Road Traffic Safety Management Research Articles

Driving risk prediction of urban arterial and collector roads using multi-dimensional real-time data

With the objective of predicting driving risks on urban arterial and collector roads, this paper primarily collects and processes vehicle trajectory data and driver-vehicle-road-environment data. Using Entropy-Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and K-means, risk scores and levels for the target roads are determined. An original dataset comprising 10,320 samples is established, with risk levels as labels and multi-dimensional data as features. SHapley Additive exPlanations (SHAP) analysis is utilized to extract important features. The performance of four ensemble models are compared, including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and Stacking. Three data resampling techniques are integrated to address data imbalance. The combination of LightGBM and Deep Convolutional Generative Adversarial Network (DCGAN) outperforms other approaches, showing potential for real-time risk prediction. Besides, DCGAN is employed to update the multi-dimensional features. The models' predictive performance is evaluated under various update ratios using F1-score and training time, with LightGBM demonstrating superior accuracy and efficiency compared to its counterparts (RF, XGBoost, and Stacking). This study develops a driving risk prediction system based on multi-dimensional features, offering a theoretical foundation and technical approach to enhance urban road traffic safety management.

Dynamics and Forecast of Road Traffic Safety Management in Russia until 2030

Dynamics and Forecast of Road Traffic Safety Management in Russia until 2030

Road accident risk analysis of at- and near-port areas reflecting regional heterogeneous characteristics

ABSTRACT In port cities, road traffic safety management should consider various types of cargo that will shift in the trucks and containers. This applies not only in the case of ‘at port’ areas within a 2-km radius but also ‘near-port’ areas within a 4–8-km radius. This is important for traffic safety because the crash severity can be high as there are many trucks to the ports that can cause large-scale crashes. Therefore, to prepare traffic safety management strategies for port city, it is necessary to identify crash risk factors that affect the crash severity. This study developed a two-stage road crash severity model of ‘at-port’ and ‘near-port’ regions using a support vector machine and a Bayesian multinomial model. Additionally, a severity model suitable for Korean ports was proposed that considered spatial heterogeneity in the Bayesian multilevel multinomial model. Findings derived from this study can be used for policy development to increase road traffic safety in port cities. ‘This article is a revised and expanded version of a paper entitled [Accident Risk Analysis of At and Near-Port Areas Reflecting Regional Heterogeneous Characteristics] presented at [The 11th International Conference on Logistics and Maritime Systems, Korea, 4-7 September 2023]’.

An Adaptive Vehicle Detection Model for Traffic Surveillance of Highway Tunnels Considering Luminance Intensity.

Vehicle detection is essential for road traffic surveillance and active safety management. Deep learning methods have recently shown robust feature extraction capabilities and achieved improved detection results. However, vehicle detection models often perform poorly under abnormal lighting conditions, especially in highway tunnels. We proposed an adaptive vehicle detection model that accounts for varying luminance intensities to address this issue. The model categorizes the image data into abnormal and normal luminance scenarios. We employ an improved CycleGAN with edge loss as the adaptive luminance adjustment module for abnormal luminance scenarios. This module adjusts the brightness of the images to a normal level through a generative network. Finally, YOLOv7 is utilized for vehicle detection. The experimental results demonstrate that our adaptive vehicle detection model effectively detects vehicles under abnormal luminance scenarios in highway tunnels. The improved CycleGAN can effectively mitigate edge generation distortion. Under abnormal luminance scenarios, our model achieved a 16.3% improvement in precision, a 1.7% improvement in recall, and a 9.8% improvement in mAP_0.5 compared to the original YOLOv7. Additionally, our adaptive luminance adjustment module is transferable and can enhance the detection accuracy of other vehicle detection models.

Analysis of Factors Causing Urban Road Traffic Accidents Based on XGboost Algorithm

To further strengthen road traffic safety management and improve the accuracy of early warning systems for road traffic safety, a model for factors causing urban road traffic accidents based on the XGBoost algorithm is proposed. Firstly, SMOTE is used to process the unbalanced data, including supplementing the missing values, deleting the duplicate values, and visualizing the data. Then the prediction model is built by the XGBoost algorithm. By comparing and analyzing the results with the LR model, linear SVM model, DT model and Lightgbm model, the average accuracy rate of the XGBoost model reaches 0.979. Based on the XGBoost algorithm, the analysis of the factors causing urban road traffic accidents has better prediction performance, which can provide a reliable reference for preventing traffic accidents.

A multiscale enhanced pavement crack segmentation network coupling spectral and spatial information of UAV hyperspectral imagery

Road pavement cracks are a critical factor affecting the health conditions of road pavements. Accurate crack detection contributes to providing data support for road maintenance measures. Compared to conventional crack detection algorithms, deep learning based crack segmentation methods have practical significance for road maintenance and traffic safety management due to their high precision and automation. However, existing deep learning methods often suffer from segmentation accuracy loss due to the varying crack sizes and the presence of stains on the pavement with spatial characteristics similar to cracks, leading to the misclassification of cracks. Therefore, this study proposed a multiscale enhanced road pavement crack segmentation network (MS-CrackSeg) by coupling spectral and spatial information to detect pavement cracks from unmanned aerial vehicle (UAV) hyperspectral imagery. MS-CrackSeg can simultaneously learn the spatial and rich spectral features of cracks in the hyperspectral imagery, improving the discrimination of those targets with similar spatial features to cracks compared to previous approaches. Moreover, the Multiscale Self-Attention-like Feature Extraction Module (MSSA) is introduced to extract and fuse multiscale crack features to enhance the crack detection. Experiments on a dataset consisting of 1031 hyperspectral images demonstrated the superior crack segmentation of the proposed method compared to the comparative methods. In particular, the proposed method achieved the highest F1-score of 0.74 and mean Intersection over Union of 0.79, indicating an exceptional performance. The developed approach offers improved data support for road maintenance measures and has validated the advantages of UAV hyperspectral imagery in road crack segmentation. The annotated hyperspectral dataset and the code for MS-CrackSeg network are available at https://github.com/williamchen-x/MS-CrackSeg.

Evaluating the Efficacy of Real-Time Connected Vehicle Basic Safety Messages in Mitigating Aberrant Driving Behaviour and Risk of Vehicle Crashes: Preliminary Insights from Highway Scenarios

Connected vehicle (CV) technology has revolutionised the intelligent transportation management system by providing new perspectives and opportunities. To further improve risk perception and early warning capabilities in intricate traffic scenarios, a comprehensive field test was conducted within a CV framework. Initially, data for basic safety messages (BSM) were systematically gathered within a real-world vehicle test platform. Subsequently, an innovative approach was introduced that combined multimodal interactive filtering with an advanced vehicle dynamics model to integrate BSM vehicle motion data with observations from roadside units. In addition, a driving condition perception methodology was developed, leveraging rough sets and an enhanced support vector machine (SVM), to identify aberrant driver behaviours and potential driving risks effectively. Furthermore, this study integrated BSM data from various scenarios, including car-following, lane changes, and free driving within the CV environment, to formulate multidimensional driving state sequence patterns for short-term predictions (0.5 s) utilising the long short-term memory (LSTM) model framework. The results demonstrated the effectiveness of the proposed approach in accurately identifying potentially hazardous driving conditions and promptly predicting collision risks. The findings from this research hold substantial promise in advancing road traffic safety management.

Exploring the Impact of Rainfall on Vehicle Trajectory Patterns and Sideslip Risk: An Empirical Investigation

Understanding the sideslip risks of various trajectory patterns, as well as the impact of rainfall on them, is critical for improving road safety. However, the lack of precise classification indicators hampers systematic analysis of the variations in vehicle trajectory patterns. To address this, this study proposes a parameterized classification method for trajectories on curved segments, employing the radius and offset of the trajectory as the primary classification features and dividing the trajectories into nine patterns. These patterns represent variations from smaller to larger radii and inside to outside lane offsets, reflecting different driving behaviors and vehicle stability during vehicle cornering. Concurrently, the friction coefficient utilization rate is used to effectively compare vehicles’ sideslip risk under different weather conditions. Based on this, we construct a framework using computer vision technology for automatically identifying trajectory patterns and measuring sideslip risk. We conducted an empirical study on a highway-curved segment with high sideslip risk in China and collected two datasets under clear and rainy conditions for analysis. The classification results show that the proposed method can effectively classify trajectories according to nine trajectory patterns. Comparative analysis reveals that vehicle trajectories in both the inside and outside lanes are notably more affected by rainfall compared to the middle lane. Meanwhile, trucks demonstrate a higher susceptibility to rainfall than cars. In addition, the analysis of the sideslip risk for different trajectory patterns discovers several high-risk patterns. This study provides an effective approach for monitoring and analyzing the sideslip risk on curved segments, thereby contributing to the enhancement of road design and traffic safety management.

Оптимізація управлінських підходів для впровадження та ефективності системи менеджменту безпеки дорожнього руху

The issues of optimizing management approaches for effective implementation of road traffic safety management systems in enterprises are considered. The authors examine internal and external factors affecting road safety and explore methods to enhance management approaches to ensure a high level of safety. Special attention is paid to process and risk-oriented approaches, as well as the importance of interaction with stakeholders. It is noted that a successful safety management strategy involves integrating safety into all areas of enterprise activities and continuously monitoring the system. Research confirms that adopting such a risk-oriented approach significantly enhances the effectiveness of road safety management systems. Reduction of risks and improvement of safety are the outcomes of implementing this strategy, enabling enterprises to more efficiently control and manage potential road hazards. Future research plans include focusing on developing new methods to assess the effectiveness of safety management systems, aimed at further improving the functionality and reliability of such systems. Additionally, there are plans to conduct a detailed study on the impact of innovative technologies on the functioning of road safety systems, to implement the most effective technical solutions. There are also plans to expand the application of safety management systems across various types of enterprises to ensure an optimal level of safety for a wide spectrum of road users. This may include not only transportation companies but also construction, logistics, and other industries involved in the transportation of goods and services. Taking into account the prospects of further research, it is expected that they will contribute to further improving road safety and ensuring stability for all road users, which is a crucial step in providing safety and comfort on highways.

GIS-based analysis of spatial\u2013temporal correlations of urban traffic accidents

BackgroundUnderstanding the spatial–temporal distribution characteristics of urban road traffic accidents is important for urban road traffic safety management. Based on the road traffic data of Wales in 2017, the spatial–temporal distribution of accidents is formed.MethodsThe density analysis method is used to identify the areas with high accident incidence and the areas with high accident severity. Then, two types of spatial clustering analysis models, outlier analysis and hot spot analysis are used to further identify the regions with high accident severity.ResultsThe results of density analysis and cluster analysis are compared. The results of density analysis show that, in terms of accident frequency and accident severity, Swansea, Neath Port Talbot, Bridgend, Merthyr Tydfil, Cardiff, Caerphilly, Newport, Denbighshire, Vale of Glamorgan, Rhondda Cynon Taff, Flintshire and Wrexham have high accident frequency and accident severity per unit area. Cluster analysis results are similar to the density analysis. Finally, the temporal distribution characteristics of traffic accidents are analyzed according to month, week, day and hour. Accidents are concentrated in July and August, frequently in the morning rush hour and at dusk, with the most accidents occurring on Saturday.ConclusionsBy comparing the two methods, it can be concluded that the density analysis is simple and easy to understand, which is conducive to understanding the spatial distribution characteristics of urban traffic accidents directly. Cluster analysis can be accurate to the accident point and obtain the clustering characteristics of road accidents.

Bus crash risk evaluation: An adjusted framework and its application in a real network

Greater attention to bus safety can lead to relevant benefits for public transport companies in terms of higher service performance, reliability, and lower insurance costs. Therefore, measuring the crash risk on bus routes provides an opportunity to improve the safety performance of transit operators. Previous research has explored the effects of many factors regarding the frequency and severity of bus crashes, whereas only a handful of studies have defined some crash risk indexes. Conversely, to the best of our knowledge, almost no research has been done regarding the crash risk in the bus transit network that integrates frequency, severity, and the exposure factors. This paper proposes a new framework to assess the crash risk for each transit bus route by the integration of safety factors, prediction models and risk methods. More precisely, this framework identifies several safety factors and specifies the risk components in terms of frequency, severity and exposure factors that may affect bus crashes. Then, it models their relationships to build a bus crash risk function. Lastly, according to the values returned by the previous function, the crash risk for each route is computed and a safety performance ranking for each route is provided. The feasibility of this framework is demonstrated in a real case study by using bus crash data provided by a mid-sized Italian bus operator. The findings show that transit managers could implement this framework in a road traffic safety management system to evaluate the risk of crashes on routes, monitor the safety performance of each route and qualify each route according to recent safety norms.

Selected Aspects of the Road Traffic Safety Management System

The article is an attempt to present the two modern concepts of safety management systems. The first is the sunflower pyramid used especially in Europe, the second is the ISO 39001 standard (Road Safety Management System) of international scope. The idea and possibilities offered by use of both concepts of safety management, as well as an attempt to assess the impact of their actions on decrease in fatalities in selected EU countries, were presented. As a result of the conducted analyses, a downward trend was noted as to the number of certificates issued for compliance with the ISO 39001 standard in the world, which may indicate an appearance of the more adequate management systems, or other tools or concepts to improve the road safety, or inadequate education society and low level of awareness in this area of stakeholders, including politicians, scientists, producers, drivers (professional and reliable), as well as other road users, including unprotected ones.

Assessing the Risk of Bus Crashes in Transit Systems

Although public transport buses may be considered a safe transportation mode, bus safety is a crucial issue from the perspectives of operators, passengers and local authorities owing to the relevant implications it generates. Therefore, assessing the risk of crashes on bus routes may help improve the safety performance of transit operators. Much research has identified patterns of bus crashes to understand the effects of many factors on the frequency and the severity of them. Conversely, to the best of our knowledge, the research measuring the risk of crashes in bus transit networks is seldom faced. This paper adjusts existing methods to assess the safety on bus transit networks by the integration of safety factors, prediction models and risk methods. More precisely, first, the methodology identifies several safety factors as well as the exposure risk factors. Second, this methodology specifies the risk components in terms of frequency, severity and exposure factors that may affect bus crashes and models their relationships in a risk function. Third, this methodology computes the risk of crashes for each route and provides a ranking of safety performance. A real case study demonstrates the feasibility of this methodology using 3,457 bus crashes provided by a mid-sized Italian bus operator. This experiment shows that transit managers could adopt this methodology to perform an accurate safety analysis on each route. Moreover, this methodology could be implemented in a road traffic safety management system in order to evaluate the risk of crashes on routes, monitor the safety performance of each route and qualify each route according to recent safety norms.

Research on Characteristics of Perpetrators and casualties in Road Traffic Accidents in China——Based on the panel data analysis of national traffic accidents from 2006 to 2019

Under the background of the rapid development of Internet technology, the emergence of Internet platform accelerates the industrial agglomeration. As a chain connecting the material circulation between industries, the rise of logistics industry is inevitable. As a key link of logistics industry, transportation, along with the rise of logistics industry, has led to an increase in traffic accidents year by year. Therefore, based on the national road traffic accident data from 2006 to 2019, this paper uses SPSS 26.0 to analyse the accident incidence, casualties and direct property loss indicators of different regions and occupation, and study the characteristics of perpetrators and casualties. The results show that the traffic accidents in the whole country show a downward trend, but they are unbalanced. Workers and farmers accounted for the largest proportion, but the death and injury rate showed a downward trend. Other showed an upward trend in the past five years, especially in the delivery industry. Future road traffic safety management should take corresponding measures to reduce the accident rate from the perspective of the development of related industries.

Traffic Accidents Detection using Geographic Information Systems (GIS)

The mission of reducing the number and severity of traffic accidents becomes the dominant target of road traffic safety management worldwide. The main objective of this work is to analyze traffic accidents in temporal and spatial frameworks in the capital city Amman and identify hotspot zones in the study area. Several statistical analyses are conducted using SQL to give insight into the temporal distribution of accidents and to identify the most revealing accidents based on several attributes such as the year of accidents, the severity of accidents, road type, and lighting environment which enables the authors to do further investigations on the more frequent accidents. GIS-based statistical and spatial analysis tools are utilized to examine the spatial pattern of accident distribution in the study area for three successive years, hotspots are identified for clusters of high concentrations. The Nearest Neighbor Index (NNI) is used to analyze the pattern of traffic accident distribution based on selective parameters. This was followed by identifying hotspot zones for regions that showed clustering using the optimal hotspot analysis tool. Experimental results showed clustering for all tested groups, and thus hotspots were detected for these accidents in the study area. The importance of this work is in providing a spatial understanding of accident distribution in the capital city of Amman which can help policymakers of road safety setting out efficient strategies for traffic safety management and find optimal solutions as required for factors causing such accidents.

Exploring the influencing factors of public traffic safety awareness in China

This research aims to develop a public traffic safety awareness questionnaire and explore the factors influencing public traffic safety awareness in China. A questionnaire survey was conducted with 5200 people in Jiaozuo, China, to assess their public traffic safety awareness. Based on 5029 effective samples, the developed public traffic safety awareness scale passed the reliability and validity tests and identified three factors: traffic safety behaviour, traffic safety attitude and traffic accident cognition. The Chinese version of the Traffic Climate Scale (three factors: external affective demand, internal requirements and functionality) and the Ten-Item Personality Inventory in China (five factors: extraversion, agreeableness, conscientiousness, emotional stability and openness) were also used in the questionnaire. The results of correlation analysis, difference analysis and multiple linear regression analysis show that in terms of individual factors, public traffic safety awareness is significantly related to age, education level, number of children and personality traits, and there are significant differences between genders and across occupations. In terms of environmental factors, public traffic safety awareness is significantly related to traffic safety education, traffic violation records, approval of traffic violations and traffic climate. Internal requirements, external emotional demands, conscientiousness and agreeableness can effectively explain and predict public traffic safety awareness. The research results provide a strong reference to help road traffic safety management departments formulate policies and play a positive role in preventing and reducing traffic accidents and ensuring public safety.

Evaluating bus accident risks in public transport

Public transit buses may be considered a safer transportation mode as opposed to others (e.g., private cars). However, safety is a crucial issue regarding transit buses from the perspectives of operators and passengers due to the relevant implications it generates. Therefore, evaluating the accident risk on bus routes provides an opportunity to improve the safety performance of transit operators. Previous research identified patterns of bus accidents and shed light on understanding the effects of many factors regarding frequency and severity of bus accidents. However, no studies have investigated accident risks in bus transit, while considering frequency, severity and exposure factors in a single function.This paper proposes a new methodology for evaluating the accident risk for each transit bus route. At first, the methodology identifies the risk components in terms of frequency, severity and exposure factors that may affect bus accidents. Next, it integrates these terms, to build a risk bus accident function providing a ranking of safety performance for each route.The feasibility of this methodology is demonstrated in a real case study using 3,457 bus accidents provided by a mid-sized Italian bus operator. This experiment shows that transit managers could adopt this methodology to perform an accurate safety analysis on each route. Moreover, this methodology may be implemented in a road traffic safety management system for bus transit operators interested in the monitoring of safety performance, in the evaluation of the risk of accidents on routes, and in the certification process according to recent safety norms.

Road traffic safety: An analysis of the cross-effects of economic, road and population factors

PurposeThrough the study of economic, traffic and population data related to road traffic accidents from 2004 to 2016, this paper analyzed the impact of various factors on road traffic casualties in China, and provided theoretical basis and suggestions for the road traffic safety management in China. MethodsBased on three aspects (economy, road, population) with five factors (gross domestic product (GDP), traffic investment, new vehicle ownership, new road mileage and newly increased population), this paper collected the relevant data of road traffic accidents in 31 provinces and cities in China, from 2004 to 2016. A panel model was established to carry out empirical analysis. ResultsAll factors have a significant impact on the number of road traffic accident casualties. When other factors remain unchanged, the number of road traffic casualties decreased by an average of 0.19 for every 100 million CNY increased in GDP. For every 100 million CNY increased in traffic investment, the number of road traffic casualties is reduced by an average of 13.93, indicating that economic development can improve road traffic safety to a certain extent. On the contrary, the growth in road mileage, new motor vehicles and population has increased the number of road traffic casualties. For every 10, 000 km of new road mileage, the number of traffic accident casualties has increased by 284.04. For every 10,000 newborns, the number of road traffic casualties increased by 7.33; as the number of new motor vehicles increases by 10,000, the number of road traffic casualties increased by an average of 21.77. ConclusionThe increase of GDP and traffic investment can significantly reduce the number of road traffic casualties in China, which shows that economic development is essential to improve road traffic safety. The numbers of new road mileage, newly increased population and the new motor vehicles are positively correlated with the number of traffic accident casualties in traffic accidents, which reflects the existing problems in road design, distribution of road resources, and traffic management in China. Therefore, it is necessary to improve the economic and road related aspects to improve road traffic safety.

Improving Road Traffic Safety Through the Use of Innovative Technologies

The use of innovative technologies is the most effective and promising way to improve the road traffic safety management system. The relevance of the work is expressed in the need for continuous implementation of innovations in the context of growing disproportion between the sharp increase in the number of vehicles, high traffic intensity, and imperfection of road traffic management (problems of existing infrastructure, poor information support for road users, etc.), which results in road traffic injuries and deaths. In the course of the research, it was found that in the context of the Republic of Adygea, using rumble strips installed at the road shoulder reduces the number of run-off-road collisions by 30%, while using centerline rumble strips reduces the number of cross-over incidents and resultant head-on collisions by 15%.

Mobile phone use while cycling: A study based on the theory of planned behavior

To examine the factors that influence mobile phone use while cycling, mobile phone addiction and perceptions of distraction were included in the theory of planned behavior (TPB) model to explore the relationship between the psychological factors of mobile phone use and cycling from the perspective of social psychology. This study tested the reliability and validity of an extended TPB questionnaire (with 603 eligible responses), explored the correlation between personality factors and other psychological factors, and constructed a structural equation model of mobile phone use behavior while cycling based on the improved TPB model. Consequently, the relationship path between various influencing factors was obtained. The results indicate that neuroticism, conscientiousness, and agreeableness are significantly associated with psychological factors. In addition, the TPB can effectively explain and predict mobile phone use behavior while cycling. Mobile phone addiction, distraction perception and behavioral intention are the most important influencing factors, followed by perceived behavioral control. The influence of attitude and subjective norms is weak. The research results may have positive effects on the prevention and reduction of bicycle accidents and improvements to the safety of cyclists. Additionally, the results provide strong evidence to support the policies of the road traffic safety management department.