Field Of Traffic Safety Research Articles

Safety culture: from student to professional

Traffic safety culture, recently this definition is very often heard at conference calls of heads of services. What does this concept imply, how is it interconnected with safety management and why do these areas of work relate to the main tasks of the Russian Railways holding in the field of traffic safety and operation of railway transport? The article discusses the causes of traffic safety violations, which are associated with the influence of the human factor, violation of labor discipline, and improper performance of official duties. The main objective of the study is to find approaches to raising the level of safety culture among railway workers. Most often, when solving a certain task, it is necessary to go beyond it and consider issues that at fi rst glance do not relate to the essence of the problem, but the more you delve into the study, you come to conclusions that show the connection between themselves sometimes in the most unexpected and unpredictable way. How to improve the traffic safety culture among the company’s employees? Is it possible to correct employees whose professional experience has been assessed not for one year, but for decades? Is it possible to change the thinking of an employee whose professional experience has been assessed not for one year, but for decades? In my opinion, an employee who has been violating for many years will no longer be able to perform his job duties properly. This is a “professional” defect that cannot be corrected, because most often it was taught this way during an internship in production. Most cases could have been avoided because minor violations combined with certain factors are fatal.

A Roadside Precision Monocular Measurement Technology for Vehicle-to-Everything (V2X).

Within the context of smart transportation and new infrastructure, Vehicle-to-Everything (V2X) communication has entered a new stage, introducing the concept of holographic intersection. This concept requires roadside sensors to achieve collaborative perception, collaborative decision-making, and control. To meet the high-level requirements of V2X, it is essential to obtain precise, rapid, and accurate roadside information data. This study proposes an automated vehicle distance detection and warning scheme based on camera video streams. It utilizes edge computing units for intelligent processing and employs neural network models for object recognition. Distance estimation is performed based on the principle of similar triangles, providing safety recommendations. Experimental validation shows that this scheme can achieve centimeter-level distance detection accuracy, enhancing traffic safety. This approach has the potential to become a crucial tool in the field of traffic safety, providing intersection traffic target information for intelligent connected vehicles (ICVs) and autonomous vehicles, thereby enabling V2X driving at holographic intersections.

ЭНЕРГОСБЕРЕГАЮЩИЕ СИСТЕМЫ ДЛЯ ВЗВЕШИВАНИЯ БОЛЬШИХ МАСС

Energy-saving weighing systems are considered, which are an effective tool for monitoring cargo flows without significant expenditure of electrical energy. They allow you to determine the exact weight of the load, prevent overloading and optimize weight distribution. They also contribute to compliance with legal requirements in the field of traffic safety and cargo transportation, reduce losses from theft and errors during loading and unloading

An Urban Built Environment Analysis Approach for Street View Images Based on Graph Convolutional Neural Networks

Traditionally, research in the field of traffic safety has predominantly focused on two key areas—the identification of traffic black spots and the analysis of accident causation. However, such research heavily relies on historical accident records obtained from the traffic management department, which often suffer from missing or incomplete information. Moreover, these records typically offer limited insight into the various attributes associated with accidents, thereby posing challenges to comprehensive analyses. Furthermore, the collection and management of such data incur substantial costs. Consequently, there is a pressing need to explore how the features of the urban built environment can effectively facilitate the accurate identification and analysis of traffic black spots, enabling the formulation of effective management strategies to support urban development. In this study, we research the Kowloon Peninsula in Hong Kong, with a specific focus on road intersections as the fundamental unit of our analysis. We propose leveraging street view images as a valuable source of data, enabling us to depict the urban built environment comprehensively. Through the utilization of models such as random forest approaches, we conduct research on traffic black spot identification, attaining an impressive accuracy rate of 87%. To account for the impact of the built environment surrounding adjacent road intersections on traffic black spot identification outcomes, we adopt a node-based approach, treating road intersections as nodes and establishing spatial relationships between them as edges. The features characterizing the built environment at these road intersections serve as node attributes, facilitating the construction of a graph structure representation. By employing a graph-based convolutional neural network, we enhance the traffic black spot identification methodology, resulting in an improved accuracy rate of 90%. Furthermore, based on the distinctive attributes of the urban built environment, we analyze the underlying causes of traffic black spots. Our findings highlight the significant influence of buildings, sky conditions, green spaces, and billboards on the formation of traffic black spots. Remarkably, we observe a clear negative correlation between buildings, sky conditions, and green spaces, while billboards and human presence exhibit a distinct positive correlation.

ON THE ISSUE OF EFFICIENT USE OF MONEY FROM THE ROAD FUND FOR MEASURES TO INCREASE THE LEVEL OF ROAD TRAFFIC SAFETY

A review of modern financial regulations regulating the allocation of budget funds for road safety measures made. An analysis of the distribution of funding and the filling of the road fund over a four-year period made. Identified inconsistencies in financing tasks related to increasing traffic safety. It established that 5% of the fund's budget or 1.63 billion hryvnias are the guaranteed allocations for road safety as of 2018. The main manager of these funds is the State Agency for Reconstruction and Development of Infrastructure of Ukraine (formerly - Ukravtodor). This institution distributes and approves allocations for traffic safety according to applications (budget requests for subvention) formed by the regions, which contain justification, physical and financial indicators that correspond to existing state regulations and departmental regulations of the specified institution. Proposals for harmonization are provided legislation. It concluded that one of the main causes of accidents is the low level of investment in improving the level of traffic safety. The need to develop a detailed, systematized description of social relations arising in the field of traffic safety, which are the object of normative and legal regulation by the state, has been identified.

An Intelligent Driving Monitoring System Utilizing Pedal Motion Sensor Integrated with Triboelectric‐Electromagnetic Hybrid Generator and Machine Learning

AbstractDriver's driving behavior and driving style have a crucial impact on traffic safety, capacity, and efficiency, so it is of great significance to monitor the driver's driving behavior and recognize their driving style. In this work, an intelligent driving monitoring system based on a triboelectric nanogenerator and electromagnetic generator is designed. The system consists of a self‐powered pedal motion sensor (SPMS) and an intelligent data processing unit (IDPU), which can monitor driving behavior and recognize driving style. SPMS is used for driving behavior monitoring, which mainly consists of a six‐phase triboelectric nanogenerator (S‐TENG) and a free‐rotating disk electromagnetic generator (FD‐EMG). S‐TENG can recognize information such as pedal movement direction, movement amplitude, and movement speed, and FD‐EMG can realize the function of a self‐powered driver's driving behavior warning. The IDPU includes a numerical calculation system for driving style characteristic variables and a driving style classifier. It can recognize the driving style based on the driving data collected by SPMS. The driving style classifier design is based on a combination of simulated driving experiments and machine learning techniques, and its accuracy is verified through experiments. This work has important potential applications in the field of traffic safety and intelligent driving.

On the Local and String Stability Analysis of Traffic Collision Risk

Conventional traffic stability studies primarily concentrate on the evolution of disturbances in vehicle motion but seldom consider how collision risk changes spatially and temporally. This study bridges the gap by extending the principles of traffic stability analysis to the field of traffic safety, focusing specifically on the temporal and spatial dynamics of collision risk. Leveraging the concepts of local and string stability, we formulate conditions under which collision risk behaves in a stable manner over time and space through the transfer function approach. A comparative analysis between conventional traffic stability and the newly introduced concept of collision risk stability reveals that while conditions for local stability are largely aligned in both domains, the criteria for string stability differ. These theoretical insights are substantiated through microscopic simulations using a variety of car-following models. The simulations also indicate that the consistency between theoretical and simulation outcomes diminishes as the disturbance magnitude increases, which is attributed to the linearization errors inherent in applying the transfer function in the theoretical derivations.

Identifying the latent relationships between factors associated with traffic crashes through graphical models

Traffic safety field has been oriented toward finding the relationships between crash outcomes and predictor variables to understand crash phenomena and/or predict future crashes. In the literature, the main framework established for this purpose is based on constructing a modelling equation in which crash outcome (e.g., frequencies) is examined in relation to explanatory variables chosen based on the problem at hand. Despite the importance and success of this approach, there are two issues that are generally not discussed: 1) the latent relationships between factors associated with crashes are oftentimes not the focus of analysis or not observed; and 2) there are not many tools to make informed decisions on which variables might have an impact on the crash outcome and should be included in a safety model, particularly when observations are limited. To address these issues, this paper proposes the use of graphical models, namely a Markov random field (MRF) modelling, Bayesian network modelling, and a graphical XGBoost approach, to disclose relationship topologies of explanatory variables leading to fatal and incapacitating injury pedestrian crashes. The application of graph learning models in traffic safety has a high potential because they are not only useful to understand the mechanism behind the crash occurrence but also can assist in devising accurate and reliable prevention measures by identifying the true variable structure and essential factors jointly acting towards crash occurrence, similar to a pathological examination.

Cross-Attention-Guided Feature Alignment Network for Road Crack Detection

Road crack detection is one of the important issues in the field of traffic safety and urban planning. Currently, road damage varies in type and scale, and often has different sizes and depths, making the detection task more challenging. To address this problem, we propose a Cross-Attention-guided Feature Alignment Network (CAFANet) for extracting and integrating multi-scale features of road damage. Firstly, we use a dual-branch visual encoder model with the same structure but different patch sizes (one large patch and one small patch) to extract multi-level damage features. We utilize a Cross-Layer Interaction (CLI) module to establish interaction between the corresponding layers of the two branches, combining their unique feature extraction capability and contextual understanding. Secondly, we employ a Feature Alignment Block (FAB) to align the features from different levels or branches in terms of semantics and spatial aspects, which significantly improves the CAFANet’s perception of the damage regions, reduces background interference, and achieves more precise detection and segmentation of damage. Finally, we adopt multi-layer convolutional segmentation heads to obtain high-resolution feature maps. To validate the effectiveness of our approach, we conduct experiments on the public CRACK500 dataset and compare it with other mainstream methods. Experimental results demonstrate that CAFANet achieves excellent performance in road crack detection tasks, which exhibits significant improvements in terms of F1 score and accuracy, with an F1 score of 73.22% and an accuracy of 96.78%.

Comparative evaluation for alternative variable importance rankings for pedestrian injury severities

Little research is dedicated to evaluating the performance difference of various metrics in ranking predictor importance in the traffic safety field. To this end, the main objective of the current paper is to evaluate and quantify different methods for sorting the variable importance related to crash severity. A comprehensive database for pedestrian-related crashes in the state of California was developed. Four popular measurement metrics used in the past were chosen for evaluation purpose: Mean Decrease Accuracy (MDA), Mean Decrease Gini (MDG), log-likelihood ratio test associated with multinomial logit model, and Principal Component Analysis (PCA). The former two metrics come under the same umbrella of the Random Forest (RF) technique, while the latter two are methods belonging to different domains. The results show the alternative methods yield different variable importance rankings with PCA being isolated from others. The two methods under the same domain of the random forest, or MDG and MDA, have the most common results, but still reveal a 17% ranking difference. It is anticipated that the results could raise more awareness of the importance of selecting the appropriate metrics to evaluate the predictor importance from different perspectives.

An exploratory investigation into the influence of risk factors on driver injury severity in angle crashes: A random parameter bivariate ordered probit model approach

Objective Angle crashes have been acknowledged as a concerning issue in the traffic safety field, though there is limited understanding of the contributions of risk factors to injury severity. This article aims to examine the impact of risk factors and unobserved heterogeneity on the severity of driver injuries in angle collisions by utilizing angle crash data in the United States from 2016 to 2021. Methods The relationship between risk factors and driver injury severities in angle crashes was investigated using a random parameter bivariate ordered probit model (RPBOP) with 4 categories of injury severity classified as outcome variables, including no injury, possible injury, minor injury, and serious jury. Risk factors were considered as explanatory variables, classified as driver characteristics, vehicle characteristics, road characteristics, environmental characteristics, time characteristics, and crash characteristics. Bayesian inference was used to assess the unobserved heterogeneity in risk factors, and marginal effects were computed to analyze the effect of each factor on injury outcomes. Results The findings demonstrate that risk factors have varying effects on driver involvement in angle crashes. Certain factors exhibited unobserved heterogeneity, including young drivers (ages 25–44), older drivers (over age 59), road grade, and collision point orientation. On the other hand, other factors, such as female gender, motorcycles, intersections, speed limit (>50 mph), poor lighting conditions, adverse weather, urban areas, and workdays, were shown to significantly increase the likelihood of driver injury in angle collisions, as well as increase susceptibility to fatal injury. Conclusions This article offers new insights into reducing driver injuries in angle crashes and has the potential to inform policy development aimed at preventing such incidents. Further research could utilize multisource data fusion and investigate the spatiotemporal stability of risk factors to enhance the generalizability of angle collision prevention strategies.

Research on Driver Emotion Classification Based on ECG Signals

Traffic includes factors that affect people, vehicles, and the environment. According to official data, traffic accidents caused directly or indirectly by human factors account for over 90% of the total number of traffic accidents, of which 70% are caused by vehicle drivers. Studying driver characteristics and considering driver factors in traffic safety has become an increasingly popular topic in the field of traffic safety. Driving emotions, as one of the driver characteristics, have a strong correlation with driving behavior. This article designs a simulated driving experiment and collects the electrocardiogram (ECG) indicators of drivers in driving with pleasure and anger during the experiment. The KNN clustering algorithm is used to classify and analyze the driver’s emotions.

Дослідження безпеки дорожнього руху у м. Тернополі

The article is devoted to the problem of researching road safety issues in the city of Ternopil with the structuring of established actual deficiencies according to the main indicators. As a result of the analysis of traffic flows in the morning period, the most problematic sections of the street and road network were identified. In general, it is observed that in the evening peak period, the speed of traffic flows on the street and road network is significantly lower than in the morning peak period. In order to analyze road traffic safety in the city of Ternopil, a cartographic method was used, which involves superimposing on the map data arrays regarding traffic accident statistics for 2018-2020. This approach makes it possible to determine the places of concentration of road accidents, however, during the analysis, it was found that road accidents in Ternopil mostly occur along the main streets without clearly defined places of concentration. In this regard, a conditional indicator of the danger of transport corridors of Ternopil was derived, which is determined by the ratio of the number of road accidents with victims to the total length of the street, which also allows analyzing the predominant location (clustering) of road accidents with victims in each district of the city. The current policy and practices of Ternopil in the field of road safety management need improvement. In the city, repair work is regularly carried out to maintain the roadway and technical equipment in proper condition, but the quality and volume of the work performed do not fully cover the need to ensure traffic safety and provide information to road users. In general, it can be stated that Ternopil is a city that is just starting proactive actions in the field of traffic safety ("starting city"). In this, it favorably differs from the vast majority of other regional centers, in which the problem of traffic safety is not even discussed so far, and consideration of this issue in terms of sustainable urban mobility of the Ternopil urban territorial community.

Энтропия системного управления безопасностью дорожного движения: методика и практика использования

The safety issues of road transport operation have been relevant for more than 125 years. During this time, the vision of the essence and ideas about the principles and methods of ensuring road safety (traffic safety paradigms) have changed several times in the world scientific community. The purpose of this article is to present to the scientific community a new, developing entropy approach to assessing the quality of traffic safety system management. The need to use entropy approaches is dictated by the awareness of two main circumstances: the extreme variety of forms, composition and, ultimately, the complexity of transport systems, as well as the low effectiveness of using traditional modeling methods in assessing the quality of large and complex systems, which include traffic safety systems. The use of the entropy approach in the field of traffic safety management is very innovative for Russia. Already quite traditional for other spheres of human existence, the methods of entropy assessment of systemic organization are unfamiliar to transport specialists. In this regard, it should be noted that the entropy approach allows solving those tasks in the field of traffic safety that are inaccessible to conventional tools. An example of such tasks can be a quantitative assessment and comparison of the quality of management of regional traffic safety systems. This is especially important for improving the quality of federal traffic safety management, in particular, for improving goal-setting and subsequent financing of the relevant regional traffic safety programs. This is the practical significance of the author’s ideas. This article presents a method for quantifying the Relative entropy Hn of regional traffic safety systems; comparison and explanation of different levels of organization of traffic safety systems in the Orenburg region and the Republic of Tyva; an entropy classification of regional traffic safety systems in Russia by levels of system organization is presented (2021); an attempt was made to philosophically comprehend the results obtained. The results presented in the article are intermediate. The ideology of using the entropy assessment of system organization can be used not only in relation to such an important aspect of the functioning of transport systems as traffic safety, but also to the identification of other system properties, such as efficiency and quality. Key words: road safety, management, quality, system (process) entropy, entropy approach.

Proceedings in the case of administrative offenses in the field of traffic safety

Proceedings in the case of administrative offenses in the field of traffic safety

Classification of criminal offenses in the field of traffic safety

Classification of criminal offenses in the field of traffic safety

Abdominal injuries in road traffic accidents – an autopsy study

Background/Aim. Traffic accident injuries are a major public health problem worldwide, with millions of people dying every year. Although the improvement of traffic safety is based on preventive action, it is necessary to conduct an analysis of victims in traffic in order to prevent future traffic accidents with the help of such findings. The aim of the study was to determine the manner of occurrence of abdominal injuries (AIs), gender differences, and the sitting position in the vehicle that affects the severity of AIs, as well as which AIs lead more often to fatalities in traffic accidents. Methods. Materials for the present study were collected from the medico-legal autopsies, showing AIs that occurred in road traffic accidents. The total number of people who died from sustained injuries or complications of the injuries after road traffic accidents was 525. Results. In the study sample, 38.3% of subjects sustained abdominal trauma. Men were more likely to have AIs, and passengers in the front seat and motorcycle drivers were identified as the most vulnerable category of these types of injuries. The liver was the most commonly injured abdominal organ. Liver destructions, liver lacerations, spleen lacerations, intestinal injuries, the simultaneous occurrence of head, chest, and AIs, or the simultaneous occurrence of head and AIs in a higher percentage led to death at the scene. Conclusion. The results of this study represent the basis for creating educational content for all traffic participants as well as the legislation in the field of traffic safety.

Separate issues of effectiveness of measures of administrative responsibility for violations in the field of traffic safety and road infrastructure in Ukraine

Separate issues of effectiveness of measures of administrative responsibility for violations in the field of traffic safety and road infrastructure in Ukraine

Further compelling evidence for safety-in-numbers: It is more than meets the eye

In the extant road safety literature, estimating safety-in-numbers is dominated by conventional cross-sectional methods in which active mode (pedestrian or cyclist) volume together with motorised traffic volume are present in regression models explaining active mode safety directly. There is “direct” evidence for safety-in-numbers when the coefficient associated with active mode volume is negative (safety improves as volume increases) or when it is smaller than one (safety decreases at a lower rate compared to the rate of increase in active mode volume). In this article we extend the concept of safety-in-numbers in the traffic safety field, introducing “indirect” safety-in-numbers, which constitutes a new form of evidence for this phenomenon. We provide empirical evidence to support this, discussing that using an approach based on heterogeneity in mean modelling–a form of random parameters (slopes) models–it is possible to reveal “indirect” safety-in-numbers effects. Therefore, such models can reveal further compelling evidence for safety-in-numbers. Accurate knowledge of safety-in-numbers effects (both direct and indirect) and their underlying mechanisms can help provide robust motives for promoting active travel and will have valuable implications for the design of road safety interventions.

The long-memory temporal dependence of traffic crash fatality for different types of road users

This study investigates the long-memory auto-correlations within daily fatality time series data of the driver, passenger, bicyclist, and pedestrian, and the long-memory cross-correlations between the fatality series of different road users. The analysis is based on the traffic crash fatalities in Florida during the period 1975–2015. A detrended fluctuation analysis (DFA) and a detrended cross-correlation analysis (DCCA) are used for capturing the long-memory auto-correlations and the cross-correlations, respectively. The investigation is conducted through the following steps: (1) identifying the periodicity of the traffic fatalities series via traditional signal processing methods; (2) removing the season effects identified by simple average value analysis and principal component analysis (PCA) from the original time series; (3) based on the original and processed series, exploring the long-memory auto-correlations and cross-correlations in the time series data of different road users. The results of DFA indicate that the auto-correlations of the fluctuations in the daily fatalities are positive for all road users regardless of the seasonal effects. The results of DCCA indicate that the fluctuation in the fatalities for any type of road user is positively associated with that for other road users. The fluctuation in traffic fatalities for any road user is therefore not random-walk but correlated to their fluctuations in the last time unit and that of other road users during the same period. The findings of this study add to the understanding of the long-memory property in traffic fatalities and provide implications for the development of time series models in the traffic safety field.