scholarly journals What factors impact pedestrian and cyclist fatalities? A state level analysis

2021 ◽  
Vol 8 (S1) ◽  
Author(s):  
Zoabe Hafeez ◽  
Malvi Mehta
Keyword(s):  
Linear Regression ◽  
Multiple Regression ◽  
Motor Vehicle ◽  
State Level ◽  
Outcome Variable ◽  
Death Rates ◽  
Light Truck ◽  
Road Users ◽  
Policy Information ◽  
Vulnerable Road Users

Abstract Background Pedestrian and bicyclist injuries and fatalities have increased since 2010 after a long downward trend. Trucks and SUVs, collectively called light trucks, have also increased in sales and size, which may affect pedestrians and bicyclists. Additionally, pedestrian and cyclist commuters vary by state and it has been speculated that an increase in such commuters may affect fatalities. Studying vulnerable road users can bestow clues on best practices for infrastructure and public health. Methods State level pedestrian and cyclist fatality data was obtained from the National Highway Transportation Safety Administration for 2018. Light truck registration by state was obtained from the Office of Highway Policy Information for 2018. Commuters who walk or bike to work were obtained from the American Community Survey from 2009 to 2011, from the latest Centers for Disease Control report. We performed multiple linear regression, accounting for total motor vehicle lane miles per 100 people, also obtained from the Office of Highway Policy Information for 2018. Multiple regression analysis was performed to assess predictors for pedestrian and cyclist fatalities with the predictors variables of light truck registration, lane miles per 100 people, and proportion of commuters who are vulnerable road users. Secondary analysis included simple linear regression of the predictor variables against each other. Results The multiple regression model, including proportion of light truck registration, lane miles per 100 people, and proportion of commuters who are vulnerable road users, accounted for 18% of the variability in the outcome variable (p = 0.03). An increased number of vulnerable road users were negatively associated with pedestrian and bicyclist fatality. Additionally, there appeared to be an association between motor vehicle lane miles per 100 people and proportion of light truck registrations that was also significant (p < 0.01). Conclusion The variables affecting vulnerable road user deaths are important to understand given their increased risk exposure on the road. This state level study identifies a potential protective variable with increased vulnerable road users being associated with a decrease in pedestrian and bicyclist death rates. Additionally, light truck proportions do not appear to have a significant effect on death rates.

scholarly journals What Factors Impact Pedestrian and Cyclist Fatalities? A State Level Analysis

2021 ◽  
Author(s):  
Zoabe Hafeez ◽  
Malvi Mehta
Keyword(s):  
Linear Regression ◽  
Multiple Regression ◽  
Motor Vehicle ◽  
State Level ◽  
Outcome Variable ◽  
Death Rates ◽  
Light Truck ◽  
Road Users ◽  
Policy Information ◽  
Vulnerable Road Users

Abstract Background Pedestrian and bicyclist injuries and fatalities have increased since 2010 after a long downward trend. Trucks and SUVs, collectively called light trucks, have also increased in sales and size, which may affect pedestrians and bicyclists. Additionally, pedestrian and cyclist commuters vary by state and it has been speculated that an increase in such commuters may affect fatalities. Studying vulnerable road users can bestow clues on best practices for infrastructure and public health. Methods State level pedestrian and cyclist fatality data was obtained from the National Highway Transportation Safety Administration for 2018. Light truck registration by state was obtained from the Office of Highway Policy Information for 2018. Commuters who walk or bike to work were obtained from the American Community Survey from 2009 to 2011, from the latest Centers for Disease Control report. We performed multiple linear regression, accounting for total motor vehicle lane miles per 100 people, also obtained from the Office of Highway Policy Information for 2018. Multiple regression analysis was performed to assess predictors for pedestrian and cyclist fatalities with the predictors variables of light truck registration, lane miles per 100 people, and proportion of commuters who are vulnerable road users. Secondary analysis included simple linear regression of the predictor variables against each other. Results The multiple regression model, including proportion of light truck registration, lane miles per 100 people, and proportion of commuters who are vulnerable road users, accounted for 18% of the variability in the outcome variable (p = 0.03). An increased number of vulnerable road users were negatively associated with pedestrian and bicyclist fatality. Additionally, there appeared to be an association between motor vehicle lane miles per 100 people and proportion of light truck registrations that was also significant (p < 0.01). Conclusion The variables affecting vulnerable road user deaths are important to understand given their increased risk exposure on the road. This state level study identifies a potential protective variable with increased vulnerable road users being associated with a decrease in pedestrian and bicyclist death rates. Additionally, light truck proportions do not appear to have a significant effect on death rates.

Comparison of epidemiology and injury profile between vulnerable road users and motor vehicle occupants in road traffic fatalities

2019 ◽  
Vol 20 (6) ◽  
pp. 581-587 ◽  
Author(s):  
Sang-Chul Kim ◽  
Hae-Ju Lee ◽  
Ji-Min Kim ◽  
So-Yeon Kong ◽  
Jung-Soo Park ◽  
...  
Keyword(s):  
Motor Vehicle ◽  
Road Traffic ◽  
Traffic Fatalities ◽  
Road Users ◽  
Vulnerable Road Users

scholarly journals Does a “Cushion Effect” Really Exist? A Morphomic Analysis of Vulnerable Road Users with Serious Blunt Abdominal Injury

Healthcare ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1006
Author(s):  
Yu-San Tee ◽  
Chi-Tung Cheng ◽  
Chi-Hsun Hsieh ◽  
Shih-Ching Kang ◽  
Chih-Yuan Fu ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Visceral Fat ◽  
Motor Vehicle ◽  
Subcutaneous Fat ◽  
Abdominal Injury ◽  
Visceral Fat Area ◽  
Cross Sectional ◽  
Road Users ◽  
Severity Of Injury ◽  
Vulnerable Road Users

Introduction: The severity of injury from motor vehicle crashes (MVCs) depends on complex biomechanical factors, and the bodily features of the injured person account for some of these factors. By assuming that vulnerable road users (VRUs) have limited protection resulting from vehicles and safety equipment, the current study analyzed the characteristics of fat distribution measured by computed tomography (CT) imaging and investigated the existence of a “cushion effect” in VRUs. Materials and Methods: This retrospective study enrolled 592 VRUs involved in MVCs who underwent CT scans. Visceral fat area and subcutaneous fat cross-sectional area were measured and adjusted according to total body area (TBA) and are presented as the visceral fat ratio and the subQ fat ratio (subcutaneous fat ratio). Risk factors for serious abdominal injury (maximum abbreviated injury scale (MAISabd ≥ 3)) resulting from MVCs were determined by univariate and multivariate analysis. Results: MAISabd ≥ 3 was observed in 104 (17.6%) of the patients. The subQ fat ratio at the L4 vertebral level was significantly lower in the MAISabd ≥ 3 group than in the MAISabd < 3 group (24.9 ± 12.0 vs. 28.1 ± 11.9%; p = 0.015). A decreased L4 subQ fat ratio was associated with a higher risk for MAISabd ≥ 3 in multivariate analysis (odds ratio 0.063; 95% CI 0.008–0.509; p = 0.009). Conclusion: The current study supported the “cushion effect” theory, and protection was apparently provided by subcutaneous fat tissue. This concept may further improve vehicle and safety designation in the future.

Does a “Cushion Effect” Really Exist? A Morphomic Analysis of Vulnerable Road Users with Serious Blunt Abdominal Injury

2021 ◽  
Author(s):  
Yu-San Tee ◽  
Chi-Tung Cheng ◽  
Chi-Hsun Hsieh ◽  
Shih-Ching Kang ◽  
CHIH-YUAN FU ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Visceral Fat ◽  
Motor Vehicle ◽  
Subcutaneous Fat ◽  
The Body ◽  
Abdominal Injury ◽  
Visceral Fat Area ◽  
Road Users ◽  
Severity Of Injury ◽  
Vulnerable Road Users

Abstract Background: The severity of injury from motor vehicle crashes (MVCs) depends on complex biomechanical factors, and the body features of the injured person account for some of these factors. By assuming that vulnerable road users (VRU) have limited protection resulting from vehicle and safety equipment, the current study analyzed the characteristics of fat distribution measured by computed tomography (CT) imaging and investigated the existence of a “Cushion effect” in VRU.Materials and Methods: This retrospective study enrolled 592 VRU involved in MVCs who underwent CT scans. Visceral fat area and subcutaneous fat cross-sectional area were measured and adjusted according to total body area (TBA) and are presented as the visceral fat ratio and the subQ fat ratio (subcutaneous fat ratio). Risk factors for serious abdominal injury [maximum abbreviated injury scale (MAISabd ≥ 3)] resulting from MVCs were determined by univariate and multivariate analysis.Results: MAISabd ≥ 3 was observed in 104 (17.6%) of the patients. The SubQ fat ratio at the L4 vertebral level was significantly lower in the MAISabd ≥ 3 group than in the MAISabd < 3 group (24.9 ± 12.0 vs 28.1 ± 11.9%; p=0.015). A decreased L4 subQ fat ratio was associated with a higher risk for MAISabd ≥ 3 in multivariate analysis (odds ratio 0.063; 95% CI 0.008-0.509; p = 0.009).Conclusion: The current study supported the “Cushion effect” theory, and protection was apparently provided by subcutaneous fat tissue. This concept may further improve vehicle and safety designation in the future.

scholarly journals Employing beam-forming for estimating the direction of arrival in a multi-path propagation environment

2005 ◽  
Vol 3 ◽  
pp. 151-155 ◽  
Author(s):  
M. Bresch ◽  
J. Shi ◽  
R. Kokozinski
Keyword(s):  
Traffic Accidents ◽  
Motor Vehicle ◽  
Direction Of Arrival ◽  
Line Of Sight ◽  
Beam Forming ◽  
Great Opportunity ◽  
Ism Band ◽  
Road Users ◽  
Vulnerable Road Users ◽  
Mobile Receiver

Abstract. Due to recent researches on traffic accidents with vulnerable road users (VRUs), several measures revealed a great opportunity of reduction. However, all measures applied so far failed to reduce the number of traffic accidents if there is no line-of-sight. Therefore, a transponder signal is utilized to make the VRU visible. The motor vehicle carries a mobile receiver for VRU detection and location. The receiver employs digital beam-forming for estimating the direction of arrival (DOA) with an antenna array for RF ISM band. A sequence of DOA estimations is used for location and motion estimation purposes.

scholarly journals Laminated Windshield Breakage Modelling in the Context of Headform Impact Homologation Tests

2015 ◽  
Vol 20 (1) ◽  
pp. 87-96
Author(s):  
P. Kosiński ◽  
J. Osiński
Keyword(s):  
Motor Vehicle ◽  
Polyvinyl Butyral ◽  
Dynamic Responses ◽  
Material Models ◽  
Fem Model ◽  
Certification Testing ◽  
Road Users ◽  
Vehicle Collision ◽  
Vulnerable Road Users ◽  
The Impact

Abstract The purpose of modelling a laminated windshield using the FEM is to provide a critical look on the way the adult headform impact tests are conducted in the process of motor vehicle certification. The main aim of the study is to modify the design of a laminated windshield in the context of a vehicle collision with vulnerable road users. The initial phase of the work was to develop a model of the adult headform impactor. The validation consisted in conducting a series of FEM analyses of the impactor certification testing according to the Regulation (EC) 631/2009. Next, the impact of the headform model on a windshield was analysed. The FEM model of laminated glass is composed of two outer layers of glass and an inner layer of polyvinyl butyral. FEM analyses of the impaction were performed at five points of the windshield characterised by various dynamic responses of the impactor and various patterns of glass cracking. In modelling the layers of glass, the Abaqus environment “brittle cracking” model was used. The following material models of PVB resin were considered: elastic, elastic-plastic, hyperelastic, and low-density foam. Furthermore, the influence of the mesh type on the process of glass cracking in a laminated windshield was analysed.

scholarly journals Comparison of physical and psychological health outcomes for motorcyclists and other road users after land transport crashes: an inception cohort study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lisa N. Sharwood ◽  
Annette Kifley ◽  
Ashley Craig ◽  
Bamini Gopinath ◽  
Jagnoor Jagnoor ◽  
...  
Keyword(s):  
Cohort Study ◽  
Health Outcomes ◽  
Injury Severity ◽  
Motor Vehicle ◽  
Road User ◽  
Inception Cohort ◽  
Post Injury ◽  
Road Users ◽  
Vulnerable Road Users ◽  
User Groups

Abstract Background Serious injuries and fatalities among vulnerable road users on two wheeled motorised vehicles have increased across Australia and internationally in the past decade yet fallen for motor vehicle occupants. Almost half of all reported motorcycle injury crashes cause serious injury or death, nearly double that of motor vehicle police-reported crashes. This study explores associations with sociodemographic and pre-injury health characteristics and health outcomes after a road traffic injury; aiming to compare motorcyclists with other road users and inform recovery care. Methods An inception cohort study recruited 1854 individuals aged > 17 years, injured following land-transport crashes in New South Wales, Australia (July 2013–November 2016). Interviews conducted at baseline, 6-and 12-months post-injury elicited demographic, socioeconomic, and self-reported health conditions. Results Primary analysis involved 1854 participants who were recruited at baseline as three distinct road user groups; 628 (33.9%) motorcyclists, 927 (50%) vehicle occupants and 299 (16.1%) bicyclists. At baseline, injury patterns differed significantly between road user groups; motorcyclists were more than twice as likely to sustain lower extremity injury (p < 0.001); to have more severe injury severity scores (p < 0.001) and longer hospital stays versus vs vehicle occupants and bicyclists (< 0.001) across these measures. Injured motorcyclists were predominantly male (88.1%, p < 0.001), were younger on average (38 years) than bicyclists (41.5 years), had lower income and education levels, and poorer pre-injury physical health than other road user groups. Despite these differences, at 12 months post-injury motorcyclists had better physical health (SF12-PCS 2.07 (0.77, 3.36), p = 0.002) and reported lower pain scores (− 0.51 (− 0.83, − 0.2), p < 0.001) than vehicle occupants. Motorcyclists displayed less evidence of psychological distress than vehicle occupants, but more than bicyclists across several measures used. Conclusions Road user types differ in important characteristics, including pre-injury health status and recovery after injury. As vulnerable road users experiencing transport crash and considering their higher initial injury severity, the degree of recovery among motorcyclists compared with other user types is remarkable and unexplained. Health and recovery outcomes after land-transport crashes is least favourable among vehicle occupants despite their higher levels of protection in a crash. This information is valuable for targeting early intervention strategies by road user type during the post-crash care phase, to improve long-term recovery.

scholarly journals Machine learning prediction of blood alcohol concentration: a digital signature of smart-breathalyzer behavior

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Kirstin Aschbacher ◽  
Christian S. Hendershot ◽  
Geoffrey Tison ◽  
Judith A. Hahn ◽  
Robert Avram ◽  
...  
Keyword(s):  
Machine Learning ◽  
Motor Vehicle ◽  
Digital Health ◽  
Blood Alcohol Concentration ◽  
Area Under The Curve ◽  
State Level ◽  
Alcohol Concentration ◽  
Future Research ◽  
Higher Alcohol ◽  
Death Rates

AbstractExcess alcohol use is an important determinant of death and disability. Machine learning (ML)-driven interventions leveraging smart-breathalyzer data may help reduce these harms. We developed a digital phenotype of long-term smart-breathalyzer behavior to predict individuals’ breath alcohol concentration (BrAC) levels trained on data from a smart breathalyzer. We analyzed roughly one million datapoints from 33,452 users of a commercial smart-breathalyzer device, collected between 2013 and 2017. For validation, we analyzed the associations between state-level observed smart-breathalyzer BrAC levels and impaired-driving motor vehicle death rates. Behavioral, geolocation-based, and time-series-derived features were fed to an ML algorithm using training (70% of the cohort), development (10% of the cohort), and test (20% of the cohort) sets to predict the likelihood of a BrAC exceeding the legal driving limit (0.08 g/dL). States with higher average BrAC levels had significantly higher alcohol-related driving death rates, adjusted for the number of users per state B (SE) = 91.38 (15.16), p < 0.01. In the independent test set, the ML algorithm predicted the likelihood of a given user-initiated BrAC sample exceeding BrAC ≥ 0.08 g/dL, with an area under the curve (AUC) of 85%. Highly predictive features included users’ prior BrAC trends, subjective estimation of their BrAC (or AUC = 82% without the self-estimate), engagement and self-monitoring, time since the last measure, and hour of the day. In conclusion, an ML algorithm successfully quantified a digital phenotype of behavior, predicting naturalistic BrAC levels exceeding 0.08 g/dL (a threshold associated with alcohol-related harm) with good discrimination capability. This result establishes a foundation for future research on precision behavioral medicine digital health interventions using smart breathalyzers and passive monitoring approaches.

Victims of Road Rage: A Qualitative Study of the Experiences of Motorists and Vulnerable Road Users

2013 ◽  
Vol 28 (6) ◽  
pp. 1068-1084 ◽  
Author(s):  
Christopher Cavacuiti ◽  
Kari Juhani Ala-Leppilampi ◽  
Robert E. Mann ◽  
Richard Govoni ◽  
Gina Stoduto ◽  
...  
Keyword(s):  
Motor Vehicle ◽  
Theory Approach ◽  
Psychological Consequences ◽  
Physical And Mental Health ◽  
Road Rage ◽  
The Road ◽  
Road Users ◽  
Vulnerable Road Users ◽  
Victim Vulnerability

Objective: To gain an in-depth understanding of road rage incidents from the victims’ perspectives. Methods: The data consisted of 30- to 60-min in-depth semistructured phone interviews with 29 self-identified victims of road rage. Twenty of the participants were in a motor vehicle, whereas 9 were pedestrians/cyclists. A qualitative Grounded Theory approach was used to inductively code and analyze the transcripts. Results: Victims reported a correlation between their vulnerability and the perceived intensity/severity of the road rage incidents. The most vulnerable victims (pedestrians and cyclists) were the least likely to view road rage incidents as a random event and the most likely to feel that they were specifically targeted. Road rage incidents tended to evolve more rapidly when there was a greater real or perceived power imbalance between the victims and perpetrators. The most vulnerable victims were the most likely to have long-term physical and mental health consequences from the incident, and to significantly modify their behavior after the incident. Conclusions: Our analysis suggests that issues of victim vulnerability play a major role in determining the intensity, severity, and psychological consequences of road rage incidents. This seems particularly true for the most vulnerable of road users, such as pedestrians and cyclists.

scholarly journals A Rare Event Modelling Approach to Assess Injury Severity Risk of Vulnerable Road Users

Safety ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 29 ◽  
Author(s):  
Mariana Vilaça ◽  
Eloísa Macedo ◽  
Margarida C. Coelho
Keyword(s):  
Risk Factors ◽  
Urban Areas ◽  
Injury Severity ◽  
Motor Vehicle ◽  
Rare Event ◽  
Motor Vehicle Crash ◽  
Crash Data ◽  
Road Users ◽  
Vulnerable Road Users ◽  
The One

Vulnerable road users (VRUs) represent a large portion of fatalities and injuries occurring on European Union roads. It is therefore important to address the safety of VRUs, particularly in urban areas, by identifying which factors may affect the injury severity level that can be used to develop countermeasures. This paper aims to identify the risk factors that affect the severity of a VRU injured when involved in a motor vehicle crash. For that purpose, a comparative evaluation of two machine learning classifiers—decision tree and logistic regression—considering three different resampling techniques (under-, over- and synthetic oversampling) is presented, comparing both imbalanced and balanced datasets. Crash data records were analyzed involving VRUs from three different cities in Portugal and six years (2012–2017). The main conclusion that can be drawn from this study is that oversampling techniques improve the ability of the classifiers to identify risk factors. On the one hand, this analysis revealed that road markings, road conditions and luminosity affect the injury severity of a pedestrian. On the other hand, age group and temporal variables (month, weekday and time period) showed to be relevant to predict the severity of a cyclist injury when involved in a crash.

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