A Study on Injury Severity Prediction for Car-to-Car Traffic Accidents

2020 ◽  
Vol 19 (4) ◽  
pp. 13-29
Author(s):  
Changwan Ko ◽  
◽  
Hyeonmin Kim ◽  
Young-Seon Jeong ◽  
Jaehee Kim
Keyword(s):  
Traffic Accidents ◽  
Injury Severity ◽  
Car Traffic ◽  
Severity Prediction

scholarly journals Traffic Accidents Classification and Injury Severity Prediction

2020 ◽  
Vol 9 (6) ◽  
pp. 845-849
Keyword(s):  
Logistic Regression ◽  
Predictive Model ◽  
Road Safety ◽  
Traffic Accidents ◽  
Injury Severity ◽  
Severity Prediction ◽  
Accident Data ◽  
Life Threatening ◽  
Analyse Data ◽  
Logistic Regression Algorithm

Traffic accidents are one of the most life-threatening dangers to human being. Deaths and injuries due to traffic accidents have a great impact on society. Traffic accidents information and data provided by public can be useful to classify these accidents according to their type and severity, and consequently try to build predictive model. Detecting and identifying injury severity in traffic accidents in real time is primordial for speeding post-accidents protocols as well as developing general road safety policies. In this project we are using Logistic Regression algorithm to classify accident data. The data to be analysed is collected from various sources, is both structured and unstructured and has several attributes. In this project we are going to detect and analyse data together to generate decision trees that give insights on previous accidents.

scholarly journals Improving the timeliness and accuracy of injury severity data in road traffic accidents in an emerging economy setting

2018 ◽  
Vol 31 (2) ◽  
pp. 140-146
Author(s):  
Carlos Lam ◽  
Chang-I Chen ◽  
Chia-Chang Chuang ◽  
Chia-Chieh Wu ◽  
Shih-Hsiang Yu ◽  
...  
Keyword(s):  
Traffic Accidents ◽  
Injury Severity ◽  
Road Traffic ◽  
Emerging Economy ◽  
Road Traffic Accidents

Children and Terror Casualties Receive Preference in ICU Admissions

2012 ◽  
Vol 6 (1) ◽  
pp. 14-19 ◽  
Author(s):  
Kobi Peleg ◽  
Michael Rozenfeld ◽  
Eran Dolev ◽  
Keyword(s):  
Traffic Accidents ◽  
Injury Severity ◽  
Road Traffic ◽  
Age Groups ◽  
Trauma Patients ◽  
Trauma Centers ◽  
Severe Injuries ◽  
Level I ◽  
Hospital Emergency Departments ◽  
Trauma Type

ABSTRACTObjective: Trauma casualties caused by terror-related events and children injured as a result of trauma may be given preference in hospital emergency departments (EDs) due to their perceived importance. We investigated whether there are differences in the treatment and hospitalization of terror-related casualties compared to other types of injury events and between children and adults injured in terror-related events.Methods: Retrospective study of 121 608 trauma patients from the Israel Trauma Registry during the period of October 2000-December 2005. Of the 10 hospitals included in the registry, 6 were level I trauma centers and 4 were regional trauma centers. Patients who were hospitalized or died in the ED or were transferred between hospitals were included in the registry.Results: All analyses were controlled for Injury Severity Score (ISS). All patients with ISS 1-24 terror casualties had the highest frequency of intensive care unit (ICU) admissions when compared with patients after road traffic accidents (RTA) and other trauma. Among patients with terror-related casualties, children were admitted to ICU disproportionally to the severity of their injury. Logistic regression adjusted for injury severity and trauma type showed that both terror casualties and children have a higher probability of being admitted to the ICU.Conclusions: Injured children are admitted to ICU more often than other age groups. Also, terror-related casualties are more frequently admitted to the ICU compared to those from other types of injury events. These differences were not directly related to a higher proportion of severe injuries among the preferred groups.(Disaster Med Public Health Preparedness. 2012;6:14–19)

scholarly journals Identification of Factors Influencing Injury Severity Prediction (ISP) in Real World Accident Based on NASS-CDS

2015 ◽  
Vol 6 (4) ◽  
pp. 119-125
Author(s):  
Pal Chinmoy ◽  
Tomosaburo Okabe ◽  
Kulothungan Vimalathithan ◽  
Sangolla Narahari ◽  
Manoharan Jeyabharath ◽  
...  
Keyword(s):  
Real World ◽  
Injury Severity ◽  
Factors Influencing ◽  
Severity Prediction

Motorcyclist Traffic Accidents and Risk Factors: A Singapore Study

1990 ◽  
Vol 4 (1) ◽  
pp. 34-38 ◽  
Author(s):  
Tze Wai Wong ◽  
Wai-On Phoon ◽  
James Lee ◽  
Ivy Po Chu Yiu ◽  
Kam Pui Fung ◽  
...  
Keyword(s):  
Public Education ◽  
Traffic Accidents ◽  
Injury Severity ◽  
Driving Experience ◽  
Factors Associated ◽  
Singapore General Hospital ◽  
Breath Testing ◽  
Severity Scores ◽  
One Year ◽  
Drunken Driving

Motorcyclist accidents cause significant morbidity and mortality in Singapore. To elucidate personal and environmental factors associated with such accidents, we studied 198 motorcyclists who were hospitalized in Singapore General Hospital between April 1986 and June 1987. The patients were mostly young and almost exclusively male with a high proportion of Malays. Most accidents occurred on Sundays and at night. Ten percent of the victims took alcohol before the accident. Most had low injury severity scores (ISS). Less experienced drivers had a significantly higher ISS than those with one year or longer of driving experience. Vigorous control of drunken driving, through public education and intensive breath testing, should reduce the incidence of traffic accidents.

Crash Data-Based Investigation into How Injury Severity Is Affected by Driver Errors

2020 ◽  
Vol 2674 (5) ◽  
pp. 452-464 ◽  
Author(s):  
Mohammad Razaur Rahman Shaon ◽  
Xiao Qin
Keyword(s):  
Injury Severity ◽  
Model Performance ◽  
Severe Injuries ◽  
Crash Data ◽  
Severity Prediction ◽  
Safety Interventions ◽  
Driving Task ◽  
Driver Error ◽  
Crash Characteristics ◽  
Crash Injury Severity

Unsafe driving behaviors, driver limitations, and conditions that lead to a crash are usually referred to as driver errors. Even though driver errors are widely cited as a critical reason for crash occurrence in crash reports and safety literature, the discussion on their consequences is limited. This study aims to quantify the effect of driver errors on crash injury severity. To assist this investigation, driver errors were categorized as sequential events in a driving task. Possible combinations of driver error categories were created and ranked based on statistical dependences between error combinations and injury severity levels. Binary logit models were then developed to show that typical variables used to model injury severity such as driver characteristics, roadway characteristics, environmental factors, and crash characteristics are inadequate to explain driver errors, especially the complicated ones. Next, ordinal probit models were applied to quantify the effect of driver errors on injury severity for rural crashes. Superior model performance is observed when driver error combinations were modeled along with typical crash variables to predict the injury outcome. Modeling results also illustrate that more severe crashes tend to occur when the driver makes multiple mistakes. Therefore, incorporating driver errors in crash injury severity prediction not only improves prediction accuracy but also enhances our understanding of what error(s) may lead to more severe injuries so that safety interventions can be recommended accordingly.

scholarly journals Crash Telemetry-Based Injury Severity Prediction is Equivalent to or Out-Performs Field Protocols in Triage of Planar Vehicle Collisions

2019 ◽  
Vol 34 (04) ◽  
pp. 356-362 ◽  
Author(s):  
Katherine He ◽  
Peng Zhang ◽  
Stewart C. Wang
Keyword(s):  
At Risk ◽  
Injury Severity ◽  
Motor Vehicle ◽  
Principal Direction ◽  
Trauma Centers ◽  
Sampling System ◽  
Vehicle Collisions ◽  
Field Triage ◽  
Severity Prediction ◽  
Patients At Risk

AbstractIntroduction:With the increasing availability of vehicle telemetry technology, there is great potential for Advanced Automatic Collision Notification (AACN) systems to improve trauma outcomes by detecting patients at-risk for severe injury and facilitating early transport to trauma centers.Methods:National Automotive Sampling System Crashworthiness Data System (NASS-CDS) data from 1999-2013 were used to construct a logistic regression model (injury severity prediction [ISP] model) predicting the probability that one or more occupants in planar, non-rollover motor vehicle collisions (MVCs) would have Injury Severity Score (ISS) 15+ injuries. Variables included principal direction of force (PDOF), change in velocity (Delta-V), multiple impacts, presence of any older occupant (≥55 years old), presence of any female occupant, presence of right-sided passenger, belt use, and vehicle type. The model was validated using medical records and 2008-2011 crash data from AACN-enabled Michigan (USA) vehicles identified from OnStar (OnStar Corporation; General Motors; Detroit, Michigan USA) records. To compare the ISP to previously established protocols, a literature search was performed to determine the sensitivity and specificity of first responder identification of ISS 15+ for MVC occupants.Results:The study population included 924 occupants in 836 crash events. The ISP model had a sensitivity of 72.7% (95% Confidence Interval [CI] 41%-91%) and specificity of 93% (95% CI 92%-95%) for identifying ISS 15+ occupants injured in planar MVCs. The current standard 2006 Field Triage Decision Scheme (FTDS) was 56%-66% sensitive and 75%-88% specific in identifying ISS 15+ patients.Conclusions:The ISP algorithm comparably is more sensitive and more specific than current field triage in identifying MVC patients at-risk for ISS 15+ injuries. This real-world field study shows telemetry data transmitted before dispatch of emergency medical systems can be helpful to quickly identify patients who require urgent transfer to trauma centers.

scholarly journals Exploring Factors Influencing Injury Severity of Vehicle At-Fault Accidents: A Comparative Analysis of Passenger and Freight Vehicles

2020 ◽  
Vol 17 (4) ◽  
pp. 1146 ◽  
Author(s):  
Jianyu Wang ◽  
Huapu Lu ◽  
Zhiyuan Sun ◽  
Tianshi Wang
Keyword(s):  
Influencing Factors ◽  
Traffic Accidents ◽  
Injury Severity ◽  
Vehicle Accidents ◽  
Passenger Vehicles ◽  
Factors Influencing ◽  
Administrative Division ◽  
Hit And Run ◽  
Side Collision ◽  
Freight Vehicles

The objective of this study is to find factors influencing the injury severity of vehicle at-fault accidents in Shenyang (China), and discuss the commonalities and differences between passenger and freight vehicle accidents. We analyzed 1647 traffic accidents from 2015 to 2017, in which motor vehicles were fully or mainly responsible, including 1164 traffic accidents caused by passenger vehicles and 483 traffic accidents caused by freight vehicles. Twenty influencing factors from the aspects of accident, driver, time, space and environmental attributes are analyzed to find their statistical connection with injury severity using the binary logistic regression model. For passenger vehicles, five influencing factors (side collision; illegal act while driving; hit-and-run; season and administrative division), showed statistically significant correlations with the injury severity. For freight vehicles, three influencing factors (illegal act while driving; season and administrative division), showed statistically significant correlations with the injury severity. Illegal act while driving is the only common influencing factor for the injury severity of both passenger and freight vehicle accidents. Side collision and hit-and-run are significant influencing factors for the injury severity of passenger vehicle accidents, but not for freight vehicle accidents. Season and administrative division present different results on influencing passenger and freight vehicle accidents. Based on these results, measures including driver education and road infrastructure improvement could be implemented to reduce the injury severity of accidents in passenger and freight vehicles.

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