Gender differences in injury severity risks in crashes at signalized intersections

Pedestrian injury in crashes at intersections often results from complex interaction among various factors. The factor identification is a critical task for understanding the causes and improving the pedestrian safety. A total of 2,614 crash records at signalized and non-signalized intersections were applied. A Partial Proportional Odds (PPO) model was developed to examine the factors influencing Pedestrian Injury Severity (PIS) because it can accommodate the ordered response nature of injury severity. An elasticity analysis was conducted to quantify the marginal effects of contributing factors on the likelihood of PIS. For signalized intersections, seven explanatory variables significantly affect the likelihood of PIS, in which five explanatory variables violate the Proportional Odds Assumption (POA). Local driver, truck, holiday, clear weather, and hit-and-run lead to higher likelihood of severer PIS. For non-signalized intersections, six explanatory variables were found significant to the PIS, in which three explanatory variables violate the POA. Young and adult drivers, senior pedestrian, bus/van, divided road, holiday, and darkness tend to increase the likelihood of severer PIS. The vehicles of large size and heavy weight (e.g. truck, bus/van) are significant factors to the PIS at both signalized and non-signalized intersections. The proposed PPO model has demonstrated its effectiveness in identifying the effects of contributing factors on the PIS.

Download Full-text

Gender differences in snowboarding accidents in Austria: a 2005–2018 registry analysis

BMJ Open ◽

10.1136/bmjopen-2021-053413 ◽

2021 ◽

Vol 11 (10) ◽

pp. e053413

Author(s):

Christopher Duane Rugg ◽

Till Malzacher ◽

Julia Ausserer ◽

Andrea Rederlechner ◽

Peter Paal ◽

...

Keyword(s):

Gender Differences ◽

Injury Severity ◽

National Registry ◽

Secondary Outcome ◽

Injury Patterns ◽

Severe Injuries ◽

And Gender ◽

Gender Specific ◽

Registry Analysis ◽

Fatal Injuries

ObjectivesTo elucidate gender differences in snowboarding accidents.DesignRetrospective registry analysis within the Austrian National Registry of Mountain Accidents.SettingSnowboard-related emergencies between November 2005 and October 2018.ParticipantsAll injured snowboarders with documented injury severity and gender (3536 men; 2155 women).Primary and secondary outcome measuresGender-specific analysis of emergency characteristics and injury patterns.ResultsOver time, the number of mild, severe and fatal injuries per season decreased in men but not in women. Accidents most frequently were interindividual collisions (>80%) and occurred when heading downhill on a slope. Men more often suffered injuries to the shoulder (15.1% vs 9.2%) and chest (6.8% vs 4.4%), were involved in accidents caused by falling (12.9% vs 9.6%) or obstacle impact (4.3% vs 1.5%), while on slopes with higher difficulty levels (red: 42.6% vs 39.9%; black: 4.2% vs 2.5%), while snowboarding in a park (4.8% vs 2.1%) and under the influence of alcohol (1.6% vs 0.5%). Women more often sustained injuries to the back (10.2% vs 13.1%) and pelvis (2.9% vs 4.2%), on easier slopes (blue: 46.1% vs 52.4%) and while standing or sitting (11.0% vs 15.8%). Mild injuries were more frequent in women (48.6% vs 56.4%), severe and fatal injuries in men (36.0% vs 29.7% and 0.9% vs 0.4%). Male gender, age and the use of a helmet were risk factors for the combined outcome of severe or fatal injuries (OR (99% CI): 1.22 (1.00 to 1.48), 1.02 (1.02 to 1.03) and 1.31 (1.05 to 1.63)). When wearing a helmet, the relative risk (RR) for severe injuries increased while that for mild injuries decreased in male snowboarders only (RR (95% CI): 1.21 (1.09 to 1.34) and 0.88 (0.83 to 0.95)).ConclusionsSnowboard injuries are proportionally increasing in women and the observed injury patterns and emergency characteristics differ substantially from those of men. Further gender-specific research in snowboard-related injuries should be encouraged.Trial registration numberNCT03755050.

Download Full-text

Development of Artificial Neural Network Models to Predict Driver Injury Severity in Traffic Accidents at Signalized Intersections

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1746-02 ◽

2001 ◽

Vol 1746 (1) ◽

pp. 6-13 ◽

Cited By ~ 132

Author(s):

Hassan T. Abdelwahab ◽

Mohamed A. Abdel-Aty

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Traffic Accidents ◽

Injury Severity ◽

Network Models ◽

Signalized Intersections ◽

Neural Network Models ◽

Artificial Neural ◽

Artificial Neural Network Models

Download Full-text

Influence of Type of Traffic Control on Injury Severity in Bicycle–Motor Vehicle Crashes at Intersections

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198118773576 ◽

2018 ◽

Vol 2672 (38) ◽

pp. 199-209 ◽

Cited By ~ 6

Author(s):

Rabbani Rash-ha Wahi ◽

Narelle Haworth ◽

Ashim Kumar Debnath ◽

Mark King

Keyword(s):

Traffic Control ◽

Injury Severity ◽

Motor Vehicle ◽

Mixed Logit ◽

Traffic Signals ◽

Signalized Intersections ◽

Mitigation Strategies ◽

Control Measures ◽

Mixed Logit Models ◽

Road Surfaces

Many studies have identified factors that contribute to bicycle–motor vehicle (BMV) crashes, but little is known about determinants of cyclist injury severity under different traffic control measures at intersections. Preliminary analyses of 5,388 police-reported BMV crashes from 2002 to 2014 from Queensland, Australia revealed that cyclist injury severity differed according to whether the intersection had a Stop/Give-way sign, traffic signals or no traffic control. Therefore, separate mixed logit models of cyclist injury severity (fatal/hospitalized, medically treated, and minor injury) were estimated. Despite similar distributions of injury severity across the three types of traffic control, more factors were identified as influencing cyclist injury severity at Stop/Give-way controlled intersections than at signalized intersections or intersections with no traffic control. Increased injury severity for riders aged 40–49 and 60+ and those not wearing helmets were the only consistent findings across all traffic control types, although the effect of not wearing helmets was smaller at uncontrolled intersections. Cyclists who were judged to be at fault were more severely injured at Stop/Give-way and signalized intersections. Speed zone influenced injury severity only at Stop/Give-way signs and appears to reflect differences in intersection design, rather than speed limits per se. While most BMV crashes occurred on dry road surfaces, wet road surfaces were associated with an increased cyclist injury severity at Stop/Give-way intersections. The results of this study will assist transport and enforcement agencies in developing appropriate mitigation strategies to improve the safety of cyclists at intersections.

Download Full-text