Road Safety Measures Research Articles

Multilevel structural equation modeling of willingness-to-pay for fatality risk reduction: perspectives of driver and district levels

Road accidents remain a serious problem and directly affect drivers. Therefore, the perspectives of drivers are important in improving road safety. The objectives of this study are to empirically examine damage due to road accidents using the willingness-to-pay (WTP) approach and to analyze the factors that influence WTP at the driver and district levels. This study obtained data on WTP derived from car drivers across Thailand, which covers 96 districts. The value of statistical life was 824,344 USD per fatality (2,296 million USD annually). The results of Multilevel Structural Equation Modeling revealed a statistically important insight. At the driver level, the Health Belief Model and sociodemographic exert influence on the intention to pay. The demographic factor that has the greatest influence on perceived risk and leads to a high intention to pay is the working age group (γ = 0.826). However, when considering the HBM, perceived susceptibility (γ = 0.901) emerges as the most valuable factor influencing drivers’ concerns about road accidents. On the other hand, district-level factors have a negative influence on the intention to pay for road safety measures. Among these factors, the law enforcement (γ = −0.555) practices implemented by local authorities have the most significant impact on drivers’ perspectives and intentions regarding WTP. This finding can be used as a guideline for budget allocation and policy recommendation for policymakers in improving road safety according to the area contexts.

Development of German pedelec (and bicycle) crashes between 2013 and 2021

Introduction: The continuous growth in the use of e-bikes (in Germany mostly pedelecs that support pedaling up to 25 km/h) raises questions about the use of historic crash data for the development of road safety measures. The aim of this study was to address this issue, by conducting a longitudinal analysis of pedelec and bicycle crash data over a period of nine years to identify trends and to clarify whether such trends are specific to pedelecs. Method: We analyzed 95,338 police reported pedelec and bicycle injury crashes from 2013 through 2021. The dataset consisted of crashes from three federal states of Germany: Brandenburg, Hesse and Saxony. Data were analyzed with respect to sex and age distribution, time, location and type of accident, conflict partner, cause of crash and injury severity. Results: Many of the analyzed variables showed a considerable degree of temporal stability, with differences as well as similarities between the two bicycle types staying quite consistent over the years. One notable difference was the mean age of the involved riders, with crashed pedelec riders being significantly older than conventional cyclists. At the same time, however, the mean age of these pedelec riders has decreased by eight years over time. Single vehicle crashes were consistently more common for pedelec riders than for cyclists. Similarly, pedelec rider crashes went with a higher injury severity over all the years. Conclusions and practical applications: While, on a more detailed level, we found differences between the two bicycle types, overall crash characteristics were remarkably similar and consistent over time. Our findings provide no clear argument for road safety measures that are specifically designed to target pedelecs. Instead, the stable crash total, and the increases in ridership of both bicycles and pedelecs, highlight the demand for new, innovative solutions to improve cycling safety in general.

Exploring traffic accidents patterns: Spatial distribution and socio-economic determinants

<p class="Els-keywords">This study employs a machine learning methodology, specifically the decision tree algorithm, in conjunction with the Quantum Geographic Information System (QGIS), to conduct a rigorous analysis of traffic accident data. The research aims to investigate various factors associated with traffic accidents, with a particular emphasis on their spatial distribution and the socio-economic determinants contributing to recurring accidents caused by drivers. The study focuses on the city of Duhok, located in the Kurdistan Region of Iraq, and utilizes a questionnaire to collect data from drivers regarding accident locations and the frequency of accidents within the past decade (from 2010 to 2020). The findings of the study reveal that the city center experiences the highest concentration of accidents, while severe collisions tend to occur in specific “black spots” scattered across the city’s road network. The decision tree model, employed to classify drivers with multiple accidents, identifies the primary causes of accidents as traffic conditions, traffic law violations, and overspeeding. Furthermore, the accident locations are found to be influenced by various factors, including different types of road hierarchy. The age and gender of drivers also contribute to accident patterns. These research findings have practical implications for enhancing road safety measures and reducing the frequency of traffic accidents. The utilization of machine learning techniques, combined with the analysis of spatial data through QGIS, provides a comprehensive understanding of the underlying factors contributing to accidents. Moreover, this research contributes novel insights to the field of road traffic accidents and safety, particularly in the context of the city of Duhok in Kurdistan Region, Iraq, and provides a valuable reference for future studies in the domain of road safety and urban planning.</p>

Big Data Analytics with the Multivariate Adaptive Regression Splines to Analyze Key Factors Influencing Accident Severity in Industrial Zones of Thailand: A Study on Truck and Non-Truck Collisions

Machine learning currently holds a vital position in predicting collision severity. Identifying factors associated with heightened risks of injury and fatalities aids in enhancing road safety measures and management. Presently, Thailand faces considerable challenges with respect to road traffic accidents. These challenges are particularly acute in industrial zones, where they contribute to a rise in injuries and fatalities. The mixture of heavy traffic, comprising both trucks and non-trucks, significantly amplifies the risk of accidents. This situation, hence, generates profound concerns for road safety in Thailand. Consequently, discerning the factors that influence the severity of injuries and fatalities becomes pivotal for formulating effective road safety policies and measures. This study is specifically aimed at predicting the factors contributing to the severity of accidents involving truck and non-truck collisions in industrial zones. It considers a variety of aspects, including roadway characteristics, underlying assumptions of cause, crash characteristics, and weather conditions. Due to the fact that accident data is big data with specific characteristics and complexity, with the employment of machine learning in tandem with the Multi-variate Adaptive Regression Splines technique, we can make precise predictions to identify the factors influencing the severity of collision outcomes. The analysis demonstrates that various factors augment the severity of accidents involving trucks. These include darting in front of a vehicle, head-on collisions, and pedestrian collisions. Conversely, for non-truck related collisions, the significant factors that heighten severity are tailgating, running signs/signals, angle collisions, head-on collisions, overtaking collisions, pedestrian collisions, obstruction collisions, and collisions during overcast conditions. These findings illuminate the significant factors influencing the severity of accidents involving trucks and non-trucks. Such insights provide invaluable information for developing targeted road safety measures and policies, thereby contributing to the mitigation of injuries and fatalities.

Investigating road conditions of crash blackspots in Addis Ababa, Ethiopia: a random parameters negative binomial model

Crash blackspots significantly impact and, to some extent, determines the entire road network’s safety level. Therefore, it is imperative to identify these blackspots and investigate the contributing factors. This becomes particularly crucial for low-income countries facing financial constraints in implementing road safety measures. Methodologically multiple studies utilised random parameter negative binomial models to predict vehicle crashes due to their ability to address unobserved heterogeneity in crash data, surpassing conventional models. However, the potential of this promising method in investigating factors influencing crash blackspots remains underutilised. This study aims to identify crash blackspots and investigates the roadway factors of such segments using the random parameters negative binomial modelling method. A three-year (2017–2019) crash data collected from the Ethiopian capital, Addis Ababa, with traffic volumes and various geometric characteristics were utilised. The model estimation results demonstrate the superiority of the random parameter negative binomial model over conventional models, showcasing its ability to reveal unobserved heterogeneity associated with road condition factors in crash blackspots. The study finds that horizontal curves and access density are significant road condition-related contributors to crash blackspots, characterised as random parameters. On the other hand, fixed-parameter influence factors include average annual daily traffic, vertical gradient, vertical curve, median width, and traffic control devices. The study highlights the need to further explore horizontal curvatures and access control as potential random parameters in crash blackspot locations. The findings may assist transportation planners/agencies in prioritising road maintenance, enhancing design standards, and implementing targeted safety interventions to improve road safety effectively.

RANKING PARENTS CONCERNS ABOUT ROAD SAFETY AT SCHOOL USING AN ANALYTIC HIERARCHY PROCESS

School road safety concerns include the drop-off location for children at the school entrance, traffic congestion around the school, and road safety amenities. Most research is done by analyzing data with descriptive statistics, but these don't give a clear order of the factors that lead to these incidents. So, the Analytic Hierarchy Process (AHP) has been used to help figure out which ways of intervening are the most effective at cutting down on accidents. The following objectives have been outlined: (i) to identify the most critical factors that contribute to road safety measures in the school area, such as high traffic volume, speeding, or a lack of pedestrian infrastructure, and (ii) to prioritize potential interventions for improving road safety. The study's methods include four main steps: making an instrument, collecting data, analyzing the data with AHP, and coming up with a list of actions to take based on priority. Results show that the most significant outcome was 33% peak hour management, and 33% of the needs of parent car management are equally critical. The other concerns are on the children's path. The outcome presented herein gives an insight into how to prioritize roads for safety mitigation, which is expected to be useful to various decision-makers.

Traffic violations analysis: Identifying risky areas and common violations

Road traffic accidents caused by traffic violations are a major public health issue that results in loss of lives and economic costs. Therefore, it is important to prioritize road safety measures that reduce the incidence and severity of accidents. In this study, we suggest an incremental road safety strategy that identifies high-risk areas and common traffic violations in order to prioritize further enforcement. In fact, by analyzing data on traffic violations in different districts and comparing them to the overall average using the Kolmogorov-Smirnov (KS) test, risky areas are identified and the most common violations are detected. We performed a comparison between several types of clustering optimizations to spot clusters to be enforced in order to reduce violations. Our results indicate that some Districts have a higher risk of traffic violations than others do, and some violations (Speeding, Registration, License, Belt, Influence, Phone, etc.) are more common than others are. We also find that k-means clustering provides the best results for identifying clusters of violations records and optimizing enforcement strategies. Our findings can be adopted by law enforcement agencies to focus on high-risk areas and target the most common violations in order to optimize their resources and improve road safety.

Knowledge Mapping with CiteSpace, VOSviewer, and SciMAT on Intelligent Connected Vehicles: Road Safety Issue

The rapid development of the Intelligent connected vehicle (ICV) industry has stimulated technological innovation in energy and communication while also highlighting the need for effective policies and road safety measures. Understanding and addressing road safety issues in the context of ICVs can contribute to ICV development and safe driving. This paper employs a knowledge mapping approach to scientifically and intuitively demonstrate research on the road safety issues of ICV over the last decade. By utilizing bibliometric tools such as CiteSpace, VOSviewer, and SciMAT, a total of 3661 original articles from the Web of Science are examined to explore three aspects. Firstly, the study investigates the collaborative relationships among authors and institutions within the industry. Secondly, it summarizes major research topics by analyzing and clustering keywords. Lastly, the paper identifies research hotspots and predicts future research directions. The findings reveal a dynamic field characterized by close collaboration among diverse institutions, with China and the United States emerging as the most active countries and mathematics and computer science journals becoming mainstream. According to three bibliometric tools, the research topics primarily revolve around three areas: Vehicular ad hoc Networks (VANET), intelligent transportation systems (ITS), and network security. Machine learning and V2X communication are predicted to be essential research topics in the next stage. Research on traffic accidents still has potential as the number of ICVs increases.

Evaluating the effectiveness of machine learning techniques in forecasting the severity of traffic accidents

Traffic accidents pose a significant public safety concern, leading to numerous injuries and fatalities worldwide. Predicting the severity of these accidents is crucial for developing effective road safety measures and reducing casualties. This paper proposes an analytic framework that utilizes machine learning models, including Naive Bayes, Random Forest, Logistic Regression, and Artificial Neural Networks, to predict the severity of traffic accidents based on contributing factors. This study analyzed ten years of UK traffic accident data (2005–2014, N = 2,047,256) to develop and compare different ML models. Results show that the proposed Random Forest and Logistic Regression models achieved an 87% overall prediction accuracy, outperforming Naive Bayes (80%) and Artificial Neural Networks (80%). By employing Random Forest-based feature importance analysis, the study identified Engine Capacity, Age of the vehicle, make of vehicle, Age of the driver, vehicle manoeuvre, daytime, and 1st road class as the most sensitive variables influencing traffic accident severity prediction. Additionally, the suggested RF model outperformed most existing models, attaining a remarkable overall accuracy and superior predictive performance across various injury severity classes. The findings have significant implications for developing efficient road safety measures and enhancing the current traffic safety system. The proposed framework and models can be adapted to various datasets to achieve accurate and effective predictions of traffic accident severity, serving as a valuable reference for implementing traffic accident management and control measures. Future research could extend the proposed framework to datasets containing Casualty Accident information to further improve the accuracy of injury severity prediction.

Examining Car Accident Prediction Techniques and Road Traffic Congestion: A Comparative Analysis of Road Safety and Prevention of World Challenges in Low-Income and High-Income Countries

Road accidents are a significant negative outcome of transportation systems, causing injuries, fatalities, traffic congestion, and economic losses. As cities expand and the number of vehicles on the road increases, traffic accidents (TAs) have become a significant problem. Studies have shown that urban development plays a more significant role in transportation safety than previously thought. Low-income countries have higher fatality rates than high-income countries, according to the Permanent International Association of Road Congress (PIARC) and the World Health Organization (WHO). Predicting and preventing the occurrence of accidents and congestion is necessary worldwide, especially in developing countries where fatality rates are higher. The objective of this study is to examine and make a comparative analysis in low-income and high-income countries of the existing literature on the global challenge of car accidents and use its prediction techniques to enhance road safety and reduce traffic congestion. The study evaluates various approaches such as logistic regression, decision tree, random forest, deep neural network, support vector machine, random forest, K-nearest neighbors, Naïve Bayes, empirical Bayes, geospatial analysis methods, and UIMA, NSGA-II, and MOPS algorithms. The research identifies current challenges, prevention ideas, and future directions for preventing accidents and congestion on the road network. Integrating GIS-based spatial statistical methods and temporal data and utilizing advanced optimization algorithms and machine learning methods can result in accurate prediction models that can help identify accident hotspots and reduce congestions and enhance traffic safety and mitigate their occurrence. Effectively preventing urban traffic congestion requires the integration of spatial data into precise accident prediction models. By employing spatial analysis, road safety planning can be enhanced, high-risk areas can be identified, interventions can be evaluated, and resources can be optimally allocated to facilitate effective road safety measures and decision-making, especially in settings with limited resources. Therefore, it is crucial to consider ML and spatial analysis techniques and advanced optimization algorithms to enhance traffic flow control, in road safety research and transport planning efforts.

IMPROVING ROAD SAFETY ON THE NATIONAL ROAD NETWORK IN ROMANIA THROUGH PILOT SOLUTIONS

In recognition of outstanding work in the field of road safety, especially for the implementation of road safety pilot projects, N.C.R.I.A.-S.A. was declared the winner. The road safety pilot projects that have attracted the attention of the IRF, being a model to follow for all road administrations are: Roller Barriers; Impact attenuators; Road sector 2+1; Cable Road barriers; Illumination of dangerous sectors with light assemblies; Projects to increase road safety by creating elevated road crossings. The International Road Federation (IRF) encourages all companies and organizations in the road development sector to get involved in the awards program it grants, because by promoting innovative ideas a positive and motivating influence can be created. The National Company for Road Infrastructure Administration S.A, Traffic Safety Service participated in the "Find a Way Global Road Safety Award" organized by IRF. In this article, the road safety projects are presented, with their results and the road safety measures implemented by the national roads and highways administrator in Romania, so that other road administrations to be encouraged to implement as many road safety measures as possible.

Exploring improvisations in road safety in a low-income setting

Road collision types repeat themselves, especially in low- and middle-income countries (LMICs), where countermeasures are often improvised and implemented with little planning. At the Shahbag intersection in Dhaka, Bangladesh, speed bumps were quickly constructed at the exit of the intersection as an improvised road safety measure following the occurrence of a fatal collision, which eventually contributed to another collision between a truck and a car. The events influencing the improvisation decision, and that action’s consequences, have been analysed using the Impromap methodology, a variation of the Accimap approach that focusses specifically on improvisation. The applicability of the Impromap as a systems-based approach to the road safety domain is assessed using the predictions described in Rasmussen’s risk management framework, and corresponding countermeasures are proposed. The analysis shows that improvisation in the road safety domain is undesirable irrespective of the economic setting as it is likely to eventually contribute to secondary collisions. Practitioner summary: In this paper, the events influencing the improvisation decision following a road crash, and that action’s consequences, have been analysed using the Impromap methodology. The applicability of Impromap as a systems-based approach in road safety domain is assessed using the Rasmussen’s risk management framework-based predictions, and corresponding countermeasures are proposed.

Post-Traumatic Complete Paralysis of the III Pair of Cranial Nerves at Sikasso Hospital: A Case Report

Public road accidents (AVP) are frequent and serious and are responsible for numerous cranioencephalic and neurological lesions. Paralyzes of the oculomotor nerves are frequently reported, especially in young subjects during these AVPs. We report the case of complete paralysis of the third pair of cranial nerves in a 22-year-old young lady, following a road accident. Clinical examination revealed intrinsic and extrinsic paralysis of the 3rd pair of cranial nerves. Computed tomography showed a subarachnoid hemorrhage. After 7 days of hospitalization under analgesics and hyperhydration, a complete regression of the lesions was obtained. An angio-scan performed 3 months after the accident showed a slight non-significant stenosis of 25. 54% of the left internal carotid without anomaly of the intracranial vascular network. Paralyzes of the third cranial pair, total or partial, most often regress within variable periods of 6 months to 2 years. The prevention of accidents on the public highway requires the application of road safety measures.

Research of a Fiber Sensor Based on Fiber Bragg Grating for Road Surface Monitoring

Road infrastructure is a key public asset because it benefits the social and economic development of any country. It plays an important role in the development of the industrial complex and the production sector, and the surfaces of transport roads should be of high quality and have a long service life. Road infrastructure, like all infrastructure, requires preservation, maintenance and repair. There are special requirements for roadways that must be observed during construction or repair. The uncertainty of the composition, temperature sensitivity and viscoelastic characteristics of road materials make the structural analysis of pavement very difficult compared to other civil structures, such as bridges, tunnels and buildings. For this reason, the question of how to improve fiber sensors based on fiber Bragg grating (FBG) arose. The novelty of this study is to modernize fiber sensors based on FBG so that they display deformation, stress and displacement, temperature and other parameters with much greater accuracy, which would provide a reliable scientific basis for modifying the theory, as well as the use of a fiber sensor based on FBG for simultaneous measurement of deformation and temperature when monitoring the road surface. This article is devoted to a detailed study of the use of fiber-optic sensors (FOS) based on fiber Bragg grating for road surface monitoring. Such a fiber sensor, consisting of a fiber Bragg grating and a pair of grids, can offer the possibility of simultaneous measurement of deformation and temperature for monitoring the pavement. Temperature and deformation measurements were carried out by installing a sensor on the surface of a made asphalt sample. The built-in fiber sensor based on FBG provides important information about how the pavement structure can withstand the load and subsidence of soil and implement road safety and stability measures in a timely manner to evaluate and predict the service life of the pavement. The results of the study showed that the synchronicity, repeatability and linearity of the characteristics of the fiber sensor are excellent. The difference between the experimental and theoretical results was about 7%. Thus, based on the results of the obtained data, the fiber sensor on the FBG can be used for monitoring and designing road surfaces and in general transport infrastructure.

Road Accident and Safety Issue in Bangladesh: A Comprehensive Study

Road accidents are a major public health concern in Bangladesh, causing significant harm to individuals and society as a whole. Despite various initiatives taken by the government, the number of road accidents continues to increase, highlighting the need for a legal approach to address this issue. This study aims to analyze the current legal framework in Bangladesh with regards to road safety and identify potential areas for improvement. The study found that the current legal framework in Bangladesh contains several provisions aimed at promoting road safety, including the Bangladesh Road Transport Act, the Bangladesh Road Transport Corporation Act, and the Bangladesh Road Transport Authority Act. However, these laws lack effective enforcement mechanisms, and there is limited accountability for those who violate traffic rules. This has led to widespread flouting of traffic regulations and a high incidence of road accidents. The study recommends the introduction of stricter penalties for those who violate traffic rules, including fines and imprisonment. It also suggests the establishment of an independent road safety commission to monitor compliance with traffic rules and to ensure effective implementation of road safety measures. The commission should have the authority to investigate road accidents and to take appropriate action against those found responsible. Additionally, the study recommends the introduction of mandatory training programs for all drivers and the provision of better infrastructure and technology to support road safety. This may include the installation of traffic lights and speed cameras, as well as the use of innovative technologies such as GPS-based monitoring systems. n conclusion, a legal approach is crucial in addressing the problem of road accidents in Bangladesh. By introducing stricter penalties, establishing an independent road safety commission, and investing in road safety infrastructure and technology, the government can help to reduce the number of road accidents and save lives. Further research is needed to understand the impact of these measures and to identify additional ways to promote road safety in Bangladesh. Keywords: Safety, Transport, Traffic, Accident, Punishment DOI: 10.7176/JLPG/132-05 Publication date: May 31 st 2023

Understanding the New Dimensions and Implications of Motor Vehicles (Amendment) Act, 2019

The Motor Vehicle Amendment Act 2019 is a significant piece of legislation that aims to reform and strengthen the laws governing road safety and motor vehicles in India. The Act was introduced as an amendment to the existing Motor Vehicles Act, 1988, and has been implemented with the objective of reducing road accidents, improving traffic management, and ensuring greater accountability among road users. The Motor Vehicle Amendment Act 2019 introduces several key provisions that have far-reaching implications for motorists, passengers, and other stakeholders. One of the major highlights of the Act is the substantial increase in fines and penalties for traffic violations, such as over-speeding, drunk driving, not wearing a seatbelt or helmet, and using a mobile phone while driving. The Act also introduces stricter provisions for driving without a valid license, allowing underage driving, and dangerous driving, with higher penalties and imprisonment in certain cases. Another significant aspect of the Motor Vehicle Amendment Act 2019 is the focus on improving road safety measures. The Act mandates the use of electronic enforcement, such as speed cameras and red-light cameras, to catch traffic violators. It also emphasizes the importance of road engineering and design, making it mandatory for road authorities to follow guidelines for road construction and maintenance. The Act also introduces provisions for protecting vulnerable road users, such as pedestrians and cyclists, and sets guidelines for their safety.

A systematic review and meta-synthesis of the complex and interconnected factors that influence planning for driving retirement

Introduction: With an aging population there are more older drivers than ever before. To reduce unnecessary road accidents and assist older drivers to successfully transition to non-driving, a better understanding of the factors that influence planning for driving retirement is required. This review explores documented factors that may influence older adults in planning for driving retirement, thus providing new understandings that can inform future preventative road safety measures, interventions, and policies. Method: A systematic search was performed using four databases to locate qualitative studies on the factors that influenced older drivers to plan for driving retirement. To identify factors influencing planning for driving retirement, a thematic synthesis approach was utilized. Identified themes were categorized in relation to elements of the Social Ecological Model theoretical framework. Results: The systematic search resulted in 12 included studies from 4 countries. Four major themes and 11 subthemes were identified regarding planning driver retirement. Each subtheme denotes a factor that may facilitate and/or impede older drivers planning for driving retirement. Conclusion: These results indicate that it is vitally important to encourage older drivers to plan for driving retirement as early as possible. Stakeholders involved in the safety of older drivers (including family, clinicians, road authorities, and policy makers) should work together on interventions and policies that empower older drivers to successfully plan for driving retirement in order to improve road safety and quality of life. Practical Applications: Introducing conversations about driving retirement via medical appointments, family, media, and peer-support groups could facilitate planning for driving retirement. Community-based ride-sharing systems and subsidized private transport options are needed to ensure continued mobility of older adults, especially in rural and regional areas that lack alternate transport services. When devising urban and rural planning, transport, license renewal, and medical testing rules, policy makers should consider older drivers’ safety, mobility, and quality of life after driving retirement.

Utilization of the information sharing road safety measure in developing Countries: Case study of Suphanburi, Thailand

Despite decades of road safety efforts, it is a big question why the number of road fatalities in developing countries has not been reduced. One of the reasons for the ineffectiveness of road safety measures is the lack of cooperation and information sharing between the concerned agencies. This study examined the utilization of the “information sharing road safety measure” based on a successful case in Kamagaya City, Japan. By cooperating with local authorities and the community, the current status and implementation issues of road safety measures under a local context were identified/extracted, especially by utilizing an engineering approach as the main part of the “information sharing road safety measure” study. Suphanburi Province, Thailand, was selected for a case study. The results showed that to successfully implement the “information sharing road safety measure,” its continuation and consistency are essentially required. Furthermore, an engineering approach for identifying risk and black spots by the local community can help in clarifying the causes of accidents in the targeted areas. The cooperation and participation of stakeholders from government sectors, i.e., municipality, police, schools, residents/communities, academic sector, and the private sector, play a key role in disseminating and sharing information regarding road safety and selecting appropriate road safety measures to suit the local contexts.

Impact of easing COVID-19 lockdown restrictions on traumatic injuries in Riyadh, Saudi Arabia: one-year experience at a major trauma centre

IntroductionLockdown restrictions due to the COVID-19 pandemic have reduced the number of injuries recorded. However, little is known about the impact of easing COVID-19 lockdown restrictions on the nature and outcome of injuries. This study aims to compare injury patterns prior to and after the easing of COVID-19 lockdown restrictions in Saudi Arabia.MethodData were collected retrospectively from the Saudi TraumA Registry for the period between March 25, 2019, and June 21, 2021. These data corresponded to three periods: March 2019–February 2020 (pre-restrictions, period 1), March 2020–June 2020 (lockdown, period 2), and July 2020–June 2021 (post easing of restrictions, period 3). Data related to patients’ demographics, mechanism and severity of injury, and in-hospital mortality were collected and analysed.ResultsA total of 5,147 traumatic injury patients were included in the analysis (pre-restrictions n = 2593; lockdown n = 218; post easing of lockdown restrictions n = 2336). An increase in trauma cases (by 7.6%) was seen in the 30–44 age group after easing restrictions (n = 648 vs. 762, p < 0.01). Motor vehicle crashes (MVC) were the leading cause of injury, followed by falls in all the three periods. MVC-related injuries decreased by 3.1% (n = 1068 vs. 890, p = 0.03) and pedestrian-related injuries decreased by 2.7% (n = 227 vs. 143, p < 0.01); however, burn injuries increased by 2.2% (n = 134 vs. 174, p < 0.01) and violence-related injuries increased by 0.9% (n = 45 vs. 60, p = 0.05) post easing of lockdown restrictions. We observed an increase in in-hospital mortality during the period of 12 months after easing of lockdown restrictions—4.9% (114/2336) compared to 12 months of pre-lockdown period—4.3% (113/2593).ConclusionThis is one of the first studies to document trauma trends over a one-year period after easing lockdown restrictions. MVC continues to be the leading cause of injuries despite a slight decrease; overall injury cases rebounded towards pre-lockdown levels in Saudi Arabia. Injury prevention needs robust legislation with respect to road safety measures and law enforcement that can decrease the burden of traumatic injuries.

Effective road safety measures in Greece

The paper presents road safety actions and measures proposed in the framework of the development of the Greek Strategic Road Safety Plan for the period 2021-2030. Five road safety pillars that concern all road crash factors are outlined. For each pillar, actions and particular measures are defined, adapted to the Greek reality and based on the principles of Vision Zero and the Safe System Approach. In total, 44 actions and 200 road safety measures are foreseen and implementation priorities are defined. Next, the framework for monitoring the implementation of actions is presented, taking into account the elements of the Safe System Approach. The use of a set of key performance indicators for road safety is proposed and the methodological framework for the collection of the necessary data and the calculation of road safety performance indicators is provided.