Investigating Head-On Crash Severity Involving Commercial Motor Vehicles in Kentucky

2021 ◽  
pp. 036119812110108
Author(s):  
James Smith ◽  
Mehdi Hosseinpour ◽  
Ryan Mains ◽  
Nathanael Hummel ◽  
Kirolos Haleem
Keyword(s):  
Injury Severity ◽  
Motor Vehicle ◽  
Preliminary Investigation ◽  
Motor Vehicles ◽  
Severe Injuries ◽  
Factors Affecting ◽  
Crash Data ◽  
Centerline Rumble Strips ◽  
Crash Injuries ◽  
Drug Impairment

This study examines various features affecting the severity associated with commercial motor vehicle (CMV, i.e., large truck and bus) head-on collisions on Kentucky highways. Recent five-year (2015–2019) crash data and variables rarely explored before (e.g., presence of centerline rumble strips, type of passing zone, and terrain type) were collected and prepared using Google Maps. A total of 378 CMV-related head-on collisions were analyzed. The generalized ordered probit (GOP) model was employed to identify the significant factors affecting the severity level resulting from CMV head-on collisions. The model allows the coefficients to vary across the injury severity categories for reliable parameter estimations. From the preliminary investigation, rolling terrains had the highest share of severe CMV head-on crashes (62% and 71% for multilane and two-lane roadways, respectively). The presence of centerline rumble strips could reduce severe crash outcomes along multilane and two-lane facilities. The GOP model identified various significant predictors of minor and severe injuries from CMV head-on crashes. Occupants wearing seatbelt were 39.3% less likely to sustain severe head-on crash injuries. From the roadway characteristics, presence of median cable and concrete barriers could significantly reduce the probability of severe head-on crash injuries, with median cables being more effective. With regard to the driver characteristics, drug impairment and speeding increased the risk of sustaining fatal/serious injuries by 39.5% and 26.4%, respectively. Necessary safety recommendations are proposed to reduce the severity of CMV head-on-related collisions. One example is installing median cable barriers along roadway stretches with a history of head-on CMV-related crashes.

scholarly journals Analysis of Severe Injuries in Crashes Involving Large Trucks Using K-Prototypes Clustering-Based GBDT Model

Safety ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 32
Author(s):  
Syed As-Sadeq Tahfim ◽  
Chen Yan
Keyword(s):  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
The United States ◽  
Clustering Methods ◽  
Severe Injuries ◽  
Traffic Crash ◽  
Crash Data ◽  
Boosted Decision Trees ◽  
The Individual ◽  
Crash Characteristics

The unobserved heterogeneity in traffic crash data hides certain relationships between the contributory factors and injury severity. The literature has been limited in exploring different types of clustering methods for the analysis of the injury severity in crashes involving large trucks. Additionally, the variability of data type in traffic crash data has rarely been addressed. This study explored the application of the k-prototypes clustering method to countermeasure the unobserved heterogeneity in large truck-involved crashes that had occurred in the United States between the period of 2016 to 2019. The study segmented the entire dataset (EDS) into three homogeneous clusters. Four gradient boosted decision trees (GBDT) models were developed on the EDS and individual clusters to predict the injury severity in crashes involving large trucks. The list of input features included crash characteristics, truck characteristics, roadway attributes, time and location of the crash, and environmental factors. Each cluster-based GBDT model was compared with the EDS-based model. Two of the three cluster-based models showed significant improvement in their predicting performances. Additionally, feature analysis using the SHAP (Shapley additive explanations) method identified few new important features in each cluster and showed that some features have a different degree of effects on severe injuries in the individual clusters. The current study concluded that the k-prototypes clustering-based GBDT model is a promising approach to reveal hidden insights, which can be used to improve safety measures, roadway conditions and policies for the prevention of severe injuries in crashes involving large trucks.

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 FAKTOR-FAKTOR YANG MEMPENGARUHI KEPATUHAN WAJIB PAJAK DALAM MEMBAYAR PAJAK KENDARAAN BERMOTOR (PKB) DI UNIT PELAYANAN PENDAPATAN PROVINSI (UPPP) KABUPATEN SELUMA

2015 ◽  
Vol 2 (1) ◽  
Author(s):  
R.A. Vivi Yulian Sari ◽  
Neri Susanti
Keyword(s):  
Rating Scale ◽  
Motor Vehicle ◽  
Tax Compliance ◽  
Descriptive Study ◽  
Motor Vehicles ◽  
Factors Affecting ◽  
Service Unit ◽  
Scale Method

R.A. Vivi Yulian Sari, Neri Susanti; Factors That Influence Compliance With Taxpayers In Paying Tax On Motor Vehicles In The Province Revenue Service Unit Of Seluma Regency This study aimed to determine the factors that influence taxpayers compliance in paying motor vehicle tax (CLA ) in Unit Revenue Services of Province ( UPPP ) Seluma District. Type of this study is descriptive study. The population in this study is whousedtaxpayermotor vehicleisregisteredinthe Unit RevenueService of Province (UPPP) SelumaDistrict, witha sample of30taxpayer-wheeled motor vehicletwo (2) located atTaisMarket and registered inUnit RevenueService of Province of(UPPP) Seluma Districtin July2013.The data collected by usingquestionnaire. Data analyzed by using rating scale method. Taxpayer perceptions towards tax penalties showed a significant effect on tax compliance in carrying out its obligations to pay motor vehicle tax ( CLA ) , is seen from the position of the respondent's perception of the value of tax penalties perceptions of factors affecting tax compliance in paying taxes on motor vehicles in Unit Revenue Service of Province( UPPP ) Seluma District with the total score of 316 is in the interval 308-381 , agreed criteria.

Does Alcohol Intoxication Protect Patients from Severe Injury and Reduce Hospital Mortality? The Association of Alcohol Consumption with the Severity of Injury and Survival in Trauma Patients

2013 ◽  
Vol 79 (12) ◽  
pp. 1289-1294 ◽  
Author(s):  
Chi-Hsun Hsieh ◽  
Li-Ting Su ◽  
Yu-Chun Wang ◽  
Chih-Yuan Fu ◽  
Hung-Chieh Lo ◽  
...  
Keyword(s):  
Alcohol Consumption ◽  
Hospital Stay ◽  
Brain Injuries ◽  
Injury Severity ◽  
Motor Vehicle ◽  
Length Of Hospital Stay ◽  
Trauma Patients ◽  
Prolonged Hospital Stay ◽  
Severe Injuries ◽  
Prolonged Hospital

Alcohol-related motor vehicle collisions are a major cause of mortality in trauma patients. This prospective observational study investigated the influence of antecedent alcohol use on outcomes in trauma patients who survived to reach the hospital. From 2005 to 2011, all patients who were older than 18 years and were admitted as a result of motor vehicle crashes were included. Blood alcohol concentration (BAC) was routinely measured for each patient on admission. Patients were divided into four groups based on their BAC level, which included nondrinking, BAC less than 100, BAC 100 to 200, and BAC 200 mg/dL or greater. Patient demographics, physical status and injury severity on admission, length of hospital stay, and outcome were compared between the groups. Odds ratios of having a severe injury, prolonged hospital stay, and mortality were estimated. Patients with a positive BAC had an increased risk of sustaining craniofacial and thoracoabdominal injuries. Odds ratios of having severe injuries (Injury Severity Score [ISS] 16 or greater) and a prolonged hospital stay were also increased. However, for those patients whose ISS was 16 or greater and who also had a brain injury, risk of fatality was significantly reduced if they were intoxicated (BAC 200 mg/dL or greater) before injury. Alcohol consumption does not protect patients from sustaining severe injuries nor does it shorten the length of hospital stay. However, there were potential survival benefits related to alcohol consumption for patients with brain injuries but not for those without brain injuries. Additional research is required to investigate the mechanism of this association further.

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 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 RESEARCH OF THE AIR TEMPERATURE INFLUENCE ON FUEL CONSUMPTION OF A PASSENGER CAR CONVERTED TO OPERATE ON LIQUEFIED PETROLEUM GAS

2021 ◽  
pp. 60-67
Author(s):  
Roman O. Rekhalov ◽  
◽  
Evgeniy M. Chikishev ◽  
Keyword(s):  
Fuel Consumption ◽  
Alternative Fuels ◽  
Motor Vehicle ◽  
Motor Vehicles ◽  
Liquefied Petroleum Gas ◽  
Passenger Car ◽  
Factors Affecting ◽  
Mobile Sources ◽  
Air Temperatures ◽  
Gas Consumption

Due to the rapid growth of environmental pollution from mobile sources, the part of alternative fuels use is increasing. One of these for motor vehicle is liquefied petroleum gas (LPG). This study focuses on the LPG use by Mitsubishi Lancer X passenger car in driving conditions. Based on the results of the previous studies analysis, the most significant factors affecting the change in fuel consumption by motor vehicles were identified. It was proved that the decrease in the ambient temperature from +30 to –20 °C leads to an increase in gas consumption from 11.2 to 13.6 l/100 km. In addition, at air temperatures from –20 °C and below, the gas-fueled engine is unstable.

scholarly journals Electric Bicyclist Injury Severity during Peak Traffic Periods: A Random-Parameters Approach with Heterogeneity in Means and Variances

2021 ◽  
Vol 18 (21) ◽  
pp. 11131
Author(s):  
Tong Zhu ◽  
Zishuo Zhu ◽  
Jie Zhang ◽  
Chenxuan Yang
Keyword(s):  
Traffic Control ◽  
Injury Severity ◽  
Safety Management ◽  
Management Strategies ◽  
Mixed Logit ◽  
Random Parameters ◽  
Severe Injuries ◽  
Crash Data ◽  
Electric Bicycle ◽  
The Impact

Accidents involving electric bicycles, a popular means of transportation in China during peak traffic periods, have increased. However, studies have seldom attempted to detect the unique crash consequences during this period. This study aims to explore the factors influencing injury severity in electric bicyclists during peak traffic periods and provide recommendations to help devise specific management strategies. The random-parameters logit or mixed logit model is used to identify the relationship between different factors and injury severity. The injury severity is divided into four categories. The analysis uses automobile and electric bicycle crash data of Xi’an, China, between 2014 and 2019. During the peak traffic periods, the impact of low visibility significantly varies with factors such as areas with traffic control or without streetlights. Furthermore, compared with traveling in a straight line, three different turnings before the crash reduce the likelihood of severe injuries. Roadside protection trees are the most crucial measure guaranteeing riders’ safety during peak traffic periods. This study reveals the direction, magnitude, and randomness of factors that contribute to electric bicycle crashes. The results can help safety authorities devise targeted transportation safety management and planning strategies for peak traffic periods.

scholarly journals Injury Severity of Bus–Pedestrian Crashes in South Korea Considering the Effects of Regional and Company Factors

2019 ◽  
Vol 11 (11) ◽  
pp. 3169 ◽  
Author(s):  
Ho-Chul Park ◽  
Yang-Jun Joo ◽  
Seung-Young Kho ◽  
Dong-Kyu Kim ◽  
Byung-Jung Park
Keyword(s):  
South Korea ◽  
Injury Severity ◽  
The Elderly ◽  
Type I ◽  
Severe Injuries ◽  
Crash Data ◽  
Level Model ◽  
Multi Level ◽  
Pedestrian Crashes ◽  
Upper Level

Bus–pedestrian crashes typically result in more severe injuries and deaths than any other type of bus crash. Thus, it is important to screen and improve the risk factors that affect bus–pedestrian crashes. However, bus–pedestrian crashes that are affected by a company’s and regional characteristics have a cross-classified hierarchical structure, which is difficult to address properly using a single-level model or even a two-level multi-level model. In this study, we used a cross-classified, multi-level model to consider simultaneously the unobserved heterogeneities at these two distinct levels. Using bus–pedestrian crash data in South Korea from 2011 through to 2015, in this study, we investigated the factors related to the injury severity of the crashes, including crash level, regional and company level factors. The results indicate that the company and regional effects are 16.8% and 5.1%, respectively, which justified the use of a multi-level model. We confirm that type I errors may arise when the effects of upper-level groups are ignored. We also identified the factors that are statistically significant, including three regional-level factors, i.e., the elderly ratio, the ratio of the transportation infrastructure budget, and the number of doctors, and 13 crash-level factors. This study provides useful insights concerning bus–pedestrian crashes, and a safety policy is suggested to enhance bus–pedestrian safety.

scholarly journals The Effects of the COVID-19 Pandemic on Trauma Presentations in a Level One Trauma Center

2020 ◽  
pp. 000313482097371
Author(s):  
Aditya K. Devarakonda ◽  
Chase J. Wehrle ◽  
Fairouz L. Chibane ◽  
Peter D. Drevets ◽  
Elizabeth D. Fox ◽  
...  
Keyword(s):  
Injury Severity Score ◽  
Hospital Stay ◽  
Severity Score ◽  
Injury Severity ◽  
Motor Vehicle ◽  
Length Of Hospital Stay ◽  
Current Data ◽  
Motor Vehicles ◽  
Trauma Severity ◽  
Alcohol Involvement

Background Over 28 million confirmed cases of COVID-19 have been reported to date, resulting in over 900 000 deaths. With an increase in awareness regarding the virus, the behavior of general population has changed dramatically. As activities such as driving and hospital presentation patterns have changed, our study aimed to assess the differences in trauma case variables before and during the COVID-19 pandemic. Methods Trauma data for the period of March 1st-June 15th were compared for the years 2015-2019 (pre-COVID) and 2020 (COVID). The data were analyzed across the following categories: injury severity score, injury mechanism, motor vehicle crashes (MVCs) vs. other blunt injuries, alcohol involvement, and length of hospital stay. Results The median injury severity score pre-COVID and during COVID was 9, representing no change. There was no difference in overall distribution of mechanism of injury; however, there was a significant decrease in the percentage of MVCs pre-COVID (36.39%) vs. COVID (29.6%, P < .05). Alcohol was significantly more likely to be involved in trauma during COVID-19 ( P < .05). The mean hospital stay increased from 3.87-5.4 days during COVID-19 ( P < .05). Discussion We saw similar results to prior studies in terms of there being no change in trauma severity. Our observation that motor vehicle collisions have decreased is consistent with current data showing decreased use of motor vehicles during the pandemic. We also observed an increase in alcohol-related cases which are consistent with the reported changes in alcohol consumption since the pandemic began.

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