-Lane Highways in IbadanMetropolis, Nigeria.

This study investigated the statistical modeling of Space MeanSpeed for traffic volume forecasting on two-lane highways in Ibadan metropolis in Nigeria. The focus was on two primary roads: Ojoo–Mokola Expressway and Mokola–Agodi Gates (Queen Elizabeth Road). Statistical Regression analysis model were developed to represent the relationship between space mean speed (u) and traffic density (k). The results obtained revealed correlation coefficient (R) value of 0.9333 and correlation determination (R²) value of 0.9876 for the Ojoo – Mokola Expressway and correlation coefficient (R) value of 0.8762 and correlation determination (R²) value of 0.7863 for Mokola – Agodi Gates road; with both road network indicated a positive linear correlation between space mean speed and traffic density. Furthermore, the calibrated space mean speedtraffic density model for both road networks indicated that Ojoo–Mokola road had a better R and R² than Mokola–Agodi Gate road. The increase in space mean speed with increasing traffic density during the peak hour was consistent with previous literature. The research provided useful insights for traffic management system and speed control measures, recommending further studies to reduce traffic congestion and improve road safety in Nigeria.

Replacing speed bumps with chicanes: modernizing India's traffic calming strategy

Urban regions across the globe are progressively using traffic calming strategies to lower traffic speeds and improve road safety. Certain measures, such as chicanes, are used as a lateral or horizontal shift in the carriageway with the intention of reducing speed. The current research aims to investigate the overall impact of speed bumps, which were either removed or replaced in response to its discomfort and public outcry. A crossing 85th percentile speed range of 12–15 km/h was observed at the speed bumps. In a pilot study, chicanes as a replacement showed an average of 49% speed reduction compared with 62% by speed bumps in mixed traffic conditions. A sample size of 5000 vehicles was used to assess the speed profiles. Despite comparatively high crossing speeds, chicanes still serve the purpose by reducing speeds to less than 25 km/h. Simulation was performed and estimated through node evaluation using a micro-simulation tool, to replicate and compare parameters such as queue delay and acceleration, reflecting how drivers might behave inside the influence range where speed bump was replaced. It was perceived that while deciding the most appropriate calming measure, the geometric attributes, speed profiles, and public opinion should be considered before implementation.

Analysis of Traffic Accident Patterns Using Association Rule Mining

This study analyzed the levels of minor, moderate, and severe traffic accidents in the Palembang Police area from 2015 to 2020 using association rule mining and the apriori algorithm. The study established valuable insights into accident trends and contributing factors by leveraging traffic accident data and determining variable relationships. With a minimum support threshold of 0.05 and a confidence value of 0.5, the processed data revealed 349 total incidents, categorized as follows: 58 minor accidents (16.62%), 168 moderate accidents (48.14%), and 123 severe accidents (35.24%). The findings highlight that moderate-level accidents form the majority, underlining the need for targeted interventions in this category. The application of the apriori algorithm facilitated the identification of frequent itemsets and rules that reveal patterns across accident variables, such as road conditions, road functions, accident types, weather conditions, and victim statuses. This study also demonstrated the practicality of the apriori algorithm in analyzing extensive datasets to extract actionable insights. The processed rules can be a foundation for developing predictive models or decision-making tools to mitigate accident risks. For example, analyzing variables at different accident levels allows policymakers to identify critical factors contributing to accidents, implement tailored safety measures, and prioritize infrastructure improvements. Furthermore, the study emphasizes the potential of data-driven traffic management and accident prevention approaches. By incorporating modern data mining techniques, stakeholders can transition from traditional data recapitulation to predictive analytics, enabling proactive measures for public safety. Future research can build upon this work by integrating real-time data sources, such as IoTbased traffic monitoring systems, to enhance the prediction accuracy and scope of analysis. Further exploration of mid- and low-confidence rules may provide insights into rare but critical patterns, offering a more comprehensive understanding of accident dynamics. Overall, this research is crucial to leveraging advanced computational methods for public safety and traffic accident reduction, aligning with global efforts to improve road safety and minimize fatalities.

Intelligent Transport System for Lane Detection and Pothole Detection using YOLO

This paper aims to develop an automated system for pothole and lane marking detection on roads using the YOLOv4- tiny model with a supporting database. The traditional methods of detecting road conditions are slow and inefficient, as they are based on manual processing. With computer vision, our system will identify potholes and lanes much more quickly and accurately. The YOLOv4-tiny model is chosen because it provides a good balance between speed and accuracy, making it suitable for real-time applications. The database of images of roads with potholes and lanes will support training and testing of the models to achieve reliable detection. It is anticipated that this paper will be helpful in the improvement of road safety and maintenance as it will be able to provide timely information about road conditions.

Road Accident Prediction

Road accidents are a global concern, leading to a significant loss of life and property. This paper presents a machine learning-based approach for predicting road accidents by leveraging feature engineering techniques and advanced Ran- dom Forest models. Using enriched datasets with preprocessing methods, the proposed model achieves a predictive accuracy of over 92%. This study also explores key insights from accident data, providing actionable outcomes to improve road safety measures.

Intelligent Vehicle Accident Detection System

Road traffic accidents in Nigeria represent a significant public safety concern, with thousands of lives lost annually. The Intelligent Vehicle Accident Detection System (IVADS) addresses this issue by providing a system that detects accidents automatically and instantly notifies emergency responders with real-time location information. IVADS integrates multiple sensors, GPS, and GSM technologies to achieve accurate accident detection and rapid communication. This paper provides a comprehensive review of the system’s design, implementation, testing results, and potential implications for road safety improvements in Nigeria. This work builds upon existing research on accident detection, using innovative sensor integration and machine learning for precise detection.

Demographic risk factors and injury severity scores in Substance-use behaviour related traffic crashes

Alcohol and drugs, including Tetrahydrocannabinol (THC), significantly impact roadway safety by impairing cognitive functions, coordination, and reaction times, leading to increased crash risk and severity. This study examines the prevalence of alcohol and drugs among 4,586 injured roadway users (drivers, riders, and passengers) in the U.S. from 2019 to 2021, utilizing an XGBoost model to identify key variables associated with Injury Severity Score (ISS) in substance-related traffic crashes, and highlighting influential factors such as injury location, demographic characteristics (age, race), safety compliance, and alcohol and drug presence. These risk factors were further analyzed through Cluster Correspondence Analysis (CCA) to reveal patterns and trends affecting injury severity across different demographic and behavioral groups. The findings reveal that 55.8% of the injured tested positive for substances, with cannabinoids being the most common, followed by alcohol, stimulants, and opioids. This study identified six core crash clusters, each with distinct characteristics, including older drivers, impaired young drivers, specific driver ethnicities, and motorcyclists. Key findings from clusters indicate that older drivers, despite high safety compliance and negative substance tests, faced crash risks potentially due to age-related limitations. Impaired young adult crashes arecharacterized by risky behavior, including alcohol and THC use combined with low safety compliance, while motorcyclists with high substance involvement and inconsistent helmet use, are identified as a high-risk group, frequently experiencing severe leg injuries. These insights underscore the need for comprehensive traffic safety policies targeting substance use and promoting effective safety measures to mitigate crash risks and improve road safety.

Analysis of Black Site and Black Spot at Jl. Daeng Manambon-Raden Kusno-Gusti Sulung Lelanang Section, Mempawah

Transportation plays a vital role in society by facilitating the movement of people and goods. However, it also brings challenges like traffic congestion and accidents. With urbanization and increased vehicles, road safety has become crucial, as accidents have significant economic and social impacts. Analyzing accident-prone areas helps identify necessary preventive measures. This research was conducted on the 10.1 km Jl. Daeng Manambon-Raden Kusno-Gusti Sulung Lelanang section in Mempawah. Primary data from field surveys includes road conditions, fittings, geometry, and vehicle speeds. Secondary data is traffic accident data (2018-2023) from the Mempawah City Police, analyzed using numerical methods.The Jalan Daeng Manambon-Raden Kusno-Gusti Sulung Lelanang corridor analysis reveals several accident-prone areas (Black Spots) with damaged road features and poor safety facilities. Key findings include deteriorating zebra crossings, speed traps, and traffic signs. High-risk accident locations were identified using Z-Score and Cusum methods. Recommendations include repairing roads, repainting markings, installing better signage, and maintaining clear sightlines. Future studies should focus on more specific interventions and monitoring to improve road safety, following Department of Settlements and Regional Infrastructure guidelines for effective accident reduction

The impact of the Korean safe rates system on work environment and road safety

Abstract The regulation of road transport supply chains has recently become a subject of international concern. A significant body of research shows that ensuring minimum rates of pay for truck drivers is important, not only to workers’ health and safety but also to achieving improved road safety outcomes and other policy objectives related to the viability of the road transport industry. For this study, we used an empirical analysis to quantitatively demonstrate the impact of South Korea’s Safe Rates System on factors of the work environment and driver behaviours that affect road safety. Specifically, we looked at the impact of the system on market structure, truck drivers’ monthly net income, average working time, time spent on maintenance, and the frequency of overloading, speeding, and drowsiness while driving. Our results suggest that increased rates of pay for owner truck drivers resulting from the implementation of the Safe Rates System led to a reduction in the average number of contracting steps between client companies who ship goods and the truck drivers who transport them, improvement in the level of market transparency, increased monthly income, decrease in total time worked, decrease in the frequency of overloading, speeding, and drowsiness while driving, and increase in time spent on vehicle maintenance. The level of compliance with the system by clients and transport companies also had an overall positive impact on the industry structure. By empirically demonstrating the system’s impact, our results provide statistical evidence useful to the debate on reintroduction of the Safe Rates System in Korea.

Road traffic injuries and fatalities in Dominican Republic, 2017-2021: a neglected epidemic

Background: Road traffic injuries and fatalities are a major public health concern in the Dominican Republic. Despite efforts to improve road safety, the number of deaths and injuries remains high. This study aims to analyze the epidemiology of road traffic fatalities and injuries in the country from 2017 to 2021. Methods: Data on road traffic injuries and fatalities were collected from the National Police Department, the Ministry of Health, and hospitals across the country. Descriptive statistics were used to analyze the data, including age, sex, and type of vehicle involved in the crash. Results: During the study period, there were 513,629 road traffic injuries and 14,692 fatalities in the Dominican Republic. The majority of those involved in crashes were males aged between 20-39 years old. Motorcycles were the most common type of vehicle involved in crashes. There was a significant reduction in the number of injuries and deaths in 2020, likely due to mobility restrictions during the COVID-19 pandemic. Conclusions: Road traffic injuries and fatalities in the Dominican Republic are a neglected epidemic that requires immediate attention. Improving road safety measures, targeting high-risk groups, and implementing safer pedestrian infrastructure are essential to reducing the number of injuries and deaths on the country's roads.

Exploring factors influencing pedestrian accident severity: a multi-source study in the city of Berlin

BackgroundRoad safety remains a critical concern in Europe’s transport sector. In urban areas, where 40% of total road fatalities occur, pedestrians are particularly vulnerable, since according to road safety data accidents involving a pedestrian are 2.8 and 15 times more probable to be fatal than in rural areas and motorways respectively. In line with the Vision Zero concept and the European Commission’s directives, many EU members are taking steps to improve road safety. Accordingly, the identification of the key factors behind pedestrian accident occurrence and pedestrian accident severity in urban areas is ever more relevant.MethodologyThe proposed methodology employs traditional logistic regression models and artificial neural networks, using accident data from Berlin, Germany, sourced from the Berlin Open Data portal. The dataset comprises information on 3,257 accidents involving pedestrians in 2018 or 2019, including details about involved vehicles, accident details, and injury severity information. Additionally, the dataset was augmented with average network speed data from Uber Movement and road network information from Geographic Information System (GIS) applications.ResultsFormal analysis results indicated several factors as significant to accident severity, such as involvement of bicycle or heavy vehicles, lighting conditions, speed limit and accident type. Additionally, a comparison between modelling approaches shows a clear performance advantage of ANNs over statistical models. Research findings provide insight for various stakeholders working to enhance pedestrian safety in urban areas.

Analyzing the Relationship Between User Feedback and Traffic Accidents Through Crowdsourced Data

Identifying road segments with a high crash incidence is essential for improving road safety. Conventional methods for detecting these segments rely on historical data from various sensors, which may inadequately capture rapidly changing road conditions and emerging hazards. To address these limitations, this study proposes leveraging crowdsourced data alongside historical traffic accident records to identify areas prone to crashes. By integrating real-time public observations and user feedback, the research hypothesizes that traffic accidents are more likely to occur in areas with frequent user-reported feedback. To evaluate this hypothesis, spatial autocorrelation and clustering analyses are conducted on both crowdsourced data and accident records. After defining hotspot areas based on user feedback and fatal accident records, a density analysis is performed on such hotspots. The results indicate that integrating crowdsourced data can complement traditional methods, providing a more dynamic and adaptive framework for identifying and mitigating road-related risks. Furthermore, this study demonstrates that crowdsourced data can serve as a strategic and sustainable resource for enhancing road safety and informing more effective road management practices.

AI-based Traffic Detection and Autonomous Signal Operation

This survey paper presents a comprehensive overview of AI-based traffic detection and autonomous signal operation systems, focusing on the integration of advanced technologies such as cameras, Raspberry Pi, and image processing algorithms, particularly YOLO (You Only Look Once). With the rapid urbanization and the increasing number of vehicles, efficient traffic management has become crucial for improving road safety and reducing congestion. Our survey examines various methodologies employed in traffic detection, including real-time monitoring and classification of vehicles, pedestrians, and cyclists. We analyze the effectiveness of using Raspberry Pi as a low-cost computing platform in conjunction with high-resolution cameras for image acquisition and processing. The paper highlights the performance of YOLO in detecting traffic objects in diverse environments, showcasing its speed and accuracy in real-time applications. Furthermore, we discuss the challenges faced in deploying these systems in real-world scenarios, including environmental variability, data privacy concerns, and the need for robust infrastructure. Finally, we propose future directions for research, emphasizing the importance of integrating machine learning with IOT solutions to create smarter, more responsive traffic management systems. This survey aims to provide insights for researchers and practitioners in developing sustainable traffic solutions leveraging AI technologies.

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.

Governmental Expenditures and the Challenges to Improve Road Safety in Brazil

This article addresses governmental actions to improve road safety through the analysis of road traffic fatalities and expenditures in education, engineering, and enforcement (the 3E’s approach) in Brazil between 2011 and 2021 from the Fundo Nacional de Segurança e Educação de Trânsito (Funset), a public fund to support actions in safety, education, and engineering to improve Brazilian road safety. Graphical analysis was conducted, Pearson correlation coefficient was used for the variables of Funset expenditures (lnpay) and road deaths in Brazil (lndeaths), and a linear regression model was built to estimate the variation in road fatalities as a function of Funset expenditures. The results show a decrease in road deaths in Brazil with a slight upward trend and a reduction in Funset expenditures for road safety improvement. There is a positive correlation between the downward trend in Funset public investments in education, engineering, and enforcement and fatalities on roads of 0.79. Funset expenditures are inadequately allocated with low expenditure execution average (16.8%). There are many challenges for the Brazilian public administration to ensure road safety, however, it is important to maintain financial support for road safety strategies to improve it and save lives.

Accident Prevention System for Cars, Buses, Trucks

Driver drowsiness is a significant factor in road accidents, contributing to a large number of crashes annually. To enhance road safety, the development of a Drivers Drowsiness Detection System is essential. This system builds to detect early signs of lazyness in drivers and provide timely alerts to prevent accidents. The proposed system uses to track key indicators of drowsiness, such as eye closure, head position, yawning, and blink rate. By utilizing a camera-based real-time monitoring system, it continuously analyzes the driver’s facial expressions. The system employs cadcade algorithms to distinguish a featurebased object detection algorithm to detect objects from images between alert and drowsy states based on collected data. When drowsiness is detected, the system triggers alerts like audio warnings, seat vibrations, or even vehicle slowing mechanisms, depending on the integration. This technology has the potential to significantly reduce the number of road accidents related to driver fatigue and could be applied in both personal vehicles and commercial fleets. The implementation of such systems represents a critical step toward improving road safety and decreasing driver-related incidents. Nowadays it is very challenging to stay active all the time due to busy schedules. Falling asleep while driving can lead to serious consequences, accidents, and even death. This situation is much more common and therefore it is very important to solve this problem. So, to solve this problem, we developed a sleep alarm system for drivers. This system alerts the user when he falls asleep at the wheel, thus the main concern is preventing accidents and saving lives. This system is useful for long- distance travelers and late-night drivers.

Exploring Smart Mobility Potential in Kinshasa (DR-Congo) as a Contribution to Mastering Traffic Congestion and Improving Road Safety: A Comprehensive Feasibility Assessment

The urban landscape of Kinshasa, Democratic Republic of Congo, faces significant mobility challenges, primarily stemming from rapid urbanization, overpopulation, and outdated infrastructure. These challenges necessitate the exploration of modern smart mobility concepts to improve traffic flow, road safety, and sustainability. This study investigates the potential of solutions such as Mobility-as-a-Service, car sharing, micro-mobility, Vehicle-as-a-Service, and electric vehicles in addressing these challenges. Through a comparative analysis of global implementations, this research identifies key success factors and barriers that inform the feasibility of integrating these solutions into Kinshasa’s unique socio-political and infrastructural context. The study presents a conceptual framework, supported by stakeholder analysis, for adapting these solutions locally. A detailed feasibility analysis considers technological, economic, social, environmental, and regulatory factors, offering a clear roadmap for implementation. Drawing on lessons from cities facing similar urban mobility challenges, the paper concludes with actionable recommendations and insights for policymakers and urban planners in Kinshasa. This research not only highlights the viability of smart mobility solutions in Kinshasa but also contributes to the broader discourse on sustainable urban development in rapidly growing cities. While smart mobility studies have largely focused on cities with developed infrastructure, there is a gap in understanding how these solutions apply to cities like Kinshasa with different infrastructural and socio-political contexts. Previous research has often overlooked the challenges of integrating smart mobility in rapidly urbanizing cities with underdeveloped transportation systems and financial constraints. This study fills that gap by offering a feasibility analysis tailored to Kinshasa, assessing smart mobility solutions for its traffic congestion and road safety issues. The smart mobility solutions studied—Mobility-as-a-Service (MaaS), car sharing, electric vehicles (EVs), and micro-mobility—were chosen for their ability to address Kinshasa’s key mobility challenges. MaaS reduces reliance on private vehicles, easing congestion and improving public transport. Car sharing offers affordable alternatives to vehicle ownership, essential in a city with income inequality. EVs align with sustainability goals by reducing emissions, while micro-mobility (bikes and e-scooters) improves last-mile connectivity, addressing public transit gaps. These solutions are adaptable to Kinshasa’s context and offer scalable, sustainable improvements for urban mobility.

“Interior lighting can help to improve road safety”

“Interior lighting can help to improve road safety”