Severity, Spatial Pattern and Statistical Analysis of Road Traffic Crash Hot Spots in Ethiopia

The reduction of traffic crashes, as well as their socio-economic consequences, has captivated the attention of safety professionals and transportation agencies. The most important activity for an effective road safety practice is to identify hazardous roadway areas based on a spatial pattern analysis of crashes and an evaluation of crash spatial relations with neighboring areas and other relevant factors. For decades, safety researchers have adopted several techniques to analyze historical road traffic crash (RTC) information using the advanced GIS-based hot spot analysis. The objective of this study is to present a GIS technique for identifying crash hot spots based on spatial autocorrelation analysis using a four-year (2014–2017) crash data across Ethiopian regions, as well as zones and towns in the Oromia region. The study considered the corresponding severity values of RTCs for the analysis and ranking of crash hot spot areas. The spatial autocorrelation tool in ArcGIS 10.5 was used to analyze the spatial patterns of RTCs and then the Getis Ord Gi* statistics tool was used to identify high and low crash severity cluster zones. The results showed that the methods used in this analysis, which incorporated Moran’s I spatial autocorrelation of crash incidents, Getis Ord Gi* and crash severity index, proved to be a fruitful strategy for identifying and ranking crash hot spots. The identified crash hot spot areas are along the entrance to and exit from Addis Ababa, Ethiopia’s capital city, so the responsible bodies and traffic management agencies should give top priority attention and conduct a thorough study to reduce the socio-economic effect of RTCs.

Download Full-text

Support Vector Machine with Principle Component Analysis for Road Traffic Crash Severity Classification

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/551/1/012068 ◽

2019 ◽

Vol 551 ◽

pp. 012068

Author(s):

N.H.M. Radzi ◽

I.S.B. Gwari ◽

N.H. Mustaffa ◽

R. Sallehuddin

Keyword(s):

Support Vector Machine ◽

Principle Component Analysis ◽

Road Traffic ◽

Component Analysis ◽

Support Vector ◽

Road Traffic Crash ◽

Crash Severity ◽

Traffic Crash ◽

Principle Component ◽

Severity Classification

Download Full-text

Traffic Crash Severity Prediction—A Synergy by Hybrid Principal Component Analysis and Machine Learning Models

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17207598 ◽

2020 ◽

Vol 17 (20) ◽

pp. 7598

Author(s):

Khaled Assi

Keyword(s):

Principal Component Analysis ◽

Principal Components ◽

Road Traffic ◽

Principal Component ◽

Component Analysis ◽

Support Vector ◽

Road Traffic Crash ◽

Crash Severity ◽

Traffic Crash ◽

Severity Prediction

The accurate prediction of road traffic crash (RTC) severity contributes to generating crucial information, which can be used to adopt appropriate measures to reduce the aftermath of crashes. This study aims to develop a hybrid system using principal component analysis (PCA) with multilayer perceptron neural networks (MLP-NN) and support vector machines (SVM) in predicting RTC severity. PCA shows that the first nine components have an eigenvalue greater than one. The cumulative variance percentage explained by these principal components was found to be 67%. The prediction accuracies of the models developed using the original attributes were compared with those of the models developed using principal components. It was found that the testing accuracies of MLP-NN and SVM increased from 64.50% and 62.70% to 82.70% and 80.70%, respectively, after using principal components. The proposed models would be beneficial to trauma centers in predicting crash severity with high accuracy so that they would be able to prepare for appropriate and prompt medical treatment.

Download Full-text

Prevalence and factors associated with road traffic crash among bus drivers in Hanoi, Vietnam

International Journal of Injury Control and Safety Promotion ◽

10.1080/17457300.2012.748810 ◽

2013 ◽

Vol 20 (4) ◽

pp. 368-373 ◽

Cited By ~ 1

Author(s):

Quang Ngoc La ◽

Andy H. Lee ◽

Lynn B. Meuleners ◽

Dat Van Duong

Keyword(s):

Road Traffic ◽

Road Traffic Crash ◽

Bus Drivers ◽

Traffic Crash ◽

Factors Associated

Download Full-text

169 Risk of road traffic crash, medical factors and behavioural adaptations in elderly drivers

Injury Prevention ◽

10.1136/injuryprev-2016-042156.169 ◽

2016 ◽

Vol 22 (Suppl 2) ◽

pp. A62.2-A62

Author(s):

Audrey Luxcey ◽

Emmanuel Lagarde ◽

Sylviane Lafont ◽

Marie Zins ◽

Benjamin Contrand ◽

...

Keyword(s):

Road Traffic ◽

Road Traffic Crash ◽

Traffic Crash ◽

Medical Factors ◽

Elderly Drivers ◽

Behavioural Adaptations

Download Full-text

Different Patterns of Mental Health Outcomes among Road Traffic Crash Survivors: A Prospective Cohort Study

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18041564 ◽

2021 ◽

Vol 18 (4) ◽

pp. 1564

Author(s):

Jelena Kovacevic ◽

Ivica Fotez ◽

Ivan Miskulin ◽

Davor Lesic ◽

Maja Miskulin ◽

...

Keyword(s):

Posttraumatic Stress Disorder ◽

Mental Disorders ◽

Posttraumatic Stress ◽

Stress Disorder ◽

Road Traffic ◽

Sociodemographic Factors ◽

Road Traffic Crash ◽

Traffic Crash ◽

Factors Associated ◽

Before And After

This study aimed to investigate factors associated with the symptoms of mental disorders following a road traffic crash (RTC). A prospective cohort of 200 people was followed for 6 months after experiencing an RTC. The cohort was comprised of uninjured survivors and injured victims with all levels of road traffic injury (RTI) severity. Multivariable logistic regression analyses were performed to evaluate the associations between the symptoms of depression, posttraumatic stress disorder and anxiety one and six months after the RTC, along with sociodemographic factors, health status before and after the RTC, factors related to the RTI and factors related to the RTC. The results showed associations of depression, anxiety, and posttraumatic stress disorder symptoms with sociodemographic factors, factors related to the health status before and after the RTC and factors related to the RTC. Factors related to the RTI showed associations only with depression and posttraumatic stress disorder symptoms. Identifying factors associated with mental disorders following an RTC is essential for establishing screening of vulnerable individuals at risk of poor mental health outcomes after an RTC. All RTC survivors, regardless of their RTI status, should be screened for factors associated with mental disorders in order to successfully prevent them.

Download Full-text