Road Safety Plan Research Articles

Urban Arterial Lane Width Versus Speed and Crash Rates: A Comprehensive Study of Road Safety

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

Road crash dynamics in Malaysia: Analysis of trends and patterns

Road crashes represent a significant public health and safety concern globally, and Malaysia is no exception. Understanding the trends and patterns of road crashes is essential for devising effective strategies to mitigate risks and enhance road safety. This study presents a comprehensive analysis of road crash dynamics, focusing on road users, severity patterns and geographical patterns in Malaysia from 2012 to 2022. Data sourced from the Royal Malaysian Police (RMP) are utilized to examine various aspects of road crashes. Road crash trend, geographical patterns, linear trend analysis and K-means clustering are employed to explore patterns of road crash in Malaysia. The findings reveal that motorcycles consistently emerged as the most involved road user. Geographical patterns discovered that Selangor exhibits higher crash number. Linear trend analysis revealed significant upward trends in crash frequency prior to the pandemic, while the number of fatalities resulting from road crash showed a downward trend over the observed period. K-means clustering identified that Selangor recorded high total crashes and high total of fatalities. This study also considers the influence of the Covid-19 pandemic on road crash dynamics, highlighting changes in travel patterns and behaviour. There also have been notable successes, such as the reduction in total fatalities and the effectiveness of targeted interventions via the accomplishments of initiatives of Malaysian Road Safety Plan 2014–2022.

Predicting the type of road accidents based on air temperature in Iran: A case study of roads in Qazvin province

This study investigates the relationship between ambient temperature, weather conditions, and types of road accidents in Qazvin province, Iran. The research addresses a significant societal challenge of road accidents, particularly in developing countries like Iran. The objectives are to analyze the correlation between temperature and accident types and to develop a predictive model using data mining techniques. The study employs a quantitative approach, analyzing over 15,000 accident records from 2010 to 2020. The findings reveal a connection between the temperature variable and the type of road accidents as well as weather conditions. Additionally, data mining analysis identifies a predictable pattern among temperature variables, types of road accidents, and weather conditions. Implications of the study underscore the importance of considering temperature and weather conditions as secondary factors influencing accidents. The predictive model can aid decision-makers in formulating effective strategies to reduce accidents. Understanding the relationship between temperature, weather, and accident types enables the design of targeted interventions to enhance road safety. This research contributes valuable insights to accident reduction efforts and emphasizes the significance of addressing environmental variables in road safety planning and policy-making.Moreover, the results of the data mining pattern analysis indicate that car overturning accidents in various weather conditions are the primary type of accidents, followed by chain accidents. However, the types of accidents vary based on different weather conditions and temperatures. The study highlights the intricate connection between weather conditions, temperature, and types of road accidents. By utilizing data mining techniques, the research provides a predictive model for accident patterns, offering valuable insights to enhance road safety strategies.

Augmented Reality (AR) as a New Educational Medium for Children: Using Road Safety Content

Road accidents, often involving one or more vehicles, can result in property damage, injuries, or even fatalities. These incidents can occur due to a range of factors, including human errors, adverse weather conditions, or mechanical failures, despite precautions taken by road users. Consequently, early education on road safety is crucial, especially for children. This research focuses on assessing children's knowledge of road accidents in Malaysia and evaluating the impact of augmented reality (AR) experiences on their understanding. To support this project, we developed an AR application using the Pendidikan Keselamatan Jalan Raya (PKJR) Year 2 Guidebook as a medium for students to learn about road safety. Selected pages in the book serve as markers, enabling students to engage with AR content while using the book. The study aims to determine the effectiveness of the AR application as an educational tool for children. A group of school children participated in testing this AR application, and their experiences were assessed using an adapted Game Experience Questionnaire (GEQ) model. The results indicate that after using the AR application, the group of 8-year-old children exhibited improved awareness of the road environment. This paper also explores the emotional aspects of participant experiences as emphasized by the GEQ. These findings highlight the potential of AR as an innovative tool for educating children about road safety, challenging traditional approaches, and promoting safer road behaviors among young individuals. Furthermore, this project aligns with the strategies outlined in the Malaysian Road Safety Plan 2020-2030, particularly in reducing risks to pedestrians. In a broader context, this study contributes to the evolving landscape of technology-driven safety initiatives, underscoring the importance of adapting contemporary educational mediums to address complex societal issues like road accidents. In conclusion, we discuss the potential for future studies involving road safety education and interactive technology.

Assessing the effectiveness of road safety measures in Barcelona (2013-2018)

IntroductionThis article aims to determine the effectiveness and extent of measures taken to decrease the severity of traffic crashes in Barcelona from 2013 to 2018. This will be achieved through an analysis of the traffic crash data. MethodOur approach involves the use of binary logistic regression models. We rely on the traffic crash dataset from 2010-2019 available in the Open Data Barcelona platform. ResultsThe outcomes obtained from the suggested models are contrasted with the strategies outlined in the Local Road Safety Plan 2013-2018 to minimize the severity of crashes. Effective preventive actions were identified, such as road safety educational programs, creating calm zones, enhancing pedestrian crossings, or expanding bicycle lanes. However, certain measures were found to be ineffective or their impact remained uncertain. ConclusionsOur findings indicate that the measures implemented in Barcelona may have participated in and influenced the decrease in the severity of traffic incidents over the past decade. Notably, fatalities have decreased more than severe injuries. More attention should be given to less effective measures such as speed controls and drug/alcohol testing.

Data Mining technique: Application of Apriori algorithm for road accident analysis

Road accidents can happen due to various factors. These factors that contribute to road accidents have cost damage to properties, injuries or deaths and most road accidents are attributable to the lack of knowledge on road safety. To provide safe driving and road safety plans, critical analysis of road accident data is needed, to identify the causes of road accidents. Annually, 1,250,000 people die and 50,000,000 are injured in road accidents worldwide, and fatal road accidents are caused by human error. Improving road conditions is not sufficient, but significantly understanding human errors that cause road accidents, and negligence of corrective and safety driving protocols provided by the concerned government agencies or private organizations. The study aimed to help get insights about the causes of road accidents, and to provide knowledge of road accidents for road safety using Association Rule Mining with the application of the Apriori Algorithm. Association rule mining using the Apriori Algorithm produces significant patterns and insights that help identify the causes of road accidents.

Remote sensing traffic scene retrieval based on learning control algorithm for robot multimodal sensing information fusion and human-machine interaction and collaboration.

In light of advancing socio-economic development and urban infrastructure, urban traffic congestion and accidents have become pressing issues. High-resolution remote sensing images are crucial for supporting urban geographic information systems (GIS), road planning, and vehicle navigation. Additionally, the emergence of robotics presents new possibilities for traffic management and road safety. This study introduces an innovative approach that combines attention mechanisms and robotic multimodal information fusion for retrieving traffic scenes from remote sensing images. Attention mechanisms focus on specific road and traffic features, reducing computation and enhancing detail capture. Graph neural algorithms improve scene retrieval accuracy. To achieve efficient traffic scene retrieval, a robot equipped with advanced sensing technology autonomously navigates urban environments, capturing high-accuracy, wide-coverage images. This facilitates comprehensive traffic databases and real-time traffic information retrieval for precise traffic management. Extensive experiments on large-scale remote sensing datasets demonstrate the feasibility and effectiveness of this approach. The integration of attention mechanisms, graph neural algorithms, and robotic multimodal information fusion enhances traffic scene retrieval, promising improved information extraction accuracy for more effective traffic management, road safety, and intelligent transportation systems. In conclusion, this interdisciplinary approach, combining attention mechanisms, graph neural algorithms, and robotic technology, represents significant progress in traffic scene retrieval from remote sensing images, with potential applications in traffic management, road safety, and urban planning.

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>

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.

Innovation and long-term planning in public policy: the case of national road safety plans in Norway

Vision Zero was adopted as the long-term ideal for transport safety in Norway in 2001. Starting in 2002, national road safety action plans covering a period of four years have been developed. This paper identifies innovative elements in these plans and explores the statistical relationship between innovation and the number of killed or seriously injured road users over time. A statistical relationship is found, but it is noisy and cannot be interpreted as a causal relationship. We show how road safety policy has become upgraded and more systematic over time, and that these developments co-exist with a gradual reduction in number of severe injuries and deaths. Important innovative elements are related to the process of policy development, which now ensures a much broader , consensus and commitment to improving road safety than before the action plans were introduced (i.e. before 2002).

A Model for Identifying Road Risk Class

Abstract In many road safety, traffic management, and travel planning analyses, it is useful to classify road sections according to risk level. Such classification is labour-intensive and needs to be reviewed periodically. The authors propose a model for identifying a discrete risk class for road sections based on selected traffic flow parameters, which are available in most measurement systems monitoring current traffic conditions. The Surrogate Safety Measures approach was applied in the model formulated using Principal Components Analysis. As input to the model SSMs are used in the form of a set of hourly average traffic flow parameters. The SSMs used are: the percentage of light vehicles exceeding the speed limit by a value in the range 21 to 30 km/h; the percentage of light vehicles exceeding the speed limit by more than 30 km/h; the traffic volume of light vehicles; the traffic volume of heavy vehicles and the mean speeds of light vehicles and heavy vehicles. This paper presents results of calculations of risk class obtained from the model for different locations on single-carriageway two-lane roads in Poland. Satisfactory compliance of risk classes designated by the road operator and identified by the model based on current traffic data was achieved. The proposed model can be used as the core of an effective alternative road safety screening method.

Fracture of Limbs Bones in a Rural and Post Conflict Health Zone

Background: Trauma is major public health problem in developing countries where they are associated with many factors, mainly such as lack of road safety, poverty and inadequate urban planning. We aim to highlight the epidemiology of the fractures limbs bones in a rural area where people live in a context of low economy, low education and insecurity. Methods: We conducted a cross-sectional, retrospective study involving all patients admitted for fracture in the surgical department of FOMULAC-KATANA General Reference Hospital from 1st January 2011 to 31st December 2014. The patients whose medical records were not completed were excluded. Results: Among 136 patients included in our study, 140 bones limbs were registered. Most of them (60.1%) were aged between 15 - 44 years and the average age is 30, 1 year. Male predominance is noticed (72.8%) with a male/female ratio of 2.7/1, and informal professionals are the most found in our patients (67.6%). The main circumstances of fracture occurrence are, respectively, road traffic accidents (57.4%), falls (22.3%) and firearms (10.3%). The leg bones (33.6%) and forearm bones (20%) are the most concerned bones. Open fractures are recorded in 40.7% of patients. The supporting treatment is mainly orthopedic (64.5%) but also surgical (35.5). The mortality rate is 3.6%. Conclusion: The fractures of the limbs bones in Katana General Reference Hospital mainly affect young male which is informal professional. The road traffic accidents are the first circumstance leading to fractures followed by falls. The leg bones are the most fractured body part and fractures are commonly opened.

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.

A Decision Support System for the safety evaluation of urban pedestrian crossings

This paper proposes an innovative methodology, based on a Decision Support Systems, for the safety evaluation of pedestrian crossings without traffic lights in urban neighborhood areas. It provides an on-site inspection performed using ad-hoc data check lists, and it allows to assign a safety rate to the pedestrian crossing, in order to define a priority list of interventions and to suggest which features need to be improved. This new approach can be useful and easy to use for public administration managers and local governances, when they need to allocate limited financial resources to several pedestrian crossings. It has been applied to 10 pedestrian crossings on two roads in the urban area of Bologna and the resulted ranking list has been used by the Municipality of Bologna in its Urban Road Safety Plan 2016-2019.

Driving as well as on a Sunny Day? Predicting Driver's Fixation in Rainy Weather Conditions via a Dual-Branch Visual Model

Dear Editor, Traffic driving is a dynamic and complicated task in which drivers are required to pay close attention to the important targets or regions to maintain safe margins. Rainy weather conditions make it more challenging with factors such as low visibility, raindrops, pedestrian with umbrellas, wipers, etc. Studies showed that rainy condition affects driving safety significantly [1], [2]. It is reported that, in raining weather condition, the odds for a fatal accident are 3.340 times higher on highways than on streets [3]. An investigation of the relationship between rainfall and fatal crashes in Texas from 1994 to 2018 on fatality analysis reporting system (FARS) database illustrated that rain-related fatal crashes represented about 6.8% of the total fatal crashes on average, moreover, the proportion showed high variability at the annual, monthly, and hourly time scales [4]. Therefore, raining is a complex and critical factor for road safety planning and management. In fact, the traffic environment is a dynamic scene with multiple sources of information, including important targets that are highly relevant to the current driving task as well as irrelevant targets that may distract the driving task [5]. Driven by the visual selective attention mechanism, experienced drivers often focus their attention on the most important regions and only show concern for objects related to driving safety in those salient regions. This selective attention mechanism [6], [7] helps drivers reduce the interference of irrelevant scene information and guarantee the driving safety. Understanding the selective attention mechanism of experienced drivers and then simulating the efficient saliency detection process in rainy conditions may help driving a car in rainy conditions as well as on a sunny day.

The Ultimate Safe System: Redefining the Safe System Approach for Road Safety

The Safe System approach to road safety has been adopted in many countries, but it has been adopted pervasively to a substantially constrained extent. This paper argues that effective adoption is hampered by two weaknesses in strategies for the implementation of Safe System: (1) interpretations of the shared responsibility principle and (2) Safe System adoption presented as simply requiring the use of multiple pillars of action. The typical description of shared responsibility includes responsibility by road users to obey the rules. This absolves accountability for road safety by the system owners and operators, facilitating victim blaming and reliance on road users who are acknowledged to be fallible. Thus, the system cannot be fully safe, and the vision of zero road trauma cannot be achieved. The extent to which road users are responsible for road safety via their actions is precisely the extent to which those responsible for the system have failed to deliver a safe road system. The assessment of road safety plans as Safe System because it includes multiple pillars of action fails to distinguish a system approach from a Safe System approach. Through these inclusions and interpretations, road safety advocates inadvertently obviate the responsibility of system owners and operators to provide a safe road system and prevent the achievement of zero road trauma, which nonetheless remains the vision described in Safe System strategies and plans. The Ultimate Safe System approach is proposed with a definition that genuinely drives the delivery of a truly Safe System and thus zero road trauma. Practical implications are considered.

Modelling injury severity of victims in collisions involving public transit in Dhaka, Bangladesh

Collisions involving public transit (PT) have increased significantly in the recent times. In developing countries, the scenario is much worse as the PT collision occurrence and injury severity are higher by manifold. This study investigates the injury severity of victims of PT involved crashes in Dhaka, Bangladesh. Victims include PT occupants and counterpart road users such as pedestrians who are more vulnerable. A latent segmentation-based ordered logit (LSOL) model is developed using police-reported crash records of Dhaka. The LSOL model addresses the ordinality of injury severity and captures unobserved heterogeneity by allocating victims into discrete latent segments. The LSOL model is estimated for two segments: segment two is high-risk segment which includes unconventional vehicle occupants and pedestrians, whereas segment one is low-risk segment. The injury severity model results suggest that person profile, environmental factors and built environment characteristics significantly influence injury severity. For example, female and elderly victims have higher likelihood to sustain severe injury. The model confirms the existence of significant heterogeneity. For instance, mid-block collisions are more likely to yield severe injury in the high-risk segment, and less likely to result in severe injury in the low-risk segment. Similar heterogeneity is confirmed for variables representing impaired driver, three-way intersection, and police-controlled intersection. The elasticity effect analysis suggests that female victims, younger and adult victims, unconventional vehicle occupants, pedestrians, and mid-block crashes have substantial positive impact on fatality. The identified influential factors and heterogeneity captured in the study need to be accommodated in road safety plans and policies.

Profiling Casualty Severity Levels of Road Accident Using Weighted Majority Voting

To determine the individual circumstances that account for a road traffic accident, it is crucial to consider the unplanned connections amongst various factors related to a crash that results in high casualty levels. Analysis of the road accident data concentrated mainly on categorizing accidents into different types using individually built classification methods which limit the prediction accuracy and fitness of the model. In this article, we proposed a multi-model hybrid framework of the weighted majority voting (WMV) scheme with parallel structure, which is designed by integrating individually implemented multinomial logistic regression (MLR) and multilayer perceptron (MLP) classifiers using three different accident datasets i.e., IRTAD, NCDB, and FARS. The proposed WMV hybrid scheme overtook individual classifiers in terms of modern evaluation measures like ROC, RMSE, Kappa rate, classification accuracy, and performs better than state-of-the-art approaches for the prediction of casualty severity level. Moreover, the proposed WMV hybrid scheme adds up to accident severity analysis through knowledge representation by revealing the role of different accident-related factors which expand the risk of casualty in a road crash. Critical aspects related to casualty severity recognized by the proposed WMV hybrid approach can surely support the traffic enforcement agencies to develop better road safety plans and ultimately save lives.

Modular Design Using Sustainable Luminescent Materials in Energy-Saving Reflective Cat’s Eyes for Public Road Safety Planning

Recently, there has been a global movement toward environmental protection and energy conservation through the design and development of new products in accordance with sustainable utilisation. In this study, rare earth luminescent materials were used owing to their active light emission and reusability. Additionally, solar light-emitting diode lights and car-light reflection were utilised to increase the recognition and reliability of reflective cat eyes. Along with carbon reduction, this can save energy and enhance road safety. This study considered the Theory of Inventive Problem Solving and a literature review to analyse the issues in existing products. Then, expert interviews were conducted to screen projects and develop product design policies. Finally, the ratio of light-storage materials was experimentally determined and the prototypes implemented. This cat’s eye addresses the shortcomings identified in previous analyses of existing products. We applied energy storage environmental protection materials, together with material proportioning (which balanced warning efficiency against cost-effectiveness) to develop diversified modular kits; these were flexible in terms of quantity and easily assembled. This study achieved four key objectives: (1) reducing the research and development costs of the manufacturer; (2) offering buyers a diverse suite of products; (3) responding to a need to improve diverse road user safety; and (4) reducing government procurement costs for safety warning products. The results provide a reference for the creative modular design of energy-saving products for public road safety planning in various industries.

THEORY OF PLANNED BEHAVIOR APPLICATION ON MOTORCYCLE RIDER SAFETY BEHAVIOR

<p>In Indonesia, the basis for implementing road safety consists of five pillars which are compiled in the General National Road Safety Plan (RUNK). One of the pillars proclaimed is the behavior of safe road users. This aims as an effort to reduce traffic accidents because road users are the biggest factor in road accidents related to user behavior. The cases of road traffic accidents in Indonesia are dominated by motorcyclists, especially Jakarta, which is one of the cities with a fairly high rate of motorcycle accidents. Because the behavior of road users is closely related to traffic accidents, and also motorcyclists are the users who are involved in the highest traffic accidents, this study explains the factors that determine the safety behavior of motorcyclists in Jakarta. The application of the Theory of Planned Behavior (TPB) is used as a basis for finding the factors that may determine the behavior of the motorcyclist. Furthermore, the research approach used in this study is quantitative with primary data in the form of a closed questionnaire instrument from 230 respondents who ride motorcycles in Jakarta. The data analysis technique used is the Structural Equation Modeling (SEM) approach using the AMOS 22.00 program as a tool in determining the determinants of driver safety behavior. The results of the analysis show that the most dominant direct predictor in determining the safety behavior of motorcyclists in Jakarta is the intention to behave safely. While the indirect factor that determines is the attitude of the driver. The results of the analysis also show that the age of the driver has a significant influence on safety behavior.</p>