Factors affecting injury severity and the number of vehicles involved in a freeway traffic accident: investigating their heterogeneous effects by facility type using a latent class approach

2021 ◽  
pp. 1-10
Author(s):  
Hyeonmyeong Jeon ◽  
Jinhee Kim ◽  
Yeseul Moon ◽  
Juneyoung Park
Keyword(s):  
Traffic Accident ◽  
Latent Class ◽  
Injury Severity ◽  
Freeway Traffic ◽  
Factors Affecting ◽  
Heterogeneous Effects ◽  
Facility Type

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.

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

Health literacy among mothers of under 3 years old children at the Hanoi Medical University Clinic: A latent class analysis

2021 ◽  
Vol 30 (9) ◽  
pp. 51-58
Author(s):  
Nguyen Thi Huong Thao ◽  
Pham Quang Thai ◽  
Do Thi Thanh Toan ◽  
Dinh Thai Son ◽  
Luu Ngoc Hoat ◽  
...  
Keyword(s):  
Health Literacy ◽  
Health Information ◽  
Latent Class ◽  
Service Providers ◽  
Health Management ◽  
Factors Affecting ◽  
Limited Health Literacy ◽  
Education Levels ◽  
Literacy Scores ◽  
Medical University

Health literacy refers to the degree to which people can access and understand health information, as well as communicate their health needs to service providers. The scale has been standardized and divided into 3 groups: Health care, prevention of disease, health promotion. Children under 3 years have immature immunological system, which can affect their development in the future. However, the health management, diseases treatment, and diseases prevention of children younger than 3 years of age depend signifcantly on the health literacy of their mothers. This study aims to describe the health literacy of mothers who have children under 3 years and some factors affecting their health literacy. Data were collected on 389 mothers of children younger than 3 years who take their children to the vaccination clinics at Hanoi Medical University and latent analysis was conducted to identify class of health literacy within the sample. Three health literacy classes were identifed. The lowest mean health literacy index was within the disease prevention dimension, where the largest number of respondents showed limited health literacy. Three distinct health literacy level were identifed and termed low (n = 35.9%), moderate (n = 243, 62.5%) and high health literacy (n = 111, 28.5%). We found that higher scores of Health Literacy Scores (HLS) closely correlated with higher educational levels, the job of mothers, the age of children and the frequency of searching for health information using the internet. There were signifcant better overall scores in HLS among parents with higher education levels (university degree or higher with more than under high school graduated).

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.

Augmenting Classifiers Performance through Clustering

2018 ◽  
Vol 8 (1) ◽  
pp. 57-68 ◽  
Author(s):  
Sachin Kumar ◽  
Prayag Tiwari ◽  
Kalitin Vladimirovich Denis
Keyword(s):  
Traffic Accident ◽  
Latent Class ◽  
Safety Concern ◽  
Health And Safety ◽  
Road Accident ◽  
Support Vector ◽  
Road Accidents ◽  
Real World Data ◽  
The Public ◽  
Accident Data

Road and traffic accident data analysis are one of the prime interests in the present era. It does not only relate to the public health and safety concern but also associated with using latest techniques from different domains such as data mining, statistics, machine learning. Road and traffic accident data have different nature in comparison to other real-world data as road accidents are uncertain. In this article, the authors are comparing three different clustering techniques: latent class clustering (LCC), k-modes clustering and BIRCH clustering, on road accident data from an Indian district. Further, Naïve Bayes (NB), random forest (RF) and support vector machine (SVM) classification techniques are used to classify the data based on the severity of road accidents. The experiments validate that the LCC technique is more suitable to generate good clusters to achieve maximum classification accuracy.

Sign in / Sign up

Export Citation Format

Share Document