Enhancing road safety: a comprehensive examination of critical factors

India needs shift in thinking to improve road safety


 
 
 Traffic and road safety is a major concern of many governments around the world as it ensures the safety of its road users. Due to an increase in the number of road users, traffic-related accidents and fatalities have increased annually. Therefore, governments have stepped up their efforts to ensure the safety of road users and to reduce the number of traffic-related accidents and fatalities. However, these efforts have proven ineffective as the data gathered to measure the effectiveness of these efforts either lack credibility or are insufficient. This has led to inconclusive findings concerning the culture of road users and the extent of their awareness of traffic safety. Therefore, this paper performs a systematic data- and text-driven analytics and review of published literature on public awareness of traffic and road safety. The selected papers were first subjected to content analysis before being grouped according to the frequency of use of keywords discussing the issue at hand. As this method enabled us to successfully identify trends as well as the efficacy of traffic and road safety research, therefore, data and text analytics is an effective method of conducting a systematic literature review. The findings of this study will help traffic authorities gain credible insights into public perception, behavior, and attitude towards traffic safety awareness. Our assessment indicates that there are two important perspectives to be considered with regard to road safety; namely human and technology; the latter of which will be the main topic of study on public awareness of road and traffic safety. Moreover, an emerging trend in the literature suggests that more efforts should be allocated to promoting public awareness of road and traffic safety.
 
 

Highway curve is prone sections accidents, this paper studies the curve road traffic safety situation based traffic safety evaluation in curves, and it uses the vehicle lateral stability to analyze curve road traffic safety features. It discusses TruckSim and Simulink co-simulation problems and establishes TruckSim vehicle simulation model, Vehicle simulation model is established in TruckSim, though the evaluation is designed in Simulink. Geometry radius curve indicators of the current specification limit road for alignment design is analyzed and verifies the reasonableness by co-simulation. The simulation results show that it is very beneficial to use a larger bend radius for road safety under conditions permit. Drivers only need to slightly adjust the steering angle so that the vehicle can pass the curve sections in larger bend radius, it not only reduces the driver’s driving strength, but also avoids the risk of vehicle skidding or roll. The results provide a reference for future vehicle safety and road safety designs.

:Background:Road traffic accidents have been one of the main causes of death worldwide. However, most of the crashes are both predictable and preventable. With the widespread use, mobile phone is considered as a major road safety risk. However, varieties of apps have recently been developed for improving the road traffic health and safety. Despite an increasing trend of these apps, there is no comprehensive analysis of their features and no taxonomy or classification of them based on the traffic safety theories or frameworks. The aim of this study was to explore characteristics of available mobile apps on the road traffic health and safety and classify them based on the Haddon’s Matrix.Methods:To conduct this research, a comprehensive and systematic review of the mobile applications developed for the road traffic health and safety was carried out through using the qualitative content analysis. Google Play was searched through using a combination of the keywords to retrieve the road traffic apps. In order to extract the app features, their description was examined, and their content was analyzed. Then they were classified in four main categories including Road Traffic Health & safety, Road Traffic Training, Road traffic Navigation, and Other Road Traffic apps. Finally, the Haddon’s matrix was applied to analyze and classify those mobile apps residing in two main categories of the traffic health & safety, and the traffic training. Haddon’s matrix is a relevant framework for structured analyses of traffic injury events. In the Haddon’s matrix, the contributions of human, vehicle/equipment and environmental factors to the injuries in the three phases pre-crash, crash and post-crash has been presented.Results:In this study, 916 mobile apps met the inclusion criteria and were included in the final analysis.19 subcategories were identified for classifying the included apps based on their features and functionalities. In total, 620 of the mobile apps were grouped on the basis of Haddon’s matrix. About 61.76% of these apps were categorized as the traffic health & safety group. Among the subcategories of the traffic health & safety group, the highest number of apps (194) was the real-time traffic alerting apps. Behavior based feed-backing subcategory was ranked the second among the traffic health and safety apps with 75 apps.When the researchers applied factors and phases of Haddon’s framework for analyzing the apps, it was found that the Haddon’s factors have been taken into account in the apps grouped into two main categories of the apps including traffic training and traffic health & safety applications. The highest percentage of Haddon’s matrix factor considered for the intervention through the apps was related to the physical & social environment (23.69%) in the traffic training category. The features of 259 apps were classified in the Event/ Driving phase of Haddon’s matrix. Most of the apps in this phase was related to the real-time traffic alerting subgroup. These apps performed in real time while driving. The lowest number of the apps in driving phase was observed in the behavior based feed backing subcategory, which inform drivers about their driving behavior in real-time. Sixteen apps had features that categorized them in both Pre-event / Pre-driving and Event / Driving phases. In addition, 20 apps were found to have feature for intervening in both Event / Driving and Post-event / Post –driving phases. All 235 apps in the traffic training category were in pre-event/ pre-driving phase, which requires users to use these apps before driving.Conclusions:Applying Haddon’s matrix on analyzing and classifying the traffic health and safety apps revealed strengths and weaknesses of the existing related mobile apps in terms of the factors that must be considered for intervention toward prevention and reduction of road traffic accidents as the main public health issue.Keywords:Mobile apps, Traffic accident prevention apps, Traffic safety, Haddon matrix, Public health

Road traffic crashes (RTCs) are considered one of the major public health issues in many countries worldwide. Investigating factors of traffic crashes, accidents, and disasters can facilitate and aid in identifying measures to mitigate their frequency and severity as well as occurrence and impact, thereby enhancing road safety. This study aims to investigate the factors that contribute to road traffic accidents in the Gaza Strip, Palestine. A comprehensive questionnaire, comprising four sub-groups of factors: human, vehicle, road, and environmental, collected data from a sample of respondents, including drivers, passengers, pedestrians, and other road users. The study utilized the relative importance index (RII) to analyze and classify each factor group after analyzing 385 questionnaires in SPSS. Over 83% of respondents across all age categories identified overspending as the most significant factor causing road traffic crashes, according to the study’s findings. Additionally, more than 78% of respondents believed that weak braking or brake failure was the highest and most significant vehicle-related factor contributing to road traffic crashes. Among the road factors, 75.4% of respondents identified low or no street illumination as a significant contributor to road traffic crashes, ranking it as the fourth most influential element overall, while environmental factors had the least impact compared to other variables.

In recent years, significant advancements in vehicle technology have spurred growing interest in Vehicular Ad hoc Networks (VANETs). This interest is driven by concerns for road safety and the need to alleviate network congestion, leading to the emergence of Intelligent Transport Systems (ITS). ITS focuses on improving road traffic management and safety through the utilization of wireless and mobile network communication technologies. VANETs play a pivotal role within the realm of ITS, facilitating tasks such as enhancing road safety, traffic monitoring, and ensuring passenger comfort by mitigating accidents and congestion. These objectives rely on the timely and accurate delivery of data to vehicle agents and relevant authorities, facilitated by reliable VANETs and Road Signal Units (RSUs). Achieving this necessitates identifying optimal routes with minimal distance, high radio access, and quality-awareness levels. To address these objectives, this study proposes the utilization of the Congestion Network with Predicted K-means multi-hop RSU algorithm (CN-MHMR) to enhance vehicular networking and communication. This algorithm facilitates efficient node transfer from base nodes to destination nodes via the shortest and energy-efficient paths, thereby enabling viable and reliable vehicular communications. The performance of the proposed model was evaluated based on various metrics, including energy consumption, throughput, delay, packet delivery ratio, accuracy, precision, and recall values.

Introduction: In today's fast-paced technology, road safety demands intelligent, efficient response systems.An advanced Road Accident Detection System by combining Generative Adversarial Networks (GANs) for real-time image dehazing with YOLOv11 for precise object detection. The GAN-based model enhances visibility in adverse weather, enabling accurate accident detection even in low-visibility scenarios. YOLOv11 effectively identifies various objects, including vehicles, pedestrians, and collision events. Upon detecting an accident, the system triggers automated emergency notifications, sending real-time alerts with precise location details to responders such as police stations, hospitals, Regional Transport Offices (RTOs) and traffic management authorities, facilitating prompt traffic clearance. By minimizing response time, this system enhances survivability rates. Extensive experimental evaluations confirm its robustness across diverse environmental conditions, outperforming traditional dehazing and object detection methods, thereby improving accident detection and optimizing emergency response mechanisms. Objectives: The objective of this project is to develop a real-time road accident detection system using GAN-based dehazing and YOLOv11 for precise object detection. The system enhances visibility in adverse weather conditions and ensures quick emergency response by automatically notifying relevant authorities, reducing response time, and improving survivability rates. Methods :The proposed system integrates Generative Adversarial Networks (GANs) for real-time image dehazing and YOLOv11 for high-speed object detection. Video frames from surveillance cameras or dashcams are preprocessed using GAN-based dehazing to improve visibility under adverse weather conditions. A transformer-based attention mechanism prioritizes critical areas for detection, enhancing precision. When an accident is detected, the system triggers automated emergency notifications with precise location details, alerting police stations, hospitals, Regional Transport Offices (RTOs), and traffic management authorities. This approach ensures rapid response, minimizing casualties Results: The proposed system effectively enhances road accident detection by integrating GAN-based dehazing and YOLOv11 object detection. The dehazing model improves image clarity, allowing for better feature extraction in low-visibility conditions, while YOLOv11 ensures accurate identification of vehicles, pedestrians, and collision events. The system significantly reduces false positives and enhances detection accuracy compared to traditional methods. Additionally, the automated emergency notification mechanism enables faster response times, demonstrating the model’s reliability in real-world scenarios and its potential for improving road safety and intelligent transportation systems. Conclusions: Enhancing road safety through real-time accident detection plays a vital role in minimizing casualties and improving emergency response efficiency. The developed system ensures accurate accident identification, even in challenging weather conditions. It delivers instant alerts to relevant authorities, significantly improving response time and making it a reliable solution for road safety and emergency management.

Car accidents remain a significant public safety issue worldwide, with the majority of them attributed to driver errors stemming from inadequate driving knowledge, non-compliance with regulations, and poor driving habits. To improve road safety, Driving Behavior Detection (DBD) systems have been proposed in several studies to identify safe and unsafe driving behavior. Many of these studies have utilized sensor data obtained from the Controller Area Network (CAN) bus to construct their models. However, the use of publicly available sensors is known to reduce the accuracy of detection models, while incorporating vendor-specific sensors into the dataset increases accuracy. To address the limitations of existing approaches, we present a reliable DBD system based on Graph Convolutional Long Short-Term Memory Networks (GConvLSTM) that enhances the precision and practicality of DBD models using public sensors. Additionally, we incorporate non-public sensors to evaluate the model's effectiveness. Our proposed model achieved a high accuracy of 97.5\% for public sensors and an average accuracy of 98.1\% for non-public sensors, indicating its consistency and accuracy in both settings. To enable local driver behavior analysis, we deployed our DBD system on a Raspberry Pi at the network edge, with drivers able to access daily driving condition reports, sensor data, and prediction results through a monitoring dashboard. Furthermore, the dashboard issues voice warnings to alert drivers of hazardous driving conditions. Our findings demonstrate that the proposed system can effectively detect hazardous and unsafe driving behavior, with potential applications in improving road safety and reducing the number of accidents caused by driver errors.

Road safety is a crucial global concern because of the high number of fatalities and injuries resulting from road crashes each year. Median crossover collisions are among the most dangerous crashes that happen on highways, frequently leading to serious or fatal injuries. The main approach to decreasing the occurrence of these types of crashes is the installation of median barriers. When the need for such installations arises, road agencies must choose from various options, including concrete barriers, cable barriers, or metal-beam guardrails. This paper is dedicated to the New Dynamic Barrier for Highways (NDBA 2.0), an innovative technology for median barriers developed by the Italian National Road Agency (ANAS), emphasizing its pivotal role in enhancing road safety. It incorporates high-tensile steel and advanced composites, offering robust protection while maintaining a lightweight profile. What distinguishes the NDBA 2.0 is its dynamic nature, featuring an intelligent system that seamlessly adapts to the road infrastructure. Its modular construction, with sections of only 200 cm, allows for easy installation and ensures compatibility across successive road segments. This adaptability reduces construction time while maintaining the highest standards of performance. From a road safety perspective, the NDBA 2.0 offers substantial advantages. Its design contributes to minimizing crash-related costs by reducing the severity of crashes, particularly in the transition zones. The barrier's design allows it to adapt to varying road conditions and traffic volumes, effectively addressing common installation challenges on existing roadways as well. Its ability to be directly supported on the road surface wear layer eliminates the need for costly foundation structures, facilitating quick installation and reducing maintenance expenses. The NDBA 2.0 barrier was designed to eliminate the need for future simulations in the design and verification of transitions between different barriers. For this reason, the NDBA 2.0 barrier has been tested in real-world conditions in class H4 and, consequently, is equipped with CE marking. This study offers a comprehensive analysis of the NDBA 2.0 barrier, whose implementation may provide significant benefits for road safety. Continued research, collaboration, and widespread adoption of the NDBA 2.0 barrier can further enhance road safety on a global scale.

Advancements in traffic simulation for enhanced road safety: A review

Comprehensive investigation of crashes associated with citywide speed limit reduction in Seattle, Washington

Background: Road traffic accidents have been one of the main causes of death worldwide. However, most of the crashes are both predictable and preventable. With the widespread use, mobile phone is considered as a major road safety risk. However, varieties of apps have recently been developed for improving the road traffic health and safety. Despite an increasing trend of these apps, there is no comprehensive analysis of their features and no taxonomy or classification of them based on the traffic safety theories or frameworks. The aim of this study was to explore characteristics of available mobile apps on the road traffic health and safety and classify them based on the Haddon’s Matrix. Methods: To conduct this research, a comprehensive and systematic review of the mobile applications developed for the road traffic health and safety was carried out through using the qualitative content analysis. Google Play was searched through using a combination of the keywords to retrieve the road traffic apps. In order to extract the app features, their description was examined, and their content was analyzed. Then they were classified in four main categories including Road Traffic Health & safety, Road Traffic Training, Road traffic Navigation, and Other Road Traffic apps. Finally, the Haddon’s matrix was applied to analyze and classify those mobile apps residing in two main categories of the traffic health & safety, and the traffic training. Haddon’s matrix is a relevant framework for structured analyses of traffic injury events. In the Haddon’s matrix, the contributions of human, vehicle/equipment and environmental factors to the injuries in the three phases pre-crash, crash and post-crash has been presented. Results: In this study, 916 mobile apps met the inclusion criteria and were included in the final analysis.19 subcategories were identified for classifying the included apps based on their features and functionalities. In total, 620 of the mobile apps were grouped on the basis of Haddon’s matrix. About 61.76% of these apps were categorized as the traffic health & safety group. Among the subcategories of the traffic health & safety group, the highest number of apps (194) was the real-time traffic alerting apps. Behavior based feed-backing subcategory was ranked the second among the traffic health and safety apps with 75 apps. When the researchers applied factors and phases of Haddon’s framework for analyzing the apps, it was found that the Haddon’s factors have been taken into account in the apps grouped into two main categories of the apps including traffic training and traffic health & safety applications. The highest percentage of Haddon’s matrix factor considered for the intervention through the apps was related to the physical & social environment (23.69%) in the traffic training category. The features of 259 apps were classified in the Event/ Driving phase of Haddon’s matrix. Most of the apps in this phase was related to the real-time traffic alerting subgroup. These apps performed in real time while driving. The lowest number of the apps in driving phase was observed in the behavior based feed backing subcategory, which inform drivers about their driving behavior in real-time. Sixteen apps had features that categorized them in both Pre-event / Pre-driving and Event / Driving phases. In addition, 20 apps were found to have feature for intervening in both Event / Driving and Post-event / Post –driving phases. All 235 apps in the traffic training category were in pre-event/ pre-driving phase, which requires users to use these apps before driving. Conclusion: Applying Haddon’s matrix on analyzing and classifying the traffic health and safety apps revealed strengths and weaknesses of the existing related mobile apps in terms of the factors that must be considered for intervention toward prevention and reduction of road traffic accidents as the main public health issue.

To achieve significant improvements, there is a need for a systematic approach in Road Safety Management. The first step of such approach is to define the burden of road casualties in the country. Part of defining the burden is the monetary valuation of the prevention of a fatality accident. Such “values of statistical life” are derived in a number of countries, however, there is a large variation among these. Interestingly, a negative correlation seems to exist between the monetary valuation of a statistical life and the road safety situation of a country, namely, countries with low “values of statistical life” have poor road safety situation and vice versa. The next step is to get commitment from decision makers. There is an accumulated knowledge among road safety scientist on what strategies and measures are the most efficient, but without commitment from decision makers, these will not will not be realized. A good example to illustrate this is the case of France, when the President of the country “suddenly” became “… absolutely horrified that French roads are the most dangerous in Europe…”, and ordered tough road safety measures resulting in unpreceded improvements in France’s road safety situation. With the commitment of decision makers, a Road Safety Policy can be established. One example for such policy is the Vision Zero in Sweden, whose parliament passed the Road Traffic Safety Bill on Vision Zero in 1997. Another example is the “Sustainable safety” policy in the Netherlands. Another crucial issue is the creation of a responsible body for road safety on the national level. A National Road Safety Committee/Commission should include the relevant departments, and most importantly be chaired by a committed person, respected by all parties. Before jumping to road safety actions and countermeasures, road safety problems should be identified in a systematic way. When presenting the road safety situation for a country or region, often used risk values build on the number of casualties related to some exposure measure. Some of these exposure measures (number of motor vehicles or motor vehicle kilometres) give a wrong depiction of the situation as they neglect the existence of pedestrians and bicyclists who carry the casualty burden of motorised traffic. To be able to make informed decisions on road safety measures based on accident statistics, we need exposure data for all road user types. Access to exposure data allows us to perform the so called 3-dimensional analysis of road safety problems. The dimensions “exposure” (person-kilometres per year), “risk” (number of injury accidents per person-kilometres per year) and “consequence” (number of killed per number of injury accidents per year) help us to get a good picture of the character of the road safety problems. However, even if this kind of approach gives a good tool for knowledge-based road safety work, several problems exist with using only accidents to analyse the road safety situation; the most obvious of them is that many accidents are never reported. The less the injury severity, the lower the reporting grade. The conclusion from this is that we need to combine Police register with Hospital register on traffic injuries. What’s more, to analyse the traffic safety situation, we also should use non-accident based Safety Performance Indicators. With knowledge of the pattern of road safety problems, Road Safety Targets can be formulated. The targets should embrace targets for road user behaviour, road infrastructure, vehicles and rescue services. A road safety vision without quantified targets will not be effective. Only countermeasures with known effectiveness should be applied. There is an accumulated experience of effectiveness of a large number of road safety countermeasures published in the Handbook of Road Safety Measures. Finally, a very important issue – most often neglected after the introduction of a countermeasure - Monitoring of performance. Data on Safety Performance Indicators should be collected continuously and their status should be followed-up yearly and in the event of deviation from the target, relevant measures should bel applied promptly.

This study developed a comprehensive maintenance management model aimed at reducing power consumption in grinding mills and kilns within the cement industry, using Kisarawe Cement Plant in Tanzania as a case study. The research addresses the critical issue of excessive energy consumption in cement manufacturing, where grinding and kiln operations account for approximately 70-80% of total electrical energy usage, significantly impacting production costs and environmental sustainability (Madlool et al., 2011; World Business Council for Sustainable Development, 2022). The study employed a quantitative case study approach, combining field data collection, analysis of equipment performance records, and expert interviews with maintenance personnel. Statistical analysis, utilising the Relative Importance Index (RII) methodology, was applied to identify and rank maintenance factors affecting power consumption (Kumar et al., 2022). Multiple regression analysis was employed to develop predictive relationships between maintenance practices and energy efficiency (Vincent et al., 2024). Data was collected from 101 equipment records across multiple operational periods, with comprehensive validation conducted over a 12-month timeframe. The analysis revealed twelve critical maintenance-related factors, with six achieving very high priority status (RII > 0.9), including kiln coating/ring formation issues (RII = 0.927), fan/blower impeller fouling (RII = 0.921), and worn grinding media in ball mills (RII = 0.921). A statistically robust mathematical model was developed with exceptional predictive capability (R² = 0.99), incorporating seven key maintenance factors: worn grinding media, misaligned mill drives, motor bearing deterioration, poor lubrication systems, kiln coating issues, fan impeller fouling, and kiln refractory degradation (Nakajima, 1988; Moubray, 2001). The model equation: Power Reduction (%) = 0.11 + 0.032×WGM + 0.05×MMD + 0.05×MBD + 0.03×PLS + 0.025×KCR + 0.011×FBF - 0.06×KRD demonstrates that targeted maintenance interventions can achieve significant energy savings. Validation results showed remarkable accuracy with 98% predicted versus 98% actual availability performance, confirming the model's practical applicability in industrial settings (Law et al., 2007). Implementation of the proposed maintenance management model led to measurable improvements in energy efficiency, reduced operational costs, and extended equipment lifecycles. The research contributes to both academic knowledge and industrial practice by providing cement manufacturers with a scientifically validated, quantitative framework for implementing energy-focused maintenance strategies that address root causes of power inefficiency while maintaining operational reliability and production quality standards.

Road safety is a crucial issue in developing Indonesia's transportation infrastructure. Jalan Teuku Umar in Tambun, Bekasi, is one of the primary routes connecting Bekasi with neighboring areas, yet it experiences a high rate of traffic accidents. Accidents on this road, driven by user behavior, inadequate road conditions, and frequent traffic violations, lead to significant material losses and, in some cases, severe injuries or fatalities (Salvapour et al., 2020). This study aims to analyze factors contributing to the high accident rate on Jalan Teuku Umar and proposes measures to enhance traffic safety. Previous research highlights driver behavior as a primary cause of accidents, with frequent violations such as red light running, speeding, and lack of attention to pedestrians (Moshki et al., 2019; Sullman et al., 2012). Additionally, infrastructure issues, including inadequate pedestrian facilities and irregular traffic flow, further compromise road safety (Demiroz et al., 2015). This research focuses on identifying risk factors specific to Jalan Teuku Umar and recommends solutions to mitigate accidents and improve safety. The study employs a quantitative, descriptive approach, incorporating field observations, surveys, and accident data analysis. A survey was conducted with 200 respondents, including drivers and pedestrians, to assess traffic behavior and safety awareness. Secondary data from police reports over three years provided insight into accident patterns and dominant causes (Sullman et al., 2012). Descriptive analysis of the collected data highlights the correlation between safety awareness, road conditions, and accident frequency. Key findings indicate that traffic violations, limited geometry road capacity, the dominance of motorcycles, inadequate road use, and the impact of land use all significantly contribute to the high accident rate. Traffic violations, helmet particularly non-use, unlit headlights, and seatbelt neglect, account for most accidents, aligning with similar findings in other areas (Kurniastuti et al., 2021). Motorcycles dominate the traffic flow, increasing fatal accident risks due to low helmet use. High-density commercial activities also create complex traffic interactions. Recommendations include stricter law enforcement on violations, infrastructure improvements, road capacity expansion, and educational campaigns on traffic safety. Regular road geometry maintenance, improved signage, and better land use management are essential to reduce accidents on Jalan Teuku Umar. This study contributes to policy development for enhancing road safety in Bekasi and similar urban areas by providing a comprehensive analysis of risk factors and proposing targeted interventions.