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Accident; Analysis and Prevention Nov 2023Cycling provides numerous benefits to individuals and to society but the burden of road traffic injuries and fatalities is disproportionately sustained by cyclists....
Cycling provides numerous benefits to individuals and to society but the burden of road traffic injuries and fatalities is disproportionately sustained by cyclists. Without awareness of the contributory factors of cyclist death and injury, the capability to implement context-specific and appropriate measures is severely limited. In this paper, we investigated the effects of the characteristics related to the road, the environment, the vehicle involved, the driver, and the cyclist on severity of crashes involving cyclists analysing 72,363 crashes that occurred in Great Britain in the period 2016-2018. Both a machine learning method, as the Random Forest (RF), and an econometric model, as the Random Parameters Logit Model (RPLM), were implemented. Three different RF algorithms were performed, namely the traditional RF, the Weighted Subspace RF, and the Random Survival Forest. The latter demonstrated superior predictive performances both in terms of F-measure and G-mean. The main result of the Random Survival Forest is the variable importance that provides a ranked list of the predictors associated with the fatal and severe cyclist crashes. For fatal classification, 19 variables showed a normalized importance higher than 5% with the second involved vehicle manoeuvring and the gender of the driver of the second vehicle having the greatest predictive ability. For serious injury classification, 13 variables showed a normalized importance higher than 5% with the bike leaving the carriageway having the greatest normalized importance. Furthermore, each path from the root node to the leaf nodes has been retraced the way back generating 361 if-then rules with fatal crash as consequent and 349 if-then rules with serious injury crash as consequent. The RPLM showed significant unobserved heterogeneity in the data finding four normal distributed indicator variables with random parameters: cyclist age ≥ 75 (fatal prediction), cyclist gender male (fatal and serious prediction), and driver aged 55-64 (serious prediction). The model's McFadden Pseudo R is equal to 0.21, indicating a very good fit. Furthermore, to understand the magnitude of the effects and the contribution of each variable to injury severity probabilities the pseudo-elasticity was assessed, gaining valuable insights into the relative importance and influence of the variables. The RF and the RPLM resulted complementary in identifying several roadways, environmental, vehicle, driver, and cyclist-related factors associated with higher crash severity. Based on the identified contributory factors, safety countermeasures useful to develop strategies for making bike a safer and more friendly form of transport were recommended.
Topics: Humans; Male; Accidents, Traffic; Bicycling; Logistic Models; Machine Learning; Random Forest; Female; Middle Aged; Aged
PubMed: 37683568
DOI: 10.1016/j.aap.2023.107275 -
Environmental Pollution (Barking, Essex... Nov 2023Road traffic accidents are a pervasive feature of everyday life, killing 36,500 people, injuring 4.5 million and, overall, generating costs to the American society of...
Road traffic accidents are a pervasive feature of everyday life, killing 36,500 people, injuring 4.5 million and, overall, generating costs to the American society of $340 billion in 2019. Understanding the underlying factors can improve the design of prevention strategies. We use all road traffic collisions in New York City between 2013 and 2021 (N = 1,269,600) and match each individual collision to the nearest weather and air pollution station. Our study uses highly disaggregated data using an hourly frequency of collisions at a fine spatial level incorporating various air pollutants and weather factors. We employ an instrumental variable approach using temperature inversions to provide exogenous variation in air pollution addressing endogeneity and measurement error concerns. We find that higher concentrations of carbon monoxide (CO) and sulfur dioxide (SO) increase the number of collisions but leave the severity (persons injured or killed) unaffected. Part of this can be explained by the effect of air pollutants on aggressive behavior: CO (p < .05) and SO (p < .01) increase the number of collisions caused by aggressive driving. Interestingly, this channel is only present in male drivers. Our results provide additional evidence that air pollution not only adversely affects health, but also has "non-health" related effects which are costly for the society.
Topics: Male; Humans; Accidents, Traffic; Particulate Matter; New York City; Air Pollution; Air Pollutants; Motor Vehicles
PubMed: 37734635
DOI: 10.1016/j.envpol.2023.122595 -
Nature Communications Jun 2024Despite the recent advancements that Autonomous Vehicles have shown in their potential to improve safety and operation, considering differences between Autonomous...
Despite the recent advancements that Autonomous Vehicles have shown in their potential to improve safety and operation, considering differences between Autonomous Vehicles and Human-Driven Vehicles in accidents remain unidentified due to the scarcity of real-world Autonomous Vehicles accident data. We investigated the difference in accident occurrence between Autonomous Vehicles' levels and Human-Driven Vehicles by utilizing 2100 Advanced Driving Systems and Advanced Driver Assistance Systems and 35,113 Human-Driven Vehicles accident data. A matched case-control design was conducted to investigate the differential characteristics involving Autonomous' versus Human-Driven Vehicles' accidents. The analysis suggests that accidents of vehicles equipped with Advanced Driving Systems generally have a lower chance of occurring than Human-Driven Vehicles in most of the similar accident scenarios. However, accidents involving Advanced Driving Systems occur more frequently than Human-Driven Vehicle accidents under dawn/dusk or turning conditions, which is 5.25 and 1.98 times higher, respectively. Our research reveals the accident risk disparities between Autonomous Vehicles and Human-Driven Vehicles, informing future development in Autonomous technology and safety enhancements.
Topics: Accidents, Traffic; Humans; Case-Control Studies; Automobile Driving; Automation; Safety; Automobiles
PubMed: 38890354
DOI: 10.1038/s41467-024-48526-4 -
Irish Journal of Medical Science Oct 2023Many cycling collisions occur due to human error, cycling ability, distraction or infrastructure. One such infrastructural issue for cyclists sharing the road with tram...
AIM
Many cycling collisions occur due to human error, cycling ability, distraction or infrastructure. One such infrastructural issue for cyclists sharing the road with tram lines is where the wheel of the bicycle gets caught in the rail track itself or in a gap between the rail and the road margin resulting in a sudden stall of the bicycle and potentially significant injury. This study aims to describe the crash characteristics of tram-track cycling collisions and their associated injuries.
METHODS
A retrospective chart review was conducted over 2 years, looking at cyclists that presented to St James's Emergency Department (ED) following injuries sustained due to a bicycle wheel catching in the on-road tram tracks.
RESULTS
Forty-eight patients were identified over a 2-year period. Sixty per cent of cyclists sustained limb fractures with 14% requiring orthopaedic surgery. Fifty per cent of patients were not wearing a helmet at the time of the incident and 54% of the collisions occurred around Dublin city centre during rush hour.
CONCLUSION
Further prospective multi-centre studies are required to properly describe the magnitude cycling accidents around the Luas tracks and inform future public health measures in this area.
Topics: Humans; Accidents, Traffic; Bicycling; Public Health; Retrospective Studies; Fractures, Bone; Wounds and Injuries
PubMed: 36624242
DOI: 10.1007/s11845-022-03254-w -
Eastern Mediterranean Health Journal =... Nov 2023Road traffic accidents are a major public health problem globally, causing millions of injuries, deaths and disabilities, and a huge loss of financial resources,...
BACKGROUND
Road traffic accidents are a major public health problem globally, causing millions of injuries, deaths and disabilities, and a huge loss of financial resources, especially in low- and middle-income countries.
AIM
To determine the incidence of road traffic injuries and associated mortality from 1997 to 2020 in the Islamic Republic of Iran.
METHODS
This retrospective study used data from the Legal Medicine Organization of the Islamic Republic of Iran to estimate the annual rates of road traffic injuries and associated mortality from 21 March 1997 to 20 March 2020. The data were analysed using STATA version 14 and the annual rates are reported per 100 000 population.
RESULTS
During the study period, 5 760 835 road traffic injuries and 472 193 deaths were recorded in the Islamic Republic of Iran. The mortality rate increased from 22.4 per 100 000 in 1997 to 40 per 100 000 in 2005 and decreased to 18.4 per 100 000 in 2020. The injury rate increased from 111.1 per 100 000 in 1997 to 394.9 per 100 000 in 2005. It decreased in 2006 and 2007 and increased from then until 2010, finally reaching 331.8 per 100 000 in 2020. The male to female ratio for road traffic mortality was 3.9 in 1997 and 4.6 in 2020. The case fatality rate was highest (20.1%) in 1997 and decreased to 5.6% in 2020.
CONCLUSION
Continuous interventions are needed to reduce the burden of road traffic injuries and associated mortality in the Islamic Republic of Iran.
Topics: Humans; Male; Female; Iran; Accidents, Traffic; Retrospective Studies; Incidence; Islam; Wounds and Injuries
PubMed: 37947230
DOI: 10.26719/emhj.23.104 -
The Pan African Medical Journal 2024trauma-related disorders following a road accident have both a health and an economic impact.
INTRODUCTION
trauma-related disorders following a road accident have both a health and an economic impact.
METHODS
we conducted a prospective study to determine the prevalence of these disorders, and to identify risk factors in subjects victims of road accidents and hospitalized in the Department of Orthopedic Surgery and Traumatology of the University Hospital Center of Sfax-Tunisia.
RESULTS
a total of sixty-ten subjects were included in this study. The prevalence of acute stress disorder was 37.1% and was associated with female sex, low educational level, previous medical and surgical history, passivity during the accident, severity of injuries and the presence of anxious and depressive symptoms. Post-traumatic stress disorder was observed in 40% of subjects and was associated with urban residential environment, passivity during the accident and anxious and depressive symptoms. Low scores for functional coping strategies and high scores for dysfunctional coping strategies were significantly associated with both disorders. Low educational level, urban residential environment, high levels of anxiety and depression, and denial coping strategy appear to be independent risk factors for acute stress and post-traumatic stress disorder.
CONCLUSION
It is therefore important to determine the profile of people at greater risk of post-traumatic stress disorder, to enable early diagnosis in victims of road accidents.
Topics: Humans; Female; Stress Disorders, Post-Traumatic; Male; Accidents, Traffic; Risk Factors; Adult; Prevalence; Prospective Studies; Middle Aged; Tunisia; Depression; Anxiety; Young Adult; Educational Status; Adaptation, Psychological; Stress Disorders, Traumatic, Acute; Sex Factors; Adolescent; Aged; Wounds and Injuries; Hospitals, University
PubMed: 38737217
DOI: 10.11604/pamj.2024.47.89.38015 -
Accident; Analysis and Prevention Aug 2024Bypass lanes are a low-cost measure to increase capacity at unsignalized T-junctions without left-turn lanes that allow through-traffic to pass left-turning vehicles on...
Bypass lanes are a low-cost measure to increase capacity at unsignalized T-junctions without left-turn lanes that allow through-traffic to pass left-turning vehicles on the right. There is very limited knowledge about the safety effects of bypass lanes. We found six previous studies that could be summarized by means of meta-analysis, and the results show an average accident reduction of 10 percent. However, the results from previous studies are inconsistent and may be biased. Therefore, the present study has estimated safety effects of by-pass lanes in Norway, based on a sample of 2,227 T-junctions (incl. 94 with bypass-lanes) for which relevant data was available for a period of up to 10 years. We developed accident prediction models and conducted before-after analyses. The accident prediction models show that junctions with bypass lanes have 82 percent more accidents than junctions without bypass lanes, when controlling for endogeneity. Endogeneity occurs when the implementation of a measure is conditional on the frequency of crashes, as has been the case with bypass lanes. The before-after analysis shows that average accident numbers decrease after the installation of bypass lanes. However, when controlling for regression-to-the-mean (RTM), average accident numbers increase. RTM means that accident numbers would have been likely to decrease even without any measure because they had been exceptionally high in the before period. The control for potential biases in our study is likely to contribute to the discrepancy between results from our study and previous studies, most of which have not controlled for the same potential biases. We conclude therefore that bypass lanes, although favorable for capacity, are likely to be unfavorable for safety when compared to other unsignalized T-junctions without left-turn lanes. Unfavorable safety effects may partly be due to site specific conditions, such as road alignment and sight conditions, that contribute to rear-end collision risk or inappropriate driver behavior. However, this does not necessarily mean that bypass lanes never should be used. For example, at junctions where a bypass lane may solve capacity problems, and where site-specific conditions are favorable, bypass lanes may still be an acceptable solution.
Topics: Humans; Accidents, Traffic; Norway; Safety; Automobile Driving; Environment Design
PubMed: 38781630
DOI: 10.1016/j.aap.2024.107643 -
The Journal of Craniofacial Surgery Sep 2023The incidence of pediatric craniofacial fractures and heterogeneity of fractures is known to increase with age. This study aimed to determine the occurrence of...
The incidence of pediatric craniofacial fractures and heterogeneity of fractures is known to increase with age. This study aimed to determine the occurrence of associated injuries (AIs) to craniofacial fractures and identify differences in patterns of and predictors for AIs in children and teenagers. A 6-year retrospective cross-sectional cohort study was designed and implemented. The study population included 397 patients aged 19 years or less diagnosed with craniofacial fracture at Helsinki University Hospital from 2013 to 2018. Boys (71.0%) and teenagers (64.7%) were predominated. Associated injuries were more common in teenagers than children. Teenagers had more often AI in 2 or more organ systems. Assault and intoxication by alcohol were observed only in teenagers and predominantly boys. A total of 27.0% of all patients sustained AIs. In 18.1%, brain injury was reported. In children, motor vehicle accident (MVA) was an independent predictor for AI. In teenagers, independent predictors for AI were female sex, isolated cranial fracture, combined cranial fracture, and high-energy trauma mechanism. Injury patterns and AI related to craniofacial fractures in the pediatric population are age-specific, requiring multidisciplinary collaboration in the diagnosis, treatment, and follow-up of such trauma. Predictors for AIs increase in complexity with age, and the role of sex as a predictor is evident in teenagers.
Topics: Male; Child; Humans; Female; Adolescent; Retrospective Studies; Cross-Sectional Studies; Fractures, Bone; Accidents, Traffic; Multiple Trauma; Skull Fractures
PubMed: 37202848
DOI: 10.1097/SCS.0000000000009343 -
Risky behaviors and road safety: An exploration of age and gender influences on road accident rates.PloS One 2024Human behavior is a dominant factor in road accidents, contributing to more than 70% of such incidents. However, gathering detailed data on individual drivers' behavior...
Human behavior is a dominant factor in road accidents, contributing to more than 70% of such incidents. However, gathering detailed data on individual drivers' behavior is a significant challenge in the field of road safety. As a result, researchers often narrow the scope of their studies thus limiting the generalizability of their findings. Our study aims to address this issue by identifying demographic-related variables and their indirect effects on road accident frequency. The theoretical basis is set through existing literature linking demographics to risky driving behavior and through the concept of "close to home" effect, finding that the upwards of 62% of accidents happen within 11km of a driver's home. Using regression-based machine learning models, our study, looking at England, UK, explores the theoretical linkages between demographics of an area and road accident frequency, finding that census data is able to explain over 28% of the variance in road accident rates per capita. While not replacing more in-depth research on driver behavior, this research validates trends found in the literature through the use of widely available data with the use of novel methods. The results of this study support the use of demographic data from the national census that is obtainable at a large spatial and temporal scale to estimate road accident risks; additionally, it demonstrates a methodology to further explore potential indirect relationships and proxies between behaviors and road accident risk.
Topics: Humans; Risk-Taking; Accidents; England; Head; Machine Learning
PubMed: 38252612
DOI: 10.1371/journal.pone.0296663 -
PloS One 2023In order to further study the expansion characteristics of left-turning non-motorized vehicles at intersections and the relationship between expansion characteristics...
In order to further study the expansion characteristics of left-turning non-motorized vehicles at intersections and the relationship between expansion characteristics and vehicle-bicycle conflicts, the trajectory point data of left-turning non-motorized vehicles are extracted using video trajectory tracking technology, and construct the cubic curve expansion envelope equation with the highest fitting degree. For the purpose of quantifying the expansion degree of non-motor vehicles after starting, two intersections in Guangxi Zhuang Autonomous Region were selected for case analysis, and the numerical range of expansion degree of the intersection with a left-turn waiting area and the intersection without a left-turn waiting area was obtained. Study the mathematical relationship between the expansion degree and its influencing factors, and establish the multivariate nonlinear regression equation between the expansion degree and the left-turn non-motorized vehicle flow, the number of parallel non-motorized vehicles, and the left-turn green light time. Analyze the vehicle-bicycle conflicts caused by the expansion of left-turning non-motorized vehicles, determine the essential factors affecting the number of non-motorized vehicles, and establish the multiple linear regression equation between the number of non-motorized vehicles and the number of left-turning non-motorized vehicles, the expansion degree, and the number of parallel non-motorized vehicles, the results show that the model has high accuracy. By analyzing the expansion characteristics of left-turning non-motorized vehicles at intersections, the relationship between different influencing factors and the expansion degree is obtained. Then the vehicle-bicycle conflicts under the influence of expansion characteristics is analyzed, providing theoretical ideas for improving traffic efficiency and optimizing traffic organization at intersections.
Topics: Bicycling; China; Accidents, Traffic
PubMed: 37708118
DOI: 10.1371/journal.pone.0291504