Risk Of Traffic Accidents Research Articles

Vehicle trajectory dataset from drone videos including off-ramp and congested traffic – Analysis of data quality, traffic flow, and accident risk

Vehicle trajectory data have become essential for many research fields, such as traffic flow, traffic safety, and automated driving. To make trajectory data useable for researchers, an overview of the included road section and traffic situation as well as a description of the data processing methodology is necessary. In this paper, we present a trajectory dataset from a German highway with two lanes per direction, an off-ramp and congested traffic in one direction, and an on-ramp in the other direction. The dataset contains 8,648 trajectories and covers 87 ​min and an ∼1,200 ​m long section of the road. The trajectories were extracted from drone videos using a posttrained YOLOv5 object detection model and projected onto the road surface via three-dimensional (3D) camera calibration. The postprocessing methodology can compensate for most false detections and yield accurate speeds and accelerations. The trajectory data are also compared with induction loop data and vehicle-based smartphone sensor data to evaluate the plausibility and quality of the trajectory data. The deviations of the speeds and accelerations are estimated at 0.45 ​m/s and 0.3 ​m/s2, respectively. We also present some applications of the data, including traffic flow analysis and accident risk analysis.

Experiences and needs of persons who have undergone limb amputation in Saki West, Oyo State, Nigeria.

Amputation is a life-changing experience involving the surgical removal of a body part. However, little is known about the experiences of persons who have undergone limb amputation in the Saki West Local Government Area (LGA), which prompted this study. An exploratory qualitative study design was used. Thirty participants were interviewed using face-to-face focus group discussions. Five sessions were conducted, with six participants in each session. Data were analysed using the thematic analysis framework of Braun and Clarke. A total of 76.3% of the participants were males and 73.3% were married. Their ages ranged from 22 and 69y, with a mean age of 48.0y and a monthly income of 20000 naira (US$12). Three major themes emerged from the study: the needs and challenges of those who have undergone amputation, coping strategies of those who have undergone amputation and recommendations by persons who have undergone amputation. The study revealed that people who have undergone amputation experienced psychological pain, such as a loss of self-worth and stigmatization from friends and loved ones. Coping strategies identified were accepting the situation and emotional support from family. Participants recommended providing financial support, empowerment programs, employment opportunities and fostering a community of amputees in the Saki West LGA. Amputees undergo psychological and psychosocial problems that can affect their health and recovery. They need emotional and financial support, rehabilitation services and provision of prostheses from family, society and the government. These services should be adequately provided in the Saki West LGA, the largest among the 10 suburban local governments in Oyo State, with numerous commercial activities and a high risk for road traffic accidents.

Can Historical Accident Data Improve Sustainable Urban Traffic Safety? A Predictive Modeling Study

Traffic safety is a critical factor for the sustainable development of urban transportation systems. This study investigates the impact of historical accident information on the prediction of future traffic accident risks, as well as the interaction between this information and other features, such as driver violations and vehicle attributes. Using a comprehensive dataset of traffic accidents involving passenger vehicles in a western Chinese city, we developed two predictive models: Model 1, which is based on vehicle information and driver violations, and Model 2, which integrates historical accident data. The results indicate that the inclusion of historical accident information significantly enhances the predictive performance of the model, particularly in terms of AUC (Area Under the Curve) and AP (Average Precision) values. Furthermore, through feature importance analysis and SHAP (SHapley Additive exPlanations) value evaluation, this study reveals the interaction effects between historical accident data and other features, and how these interactions influence model decisions. The findings suggest that historical accident data play a positive role in predicting future accident risk, with varying effects on risk mitigation. These insights provide a scientific basis for developing strategies to ensure the sustainable development of urban transportation systems.

Reduce Speed Limits to Minimize Potential Harm and Maximize the Health Benefits of Street Trees

Urban greening is threatened by the concern that street trees increase traffic-related injury/death. Associations between all serious and fatal traffic crashes and street tree percentages were examined in Sydney, Australia. Associations were adjusted for confounding factors relating to driver behavior (speeding, fatigue, and use of alcohol) and road infrastructure, including alignment (e.g., straight, curved), surface condition (e.g., dry, wet, ice), type (e.g., freeway, roundabout), and speed limit. Models indicated that 10% more street trees were associated with 3% and 20% higher odds of serious or fatal injuries and 20% tree collisions on roads of any speed, respectively. However, further analysis stratified by speed limit revealed contrasting results. Along roads of 70 km/h or greater, 10% more street trees were associated with 8% higher odds of serious or fatal injury and 25% higher odds of death. Comparable associations were not found between street trees and serious or fatal injuries along roads below 70 km/h. Reducing speed limits below 70 km/h saves lives and may mitigate risks of serious or fatal traffic accidents associated with street trees, enabling greener, cooler, healthier cities.

Assessing braking performance on wet-road through water-depth estimation and vehicle-pavement dynamic simulation

ABSTRACT The presence of water on wet roads due to rainfall has emerged as a critical factor influencing driving safety. During severe weather, the non-uniform water distribution on the road can significantly diminish pavement friction, elevating the risk of traffic accident. Furthermore, the tire-pavement friction coefficient varies with vehicle velocity, rendering wet braking analysis complex. Conventional braking risk analysis often assumes a fixed friction coefficient, neglecting the impact of uneven pavement-induced water depth irregularities. This paper introduces a novel braking performance assessment method based on water-depth estimation and vehicle-pavement simulation. The uneven water-depth distribution is first estimated using LiDAR-measured pavement geometry. A co-simulation framework is then proposed to analyze the tire-pavement friction and study the dynamic braking performance. The 85th percentile stopping distance is adopted as the evaluation index for quantifying braking risk. Results from case studies highlight the substantial influence of rainfall intensity and vehicle velocity on braking risk. Additionally, pavement rutting accumulates deeper water depth, thereby elevating braking safety risks. The proposed model offers a novel perspective on vehicle braking risk analysis and can serve as a valuable safety indicator for highway transportation management and decision-making in driving strategies during wet weather conditions.

Paediatric e-scooter riders at high risk of life-threatening traffic accidents.

This study presents the epidemiology and the development of e-scooter and non-electric scooter injuries in children and adolescents to highlight the nature and the severity of such injuries. All children and adolescents with electric or non-electric scooter related injuries between January 2019 until December 2022 were included in this single-centre study and retrospectively evaluated. An epidemiological overview including age, sex, type of scooter, injury mechanism, diagnosed injuries and surgical interventions were presented. Further, statistical comparisons of the means and proportions of injuries and the number of surgical interventions between electric and non-electric scooter riders were made. The study consists of 633 children and adolescents (9.1 ± 5.0 years; 231 female, 36.5%, 402 male, 63.5%). 80.1% (8.0 ± 4.1 years) used non-electric scooters, while 19.9% (14.2 ± 4.1 years) used e-scooters. In comparison, adolescent e-scooter riders were significantly more likely to be involved in traffic accidents (non-electric scooter: 16/507; e-scooter: 21/126; p = 0.0001) and suffered severe head injuries more often (non-electric scooter: 8/134; e-scooter: 7/37; p = 0.0217) than patients riding non-electric scooters. Adolescent e-scooter riders were more likely to be involved in potentially life-threatening traffic accidents having a higher risk of severe head injuries requiring surgery compared to non-electric scooter riders. Adolescent e-scooter riders have a higher risk to be involved in potentially life-threatening traffic accidents than riders of non-electric scooters. E-scooter riders have a higher risk of sustaining serious head injuries requiring surgical intervention than non-electric scooter riders. The data highly recommends the implementation of prevention strategies especially educating youth and parents and wearing protective clothing. Legislation and law enforcement could help prevent such injuries and potentially life-threatening traffic accidents involving children and adolescents who ride e-scooters.

Road Defect Identification and Location Method Based on an Improved ML-YOLO Algorithm.

The conventional method for detecting road defects relies heavily on manual inspections, which are often inefficient and struggle with precise defect localization. This paper introduces a novel approach for identifying and locating road defects based on an enhanced ML-YOLO algorithm. By refining the YOLOv8 object detection framework, we optimize both the traditional convolutional layers and the spatial pyramid pooling network. Additionally, we incorporate the Convolutional Block Attention to effectively capture channel and spatial features, along with the Selective Kernel Networks that dynamically adapt to feature extraction across varying scales. An optimized target localization algorithm is proposed to achieve high-precision identification and accurate positioning of road defects. Experimental results indicate that the detection accuracy of the improved ML-YOLO algorithm reaches 0.841, with a recall rate of 0.745 and an average precision of 0.817. Compared to the baseline YOLOv8 model, there is an increase in accuracy by 0.13, a rise in recall rate by 0.117, and an enhancement in average precision by 0.116. After the high detection accuracy of road defects was confirmed, generalization experiments were carried out on the improved ML-YOLO model in the public data set. The experimental results showed that compared with the original YOLOv8n, the average precision and recall rate of all types of ML-YOLO increased by 0.075, 0.121, and 0.035 respectively, indicating robust generalization capabilities. When applied to real-time road monitoring scenarios, this algorithm facilitates precise detection and localization of defects while significantly mitigating traffic accident risks and extending roadway service life. A high detection accuracy of road defects was achieved.

Development of micromodels for improvements in driving safety using as a case study a university campus in Panama

This research focuses on addressing critical driving safety issues on university campuses, particularly vehicular congestion, inadequate parking, and hazards arising from the interaction between vehicles and pedestrians. These challenges are common across campuses and demand effective solutions to ensure safe and efficient mobility. To address these issues, the study developed detailed microsimulation models tailored to the Victor Levi Sasso campus of the Technological University of Panama. The primary function of these models is to evaluate the effectiveness of various safety interventions, such as speed reducers and parking reorganization, by simulating their impact on traffic flow and accident risk. The models provide calculations of traffic parameters, including speed and travel time, under different safety scenarios, allowing for a comprehensive assessment of potential improvements. The results demonstrate that the proposed measures significantly enhance safety and traffic efficiency, proving the model’s effectiveness in optimizing campus mobility. Although the model is designed to tackle specific safety concerns, it also offers broader applicability for addressing general driving safety issues on university campuses. This versatility makes it a valuable tool for campus planners and administrators seeking to create safer and more efficient traffic environments. Future research could expand the model’s application to include a wider range of safety concerns, further enhancing its utility in promoting safer campus mobility.

Machine learning and multisource data analysis approach towards traffic accident risk prediction

Traffic accidents are continually a concern across the world bringing with them huge economic costs and high rates of fatality and injuries each single year. Predictive modeling can lessen such risks if the accident hotspots and timing can easily be ascertained. This study collects various data including historical accident reports, water and meteorological conditions, road characteristics, and telematics data, to form traffic accident risk predictive models. To construct and analyze these predictive models, we use various machine learning techniques such as logistic regression, decision trees, and ensemble methods (Random Forest and Gradient Boosting). The results bear out as efficient and effective, ensemble models are streets ahead than regression-based techniques with Gradient Boosting achieving AUC-ROC scores of up to 0.89. The results of this research can also assist in applying effective safety measures in a more cost-effective manner as well as facilitate the development of efficient transport management systems and policies.

Design System of Solar-Powered Safety Lamp Integrated Proximity Sensor as a Solution for Accident Prevention at Blind Spot Areas

The curved contour of the road creates blind spots which increase the risk of traffic accidents. Throughout 2019, there were 107,500 accident cases recorded in Indonesia. Therefore, efforts are needed to provide road infrastructure that supports driving safety, such as traffic signs to reduce the risk of accidents in blind spot areas. This research aims to design a smart safety light warning system that is integrated with solar-based street lighting. The design uses a design-based research approach referring to several established standards including Indonesia National Standard (SNI) 7931: 2008, SNI IEC 60529:2014, Regulation of the Minister of Transportation of the Republic of Indonesia Number PM 13 of 2014, Manual on Uniform Traffic Control Devices (MUTCD) issued by the Federal Highway Administration (FHWA) of the United States Department of Transportation (USDOT), and Article 115 of Minister of Transportation Republic of Indonesia Regulation PM 27 of 2018. This study used Design-Based Research (DBR) as a research methodology. The result of this study is a prototype design and development of smart safety light warning system which can be optimized. Validation of the design and circuit is conducted using prototyping modeling 1:20 from the original dimensions to measure the function and performance of the circuit. The prototype is capable of turning on warning lights and siren notifications when detecting objects with a maximum distance of 350 cm as measured by the HC – SR04 sensor. Charging the stored solar panels via the accumulator allows the technology to continue working for 45 hours without charging. For power efficiency purposes, the technology is equipped with an LDR (Light Dependent Resistor) sensor with a minimum limit of 341-lux which regulates the lighting. The development of these safety lights will increase driver awareness when passing through blind spot areas and have positive implications for reducing the number of accidents in Indonesia.

A study of the relation between traffic accidents and election posters

Inattention in traffic is a risk factor for the occurrence of accidents. Elements surrounding traffic could potentially be a safety risk for road users. This study investigates the relation between election posters and traffic accidents using a seasonal autoregressive-moving-average model with exogeneous variables (seasonal ARMAX) correcting for variations in meteorological conditions and general traffic volume indicated through public holidays. Danish traffic accident micro data are used to construct a time series for daily number of accidents in city zones adjusted for probable known accident causes, namely drunk driving, narcotics, drivers deemed unfit to drive, and accidents related to low visibility. Model parameters are determined from Maximum Likelihood estimation on the period from 1 January 1997 to 15 December 2009 with an indicator for general election periods. The presence of general election posters is associated with an increased risk of traffic accidents (p = 0.003), when correcting for meteorological variables and public school holidays. While a causal relation between traffic accidents and the presence of election posters cannot be concluded from the analysis, we find distraction from election posters to be the most likely explanation for the associated increase in traffic accidents. On average, the model indicates that 1.18 additional daily accidents occur for general elections in Denmark. In total, that amounts to 127 additional accidents for the investigated period of time. This study shows that such sudden changes in traffic surroundings are significantly related to increased safety risks which could lead to a better discussion of relevant traffic legislation.

Multimodal Dataset Construction and Validation for Driving-Related Anger: A Wearable Physiological Conduction and Vehicle Driving Data Approach

Anger impairs a driver’s control and risk assessment abilities, heightening traffic accident risks. Constructing a multimodal dataset during driving tasks is crucial for accurate anger recognition. This study developed a multimodal physiological -vehicle driving dataset (DPV-MFD) based on drivers’ self-reported anger during simulated driving tasks. In Experiment 1, responses from 624 participants to anger-inducing videos and driving scenarios were collected via questionnaires to select appropriate materials. In Experiments 2 and 3, multimodal dynamic data and self-reported SAM emotion ratings were collected during simulated and real-vehicle tasks, capturing physiological and vehicle responses in neutral and anger states. Spearman’s correlation coefficient analysis validated the DPV-MFD’s effectiveness and explored the relationships between multimodal data and emotional dimensions. The CNN-LSTM deep learning network was used to assess the emotion recognition performance of the DPV-MFD across different time windows, and its applicability in real-world driving scenarios was validated. Compared to using EEG data alone, integrating multimodal data significantly improved anger recognition accuracy, with accuracy and F1 scores rising by 4.49% and 9.14%, respectively. Additionally, real-vehicle data closely matched simulated data, confirming the dataset’s effectiveness for real-world applications. This research is pivotal for advancing emotion-aware human–machine- interaction and intelligent transportation systems.

Major injuries and associated factors in traffic accidents among motorcycle food delivery riders during the COVID-19 pandemic in Thailand

Motorcycle food delivery riders (MFDRs) are at a higher risk of traffic accidents compared to regular motorcyclists. Their safety is a significant issue in many developing countries, particularly in Thailand, which has the highest rate of motorcyclist fatalities globally. This study aimed to determine the prevalence of traffic accidents and explore the association between risky riding behaviors, concerns for working conditions, and accidents among MFDRs. This cross-sectional study was conducted in Chiang Mai, Thailand. Data were collected from a total of 709 MFDRs through an online platform. Collected data included working conditions, risky riding behaviors, concerns about their work conditions, and experience with traffic accidents. In the past six months, the incidence of traffic accidents among MFDRs was 18.9 %., with 2.4 % experiencing hospitalized major injuries, 11.4 % having non-hospitalized major injuries, and 5.1 % having minor injuries. A total of 84.3 % of MFDRs exhibited at least one risky riding behavior, while 5.8 % engaged in ten or more such behaviors. Multivariable logistic regression analysis with adjustments for sex, age, and body mass index indicates that red-light running (adjusted odds ratio, aOR 2.78, 95 % CI 1.07 to 7.21) and being concerned for customer behaviors (aOR 2.66, 95 % CI 1.26 to 5.62) were significantly associated with major injuries. Additionally, factors such as sleeping less than 6 h per day (aOR 1.68, 95 % CI 1.10 to 2.56), riding the wrong way (aOR 2.49, 95 % CI 1.12 to 5.52), and having concern for customer behaviors (aOR 1.91, 95 % CI 1.03 to 3.55) were associated with total number of accidents. Red-light running and concerns for customer behavior in MFDRs are associated with a higher risk of traffic accidents with major injuries. Effective strategies and interventions should focus on strengthening the enforcement of traffic risk behaviors and encouraging food delivery companies to establish regulations relating to the workers’ concerns.

Mapping the relationship between traffic accidents, road network configuration, and urban land use

Understanding the nature of traffic accidents in relation to urban access networks is crucial for building safer and more resilient cities. This paper examines the issue of traffic accidents through the lenses of urban configurational theory and urban land use. Three data layers were used in the study, including space syntax analysis conducted in Depthmap X, geotagged traffic accidents collected by the police department, and geotagged land-use data. The method involved superimposing these data layers and exploring potential correlations using a geographic information system (GIS). The findings indicate significant correlations between the spatial frequency of traffic accidents and the choice measure (at 2500 m), local integration, and active land use. The findings of this study can help inform planners and policymakers about the best location to implement safety measures to reduce the risk of traffic accidents in urban access networks.

Real-time accident risk identification for freeway weaving segments based on video analytics

Accurately identifying the risk of traffic accidents in real time for specific road sections is crucial for implementing proactive traffic control strategies. The existing studies have focused on real-time prediction of collisions between two vehicles, identification of accident risks on road segments relies heavily on historical accident data. This study proposes a framework for identifying road section accident risks at the granular level by utilizing traffic conflicts as an intermediary. In this framework, vehicles are detected and tracked in real time and the pixel coordinates are then efficiently converted into world coordinates based on the camera projection principle. A binary logit model is constructed to identify road section accident risks using carefully selected macroscopic traffic flow parameters. The proposed framework was validated on two one-sided ramp weaves in Jinan, China, achieving an accuracy of 76.2 %. The findings will serve as a foundation for traffic safety monitoring and proactive traffic control.

Traffic Accidents: The Contrasting Roles of Life Satisfaction and Anxiety

AbstractIt is well established that people with low subjective wellbeing have heightened traffic accident risk. However, the roles of particular mental states, including overall evaluative subjective wellbeing and specific forms of mental distress such as depression or anxiety, have not hitherto been disentangled. Using an official representative population survey, we show that different forms of mental distress are associated with single versus multiple accident prevalence within a year. Anxiety (a lack of calmness) is strongly associated with having an accident. By contrast, conditional on having had at least one accident, overall life satisfaction is more strongly associated with having multiple accidents than are anxiety, depression or overall mental health. The relationship is n-shaped so that people with moderately low wellbeing have the highest multiple accident probability. These results are robust to the exclusion of respondents for whom reverse causality may be present and to the inclusion of variables representing other risky behaviours and representing social isolation. The findings imply that specific forms of intervention are likely to be needed to reduce accidents for people with moderately low subjective wellbeing who may engage in habitual risky behaviours that result in multiple accidents.

Work stressors and aggressive driving: The mediating roles of stress appraisals

While the association between work stressors and the risk of traffic accidents and driving lapses has been established, there is a lack of research exploring the relationship between work stressors and aggressive driving—a notable contributor to traffic incidents and injuries. To fill this gap, the present study utilized the challenge-hindrance stressor framework and the transactional theory of stress to investigate the effects of these stressors on aggressive driving behaviors, with a particular focus on the mediating roles of challenge and hindrance appraisals. We used time-lagged design and gathered self-reported data from 247 employees at a Chinese company over three distinct time points. The results of structural equation modeling indicated that both challenge and hindrance stressors are positively associated with aggressive driving, with their effects mediated by corresponding appraisals of challenge and hindrance. Notably, challenge appraisals were inversely related to aggressive driving. Our study is one of the first attempts to explore the relationship between work stressors and aggressive driving, underscoring the critical role of stress appraisals in understanding responses to stressors.

Comparison of Energy Absorptive Capacities of Different Aluminum Alloy Foams Placed Inside the Crash Box

Increasing population worldwide and the resulting increasing number of automobiles increase the risk of traffic accidents. Due to this increasing risk, automobile manufacturers take various safety measures to protect drivers and passengers in case of possible accidents. Crash boxes are one of the passive safety system elements that are the first to absorb the impact in the event of a front or rear impact accident, absorbing the resulting deformation energy and ensuring that it is transmitted into the car at the least possible level. Therefore, increasing the energy absorption ability of crash boxes is an extremely important issue. In this study, it was aimed to increase the energy absorption capabilities by placing aluminum foam based materials produced by using the powder metallurgy method using three different aluminum alloys (Al2024, Al5083, and Al6061) inside the crash boxes, which are normally manufactured as hollow. In addition, the produced aluminum foams were compared in terms of pore sizes with SEM images. It can be said that Al6061 is the most ideal material among the alloys used in terms of pore structure and homogeneity. On the other hand, Al6061 alloys produced the greatest damped energy value within the parameters of the investigation, 221.711 J. This value was 169.556 J for Al2024 alloy and 214.101 J for Al5083 alloy. As a result, it was concluded that the amount of energy absorption can be increased by about 4-5 times by using metallic foams produced using aluminum materials compared to the empty crash box.

Perceived Traffic Risk on Road Infrastructure Conditions among Car Drivers in Lhokseumawe City, Aceh, Indonesia

According to the Indonesian Department of Transportation, 86% of traffic accidents are attributed to human factors. Additionally, road conditions are identified as a contributing factor to road accidents. Therefore, this study aimed to explore road conditions and their impact on the driving behaviour of car drivers. Data for this exploration were collected by distributing questionnaires to 200 respondents, utilizing a Likert scale for answer options. The technique used was random sampling, utilizing descriptive statistical analysis and a Confirmatory Factor Analysis model. Consequently, the result showed that each latent variable construct examined by car drivers had a value exceeding 80%. The most prominent latent variable was the "Condition of Road Surface Facilities," accounting for 88.1% and featuring eight indicators. The indicator "Disturbed when passing through a narrow road," part of the latent variable "Condition of Road Equipment Facilities," had the highest standardized value at 1.223. Finally, this study suggested that the government should prioritize attention to road infrastructure conditions to enhance driver comfort and mitigate the risk of traffic accidents.

Reviewing the prevalence of refractive errors and color blindness among commercial drivers

Road traffic crashes pose a significant global public health concern, claiming the lives of approximately 1.19 million people each year. A worrisome trend surfaces as a considerable majority of these fatalities involve vulnerable road users, encompassing not only drivers of heavy and light vehicles but also pedestrians, cyclists, and motorcyclists. Visual impairments play a significant role in these tragic events, further exacerbating the risks faced by road users. Whether due to poor eyesight or other vision-related challenges, impaired visual perception contributes to the vulnerability of individuals on the road. This review aimed to systematically analyze the currently available research and articles to determine the prevalence of color vision defects and refractive errors among commercial drivers. The primary objective was to investigate whether refractive errors and color vision defects pose a risk factor for road safety among commercial drivers. A primary search of databases including PubMed, Google Scholar, and others was conducted using relevant keywords. Articles published between 2010 and 2023 were considered, resulting in 16 cross-sectional studies being included for analysis. Prevalence rates were calculated using weighted averages based on sample sizes. Among commercial drivers, the prevalence of color vision impairment was lower (4.35%) compared to refractive errors (23.42%). Individuals with protanopia were at higher risk of road traffic accidents (RTAs). Complex issues such as alcoholism and visual impairments contributed to higher RTA frequencies in certain populations, emphasizing the multifaceted nature of road safety challenges.