Factors affecting injury severity among recreational skiers and snowboarders: an epidemiology study

2010 ◽  
Vol 18 (12) ◽  
pp. 1804-1809 ◽  
Author(s):  
Paolo Girardi ◽  
Marco Braggion ◽  
Giuseppe Sacco ◽  
Franco De Giorgi ◽  
Stefano Corra
Keyword(s):  
Injury Severity ◽  
Epidemiology Study ◽  
Factors Affecting

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.

scholarly journals Prediction of Fatal and Major Injury of Drivers, Cyclists, and Pedestrians in Collisions

2020 ◽  
Vol 32 (1) ◽  
pp. 39-53
Author(s):  
Dalia Shanshal ◽  
Ceni Babaoglu ◽  
Ayşe Başar
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Injury Severity ◽  
Predictive Analytics ◽  
Machine Learning Techniques ◽  
Lasso Regression ◽  
Severe Injuries ◽  
Factors Affecting ◽  
Spatio Temporal ◽  
Using Data

Traffic-related deaths and severe injuries may affect every person on the roads, whether driving, cycling or walking. Toronto, the largest city in Canada and the fourth largest in North America, aims to eliminate traffic-related fatalities and serious injuries on city streets. The aim of this study is to build a prediction model using data analytics and machine learning techniques that learn from past patterns, providing additional data-driven decision support for strategic planning. A detailed exploratory analysis is presented, investigating the relationship between the variables and factors affecting collisions in Toronto. A learning-based model is proposed to predict the fatalities and severe injuries in traffic collisions through a comparison of two predictive models: Lasso Regression and Random Forest. Exploratory data analysis results reveal both spatio-temporal and behavioural patterns such as the prevalence of collisions in intersections, in the spring and summer and aggressive driving and inattentive behaviours in drivers. The prediction results show that the best predictor of injury severity for drivers, cyclists and pedestrians is Random Forest with an accuracy of 0.80, 0.89, and 0.80, respectively. The proposed methods demonstrate the effectiveness of machine learning application to traffic and collision data, both for exploratory and predictive analytics.

scholarly journals Analysis of the Risk Factors Affecting the Severity of Traffic Accidents on Spanish Crosstown Roads: The Driver’s Perspective

2020 ◽  
Vol 12 (6) ◽  
pp. 2237 ◽  
Author(s):  
Natalia Casado-Sanz ◽  
Begoña Guirao ◽  
Maria Attard
Keyword(s):  
Traffic Accidents ◽  
Injury Severity ◽  
Road Traffic ◽  
Fatal Outcome ◽  
Policy Support ◽  
Health Concern ◽  
Contributing Factors ◽  
Factors Affecting ◽  
Traffic Volumes ◽  
New Vision

Globally, road traffic accidents are an important public health concern which needs to be tackled. A multidisciplinary approach is required to understand what causes them and to provide the evidence for policy support. In Spain, one of the roads with the highest fatality rate is the crosstown road, a particular type of rural road in which urban and interurban traffic meet, producing conflicts and interference with the population. This paper contributes to the previous existing research on the Spanish crosstown roads, providing a new vision that had not been analyzed so far: the driver’s perspective. The main purpose of the investigation is to identify the contributing factors that increment the likelihood of a fatal outcome based on single-vehicle crashes, which occurred on Spanish crosstown roads in the period 2006-2016. In order to achieve this aim, 1064 accidents have been analyzed, applying a latent cluster analysis as an initial tool for the fragmentation of crashes. Next, a multinomial logit (MNL) model was applied to find the most important factors involved in driver injury severity. The statistical analysis reveals that factors such as lateral crosstown roads, low traffic volumes, higher percentages of heavy vehicles, wider lanes, the non-existence of road markings, and finally, infractions, increase the severity of the drivers’ injuries.

Exploring Vehicle–Pedestrian Crash Severity Factors on the Basis of In-Car Black Box Recording Data

2017 ◽  
Vol 2659 (1) ◽  
pp. 148-154 ◽  
Author(s):  
Tai-Jin Song ◽  
Jaehyun (Jason) So ◽  
Jisun Lee ◽  
Billy M. Williams
Keyword(s):  
Injury Severity ◽  
Video Recording ◽  
Black Box ◽  
Video Data ◽  
Crash Severity ◽  
Vehicle Speed ◽  
Factors Affecting ◽  
Explanatory Variables ◽  
Severity Of Injury ◽  
Pedestrian Crashes

This study investigated the main factors affecting the severity of injury to pedestrians in taxi–pedestrian crashes on urban arterial roads. Video data recorded by an in-car black box were used. Because the video data provided direct crash observation, they were more reliable than the crash data, and video images and speed profiles retrieved from the black box were advantageous for safety studies. For analysis of the black box data, this study defined new explanatory variables that affected injury severity; these variables could not have been identified by the conventional method, which was based on crash reports. A multiple-indicator and multiple-cause model was used to investigate the relationship between the explanatory variables and injury severity. A total of 484 taxi–pedestrian crash scenes over 2 years was used for the multivariate analysis in the city of Incheon, South Korea. The crash characteristics most strongly associated with increased crash severity were failure by the pedestrian to watch for approaching vehicles, jaywalking by the pedestrian, the pedestrian being elderly, excessive vehicle speed, failure by the driver to immediately stop, limited driver vision, and nighttime. This study emphasized the potential of individualized black box video recording data for crash severity analysis and investigation of the causal factors of crashes.

scholarly journals Risk Factors Affecting Driver Severity of Single- Vehicle Run Off Road Crash for Thailand Highway

2020 ◽  
Vol 24 (5) ◽  
pp. 207-216
Author(s):  
Chamroeun Se ◽  
Thanapong Champahom ◽  
Sajjakaj Jomnonkwao ◽  
Vatanavongs Ratanavaraha
Keyword(s):  
Risk Factors ◽  
Injury Severity ◽  
Information Management System ◽  
Logit Analysis ◽  
Factors Affecting ◽  
Roadside Safety ◽  
Fatal Crash ◽  
Run Off ◽  
Single Vehicle ◽  
Safety Features

Single-Vehicle Run Off Road (ROR) crash has been the leading crash type in terms of frequency and severity in Thailand. In this study, multinomial logit analysis was applied to identify the risk factors potentially influencing driver injury severity of single-vehicle ROR crash using accident records between 2011 and 2017 which were extracted from Highway Accident Information Management System (HAIMS) database. The analysis results show that the age of driver older than 55 years old, male driver, driver under influence of alcohol, drowsiness, ROR to left/right on straight roadway increase the probability of fatal crash, while other factors are found to mitigate severity such as the age of driver between 26-35 years old, using seatbelt, ROR and hit fixed object on straight and curve segment of roadway, mounted traffic island, intersection-related and accident in April. This study recommends the need to improve road safety campaign, law enforcement, and roadside safety features that potentially reduce level of severity of driver involving in single-vehicle ROR crash.

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