Heterogeneous impacts of gender-interpreted contributing factors on driver injury severities in single-vehicle rollover crashes

2016 ◽  
Vol 94 ◽  
pp. 28-34 ◽  
Author(s):  
Qiong Wu ◽  
Guohui Zhang ◽  
Cong Chen ◽  
Rafiqul Tarefder ◽  
Haizhong Wang ◽  
...  
Keyword(s):  
Contributing Factors ◽  
Single Vehicle ◽  
Vehicle Rollover ◽  
Injury Severities

Analysis of Single-Vehicle Crash Injury Severities in Urban River-Crossing Road Tunnels

2015 ◽  
Vol 743 ◽  
pp. 526-532 ◽  
Author(s):  
C.M. Jiang ◽  
J.J. Lu ◽  
L.J. Lu
Keyword(s):  
Heavy Vehicles ◽  
Probit Regression ◽  
Factors Affecting ◽  
Explanatory Variables ◽  
Urban Road ◽  
Road Tunnels ◽  
Negative Effect ◽  
Single Vehicle ◽  
Injury Severities ◽  
One Year

Based on the originally unprocessed data from the Official Platform of“110”Alarming Receiving Center (OP110ARC) of Shanghai Public Security Bureau (SPSB), 529 single-vehicle crashes reported during one year and a half which happened at the thirteen urban road tunnels going across the Huangpu River are used in this study. To investigate the factors affecting the crash influence severity levels, ordered probit regression is established. Several categories of factors are considered as explanatory variables in the models. The study finds that the entrance of the tunnels is the site where severe injury crashes trend to occur. Rainy and snowy days impose vehicles and motorists driving via the tunnel sections in danger. Tunnels with a low speed limit (40 km/h in this study) may be not as safe as we thought before. Two-wheel vehicles without sufficient physical protection for its drivers and heavy vehicles also show a negative effect on the operation safety of single-vehicle at these studied tunnels. Alcohol involved drivers are more likely to suffer from a severe crashes and gets badly hurt.

Examining Driver Injury Severity in Single-Vehicle Road Departure Crashes Involving Large Trucks

2021 ◽  
pp. 036119812110101
Author(s):  
Mehdi Hosseinpour ◽  
Kirolos Haleem
Keyword(s):  
Rural Areas ◽  
Injury Severity ◽  
Factors Affecting ◽  
Crash Data ◽  
Natural Logarithm ◽  
Concrete Barriers ◽  
Local Roads ◽  
Single Vehicle ◽  
Safety Countermeasures ◽  
Injury Severities

Road departure (RD) crashes are among the most severe crashes that can result in fatal or serious injuries, especially when involving large trucks. Most previous studies neglected to incorporate both roadside and median hazards into large-truck RD crash severity analysis. The objective of this study was to identify the significant factors affecting driver injury severity in single-vehicle RD crashes involving large trucks. A random-parameters ordered probit (RPOP) model was developed using extensive crash data collected on roadways in the state of Kentucky between 2015 and 2019. The RPOP model results showed that the effect of local roadways, the natural logarithm of annual average daily traffic (AADT), the presence of median concrete barriers, cable barrier-involved collisions, and dry surfaces were found to be random across the crash observations. The results also showed that older drivers, ejected drivers, and drivers trapped in their truck were more likely to sustain severe single-vehicle RD crashes. Other variables increasing the probability of driver injury severity have included rural areas, dry road surfaces, higher speed limits, single-unit truck types, principal arterials, overturning-consequences, truck fire occurrence, segments with median concrete barriers, and roadside fixed object strikes. On the other hand, wearing seatbelt, local roads and minor collectors, higher AADT, and hitting median cable barriers were associated with lower injury severities. Potential safety countermeasures from the study findings include installing median cable barriers and flattening steep roadside embankments along those roadway stretches with high history of RD large-truck-related crashes.

Identification of characteristics and frequent scenarios of single-vehicle rollover crashes during pre-ballistic phase; part 1 – A descriptive study

2017 ◽  
Vol 107 ◽  
pp. 31-39 ◽  
Author(s):  
Taewung Kim ◽  
Dipan Bose ◽  
Jon Foster ◽  
Varun Bollapragada ◽  
Jeff R. Crandall ◽  
...  
Keyword(s):  
Descriptive Study ◽  
Single Vehicle ◽  
Vehicle Rollover

Identifying Patterns of Key Factors in Sun Glare-Related Traffic Crashes

2021 ◽  
pp. 036119812110378
Author(s):  
Subasish Das ◽  
Xiaoduan Sun ◽  
Bahar Dadashova ◽  
M. Ashifur Rahman ◽  
Ming Sun
Keyword(s):  
Environmental Issues ◽  
The United States ◽  
Traffic Information ◽  
Key Factors ◽  
Contributing Factors ◽  
Additional Insight ◽  
Traffic Crashes ◽  
Traffic Crash ◽  
Single Vehicle ◽  
Open Country

Sun glare is one of the major environmental issues contributing to traffic crashes. Every year, many traffic crashes in the United States are attributed to sun glare. However, quantitative analysis of the influence of sun glare on traffic crashes has not been widely undertaken. This study used traffic crash narrative data for 7 years (2010–2016) from Louisiana to identify crash reports that provided evidence of drivers indicating sun glare as the primary contributing factor of the crashes. Additional geometry and traffic information was collected to identify the list of key crash-contributing factors. This study used cluster correspondence analysis to perform the data analysis. After performing several iterations, six clusters were identified that provided additional insight in relation to sun glare-related crashes. The six clusters are associated with mixed (business and residential) localities, intersection-related crashes on U.S. roadways, single-vehicle crashes on residential two-lane undivided roadways, curve-related crashes on parish roadways in residential localities, interstate-related crashes in open country localities, and curve-related crashes in open country localities. The findings of the current study can add insights to the ongoing safety analysis on sun glare-related crashes.

scholarly journals Effects of Human-Centered Factors on Crash Injury Severities

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Emmanuel Kofi Adanu ◽  
Steven Jones
Keyword(s):  
Logit Model ◽  
Latent Class ◽  
Unobserved Heterogeneity ◽  
Model Fit ◽  
Contributing Factors ◽  
Traffic Crashes ◽  
Fit Statistics ◽  
Mnl Model ◽  
Serious Injury ◽  
Injury Severities

Factors related to drivers and their driving habits dominate the causation of traffic crashes. An in-depth understanding of the human factors that influence risky driving could be of particular importance to facilitate the application of effective countermeasures. This paper sought to investigate effects of human-centered crash contributing factors on crash outcomes. To select the methodology that best accounts for unobserved heterogeneity between crash outcomes, latent class (LC) logit model and random parameters logit (RPL) model were developed. Model estimation results generally show that serious injury crashes were more likely to involve unemployed drivers, no seatbelt use, old drivers, fatigued driving, and drivers with no valid license. Comparison of model fit statistics shows that the LC logit model outperformed the RPL model, as an alternative to the traditional multinomial logit (MNL) model.

Sign in / Sign up

Export Citation Format

Share Document