traffic crash
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2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Zhenzhou Yuan ◽  
Kun He ◽  
Yang Yang

With the development of freeway system informatization, it is easier to obtain the traffic flow data of freeway, which are widely used to study the relationship between traffic flow state and traffic safety. However, as the development degree of the freeway system is different in different regions, the sample size of traffic data collected in some regions is insufficient, and the precision of data is relatively low. In order to study the influence of limited data on the real-time freeway traffic crash risk modeling, three data sets including high precision data, small sample data, and low precision data were considered. Firstly, Bayesian Logistic regression was used to identify and predict the risk of three data sets. Secondly, based on the Bayesian updating method, the migration test towards high and low precision data sets was established. Finally, the applicability of machine learning and statistical methods to low precision data set was compared. The results show that the prediction performance of Bayesian Logistic regression improves with the increasing of sample size. Bayesian Logistic regression can identify various significant risk factors when data sets are of different precision. Comparatively, the prediction performance of the support vector machine is better than that of Bayesian Logistic. In addition, Bayesian updating method can improve the prediction performance of the transplanted model.


2021 ◽  
Author(s):  
Zuriyash Mengistu ◽  
Ahmed Ali ◽  
Teferi Abegaz

Abstract Background Traumatic brain injury (TBI) is one of the common preventable causes of mortality and disability among road traffic victims worldwide, most especially in low- and middle-income countries, including Ethiopia. Objective to determine risk factors of mortality after traumatic brain injury due to road traffic crash. Methods This study aimed to examine the predictive factors of short-term mortality after severe brain injury due to a road traffic crash. The study was done on a prospective cohort of 242 severely brain-injured patients selected using cluster sampling in Addis Ababa City hospitals. The study was conducted from February 2018 to November 2019. Data were collected from brain-injured patients using a questionnaire and recorded findings within the first 24 hours of admission, Survival Analysis was used for statistical analysis. Ethical clearance was obtained from the Addis Ababa University, College of Health Sciences Institutional Review Board (IRB). Confidentiality of information about injured patients was maintained. Results In this study, the death rate was 73(30.2%). The majority of TBI patients accounting for, 186(81%) were men. The median age of TBI patients was 29 years. The hazard for those patients with subnormal body temperature was 1.64 times that of normal temperature (AHR: 1.64; CI: 2.14-10.29). The estimated fatality hazard ratio for patients who experienced Glasgow Coma Scale (GCS)below six was 5.61 times higher compared to GCS six to eight (CI:3.1-10.24). Conclusion In conclusion, there was high early mortality of patients (30.2%) in Ethiopia. Being men, young and lower GCS were associated with higher mortality hazards. Hence, optimum advanced neuro-surgical pre-hospital care programs are urgently needed.


2021 ◽  
Author(s):  
Yu Miao ◽  
Bowen Cai ◽  
Tao Li

Abstract Traffic crash prediction is vital for relevant agencies to take precautionary measures to minimize the economic and social losses from traffic accidents. Currently, the popularity of machine learning, deep learning, and traditional regression-based models in crash predictions eclipsed the use of count data time series models. Count data model has many intrinsic advantages over machine learning based methods in crash analysis. It is an extension of conventional time series regression by extending normal distribution to Poisson and Negative binomial. Meanwhile, covariate variables can get properly incorporated and their influence on dependent variable is well interpreted. This study attempts to compare and examine the performances of the count data time series model with the regression-based models in hourly crash prediction, utilizing traffic crash data from the Sutong Yangtze River Bridge in China. Log linear extension of Poisson distribution integer valued generalized autoregressive conditional heteroscedasticity models (INGARCH), as a type of count data model, is adopted and compared with the zero-inflated Poisson model (ZIP), as well as the cumulative link model for ordinal regression (CLM). The performances of ZIP and log linear extension of INGARCH count data model are similar and superior to the performances of CLM. Results showed that previous traffic accidents influence the crash occurrence in the near future and the employment of count data time series model in hourly crash prediction can appropriately capture this influence, with an average model sensitivity rate of 77.5%.


2021 ◽  
Vol 4 (2) ◽  
pp. 221-230
Author(s):  
Zeliha Cagla Kuyumcu ◽  
Suhrab Ahadi ◽  
Hakan Aslan

The lives of approximately 1.3 million people are cut short every year as a result of road traffic crashes. Between 20 and 50 million people suffer non-fatal injuries, with many incurring a disability as a result of their injury. The risk of dying in a road traffic crash is more than 3 times higher in low-income countries than in high-income countries [1]. In Turkey, 18% of traffic accidents was related to pedestrian-vehicle collisions in urban roads in 2020. In addition, 20% of death toll caused by accidents is pedestrians in 2020 [2]. This study deals with the some of classifiers to forecast the number of injuries as a result of traffic accidents. The classifier’s performance ratios were also examined.


2021 ◽  
Vol 32 (4) ◽  
pp. 15-28
Author(s):  
Guanlong Li ◽  
Yueqing Li ◽  
Yalong Li ◽  
Brian Craig ◽  
Xing Wu

Driving is the essential means of travel in Southeast Texas, a highly urbanized and populous area that serves as an economic powerhouse of the whole state. However, driving in Southeast Texas is subject to many risks as this region features a typical humid subtropical climate with long hot summers and short mild winters. Local drivers would encounter intense precipitation, heavy fog, strong sunlight, standing water, slick road surface, and even frequent extreme weather such as tropical storms, hurricanes and flood during their year-around travels. Meanwhile, research has revealed that the fatality rate per 100 million vehicle miles driven in urban Texas became considerably higher than national average since 2010, and no conclusive study has elucidated the association between Southeast Texas crash severity and potential contributing factors. This study used multiple correspondence analysis (MCA) to examine a group of contributing factors on how their combinatorial influences determine crash severity by creating combination clouds on a factor map. Results revealed numerous significant combinatorial effects. For example, driving in rain and extreme weather on a wet road surface has a higher chance in causing crashes that incur severe or deadly injuries. Besides, other contributing factors involving risky behavioral factors, road designs, and vehicle factors were well discussed. The research outcomes could inspire local traffic administration to take more effective countermeasures to systematically mitigate road crash severity.


PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0253690
Author(s):  
Zuriyash Mengistu ◽  
Ahmed Ali ◽  
Teferi Abegaz

Background Road Traffic crash injury is one of the main public health problems resulting in premature death and disability particularly in low-income countries. However, there is limited evidence on the crash fractures in Ethiopia. Objective The study was conducted to assess the magnitude of road traffic crash fractures and visceral injuries. Methods A hospital-based cross-sectional study was conducted on 420 fracture patients. Participants were randomly selected from Addis Ababa City hospitals. The study was carried out between November 2019 and February 2020. Data were collected using a questionnaire and record of medical findings. Multilevel logistic regression analysis was carried out. Ethical clearance was obtained from the Addis Ababa University, College of Health Sciences Institutional Review Board. Confidentiality of participants’ information was maintained. Results The study found out that the majority 265 (63. 1%) of fracture cases were younger in the age group of 18 to 34 years. Males were more affected—311(74.0%). The mortality rate was 59(14.1%), of those 50(85.0%) participants were males. The major road traffic victims were pedestrians—220(52.4%), mainly affected by simple fracture type -105(53.3%) and compound fracture type—92(46. 7%). Drivers mainly suffered from compound fracture type -23 (59.0%). One hundred eighty-two (43.3%) of fracture patients had a visceral injury. Homeless persons who sit or sleep on the roadside had a higher risk of thoracic visceral injury compared to traveler pedestrians (AOR = 4.600(95%CI: 1.215–17.417)); P = 0.025. Conclusion Visceral injury, simple and compound fractures were the common orthopedic injury types reported among crash victims. Males, pedestrians, and young age groups were largely affected by orthopedic fracture cases. Homeless persons who sited or slept on the roadside were significant factors for visceral injury. Therefore, preventing a harmful crash and growing fracture care should be considered to reduce the burden of crash fracture.


2021 ◽  
Vol 11 (19) ◽  
pp. 8828
Author(s):  
Alamirew Mulugeta Tola ◽  
Tamene Adugna Demissie ◽  
Fokke Saathoff ◽  
Alemayehu Gebissa

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.


Author(s):  
Subasish Das ◽  
Xiaoduan Sun ◽  
Bahar Dadashova ◽  
M. Ashifur Rahman ◽  
Ming Sun

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.


2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Amjad H. Albayati ◽  
Zahraa A. Ramadan

This study describes traffic crash rates in selected multilane rural highways in Wasit governorate in Iraq. The main objective of this research is to investigate relationships between total, fatal crash rates and their kinds and factors such as hourly traffic flow and average spot speed. The study is based on data collected from two sources: police stations and traffic surveys. Three highways are selected to cover the locations of the accidents. The selection includes Kut – Suwera with five segments, Kut – ShekhSaad with three segments, and Kut – Hay with two segments multilane divided highways. Multiple linear regression analysis is applied to the data by using SPSS software to attain the relationships between the dependent variables and the independent variables in order to identify elements that are strongly correlated with crashes rates and severity. Seven regression models are developed which verify weak and strong statistical relationships between crashes types and average spot speed with hourly traffic flow respectively. As the hourly traffic flow of automobile grows, the need for safe traffic facilities also grown.  


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