Injury severity analysis of motorcycle crashes: A comparison of latent class clustering and latent segmentation based models with unobserved heterogeneity

2021 ◽  
pp. 100188
Author(s):  
Fangrong Chang ◽  
Shamsunnahar Yasmin ◽  
Helai Huang ◽  
Alan H.S. Chan ◽  
Md. Mazharul Haque
Keyword(s):  
Latent Class ◽  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
Motorcycle Crashes

Modeling Safety Effects of Horizontal Curve Design on Injury Severity of Single-Motorcycle Crashes with Mixed-Effects Logistic Model

2017 ◽  
Vol 2637 (1) ◽  
pp. 38-46 ◽  
Author(s):  
Chunfu Xin ◽  
Zhenyu Wang ◽  
Chanyoung Lee ◽  
Pei-Sung Lin
Keyword(s):  
Logistic Model ◽  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
Mixed Effects ◽  
Severe Injury ◽  
Horizontal Curve ◽  
Random Parameters ◽  
Horizontal Curves ◽  
Curve Design ◽  
Motorcycle Crashes

Horizontal curves have been of great interest to transportation researchers because of expected safety hazards for motorcyclists. The impacts of horizontal curve design on motorcycle crash injuries are not well documented in previous studies. The current study aimed to investigate and to quantify the effects of horizontal curve design and associated factors on the injury severity of single-motorcycle crashes with consideration of the issue of unobserved heterogeneity. A mixed-effects logistic model was developed on the basis of 2,168 single-motorcycle crashes, which were collected on 8,597 horizontal curves in Florida for a period of 11 years (2005 to 2015). Four normally distributed random parameters (moderate curves, reverse curves, older riders, and male riders) were identified. The modeling results showed that sharp curves (radius <1,500 ft) compared with flat curves (radius ≥4,000 ft) tended to increase significantly the probability of severe injury (fatal or incapacitating injury) by 7.7%. In total, 63.8% of single-motorcycle crashes occurring on reverse curves are more likely to result in severe injury, and the remaining 26.2% are less likely to result in severe injury. Motorcyclist safety compensation behaviors (psychologically feeling safe, and then riding aggressively, or vice versa) may result in counterintuitive effects (e.g., vegetation and paved medians, full-access-controlled roads, and pavement conditions) or random parameters (e.g., moderate curve and reverse curve). Other significant factors include lighting conditions (darkness and darkness with lights), weekends, speed or speeding, collision type, alcohol or drug impairment, rider age, and helmet use.

Cluster-Based Approach to Analyzing Crash Injury Severity at Highway–Rail Grade Crossings

2017 ◽  
Vol 2608 (1) ◽  
pp. 58-69 ◽  
Author(s):  
Yashu Kang ◽  
Aemal Khattak
Keyword(s):  
Latent Class ◽  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
Cluster Method ◽  
Model Parameters ◽  
Statistical Regression ◽  
Variational Bayesian ◽  
Crash Data ◽  
Grade Crossings ◽  
Crash Injury Severity

The presence of unobserved heterogeneity in crash data can result in estimation of biased model parameters and incorrect inferences. The research presented in this paper investigated severity of crashes reported at highway–rail grade crossings by appropriately clustering the data, accounting for unobserved heterogeneity. A combination of data mining and statistical regression methods was used to cluster crash data into subsets and then to identify factors associated with crash injury severity levels. This research relied on highway–rail accident, incident, and crossing inventory databases for 2011 to 2015 obtained from FRA. Three clustering methods— K-means, traditional latent class cluster, and variational Bayesian latent class cluster—were considered, and the variational Bayesian latent class cluster method was chosen for partitioning the data set for model estimation. Unclustered data as well as the clustered subsets were used to estimate ordered logit models for crash injury severity. A comparison revealed that the cluster-based approach provided more relevant model parameters and identified factors relevant only to certain clusters of the data.

Factors Affecting Driver Injury Severity in the Wrong-Way Crash: Accounting for Potential Heterogeneity in Means and Variances of Random Parameters

2021 ◽  
pp. 036119812110098
Author(s):  
Miao Yu ◽  
Jinxing Shen ◽  
Changxi Ma
Keyword(s):  
High Speed ◽  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
Research Effort ◽  
Random Parameter ◽  
Policy Implications ◽  
Contributing Factors ◽  
Severe Injuries ◽  
Data Set ◽  
Severity Levels

Because of the high percentage of fatalities and severe injuries in wrong-way driving (WWD) crashes, numerous studies have focused on identifying contributing factors to the occurrence of WWD crashes. However, a limited number of research effort has investigated the factors associated with driver injury-severity in WWD crashes. This study intends to bridge the gap using a random parameter logit model with heterogeneity in means and variances approach that can account for the unobserved heterogeneity in the data set. Police-reported crash data collected from 2014 to 2017 in North Carolina are used. Four injury-severity levels are defined: fatal injury, severe injury, possible injury, and no injury. Explanatory variables, including driver characteristics, roadway characteristics, environmental characteristics, and crash characteristics, are used. Estimation results demonstrate that factors, including the involvement of alcohol, rural area, principal arterial, high speed limit (>60 mph), dark-lighted conditions, run-off-road collision, and head-on collision, significantly increase the severity levels in WWD crashes. Several policy implications are designed and recommended based on findings.

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.

scholarly journals Motorcycle crashes and upper extremity trauma

SICOT-J ◽  
2021 ◽  
Vol 7 ◽  
pp. 8
Author(s):  
Erin Cravez ◽  
Kelsey A. Rankin ◽  
Nathaniel Ondeck ◽  
Lee Yaari ◽  
Michael Leslie ◽  
...  
Keyword(s):  
Upper Extremity ◽  
Injury Severity ◽  
Cohort Analysis ◽  
Cost Of Care ◽  
Hospital Length Of Stay ◽  
Rank Test ◽  
Severe Injuries ◽  
Increased Risk ◽  
Extremity Injuries ◽  
Motorcycle Crashes

Objectives: Upper extremity injuries following motorcycle crashes (MCC) incur increased healthcare costs and rehabilitation needs. We aim to characterize the epidemiology of MCC upper extremity injuries and identify factors that influence the severity of and cost of care for upper extremity injuries. Methods: We performed a retrospective cohort analysis of 571 patients with upper extremity injuries after MCC at a level 1 trauma center from 2002 to 2013. We collected data pertaining to demographics, helmet use, toxicology, bony injury, Injury Severity Score (ISS), Glasgow Coma Scale (GCS), hospital length of stay (LOS), and cost. Continuous variables were compared using t-test or Wilcoxon rank test, depending on data distribution, and dichotomous variables were compared using Pearson’s chi-squared or Fisher’s exact tests. Regression models were used to evaluate the effect of intoxication or helmets on injury location, severity, cost of care, and LOS. Results: The incidence of MCC upper extremity injury was 47.5%, with hand and forearm fractures the most common injuries (25.5% and 24.7% of total injuries). Intoxicated patients were more likely to have a high cost of care (p = 0.012), extended LOS (p = 0.038), plastic surgery involvement in their care (p = 0.038), but fewer upper extremity bony injuries (p = 0.019). Non-helmeted patients sustained less upper extremity bony injuries (p < 0.001) and upper extremity soft tissue injuries (p = 0.001), yet more severe injuries (ISS ≥ 30, p = 0.006 and GCS < 9, p < 0.01) than helmeted patients. Conclusion: Upper extremity injuries are common in motorcyclists. Despite vital protection for the brain and maxillofacial injury, helmeted MCC patients have an increased incidence of upper extremity injuries compared to non-helmeted patients, but overall have less severe injuries. Intoxicated patients have fewer upper extremity bony injuries, but the higher cost of care, and extended LOS. Therefore, even with the increased risk of injury helmets may expose to the upper extremity, helmets reduced overall morbidity and mortality. In addition to mandatory helmet laws, we advocate for further development of safety equipment focusing specifically on the prevention of upper extremity injuries.

Identifying and treating unobserved heterogeneity with FIMIX-PLS

2016 ◽  
Vol 28 (2) ◽  
pp. 208-224 ◽  
Author(s):  
Lucy M. Matthews ◽  
Marko Sarstedt ◽  
Joseph F. Hair ◽  
Christian M. Ringle
Keyword(s):  
Structural Equation ◽  
Latent Class ◽  
Structural Model ◽  
Unobserved Heterogeneity ◽  
Equation Modeling ◽  
Content Type ◽  
Measurement Models ◽  
European Business ◽  
Number Of Segments

Purpose Part I of this article (European Business Review, Volume 28, Issue 1) offered an overview of unobserved heterogeneity in the context of partial least squares structural equation modeling (PLS-SEM), its prevalence and challenges for social sciences researchers. This paper aims to provide an example that explains how to identify and treat unobserved heterogeneity in PLS-SEM by using the finite mixture PLS (FIMIX-PLS) module in the SmartPLS 3 software (Part II). Design/methodology/approach This case study illustrates the application of FIMIX-PLS using a popular corporate reputation model. Findings The case study demonstrates the capability of FIMIX-PLS to identify whether unobserved heterogeneity significantly affects structural model relationships. Furthermore, it shows that FIMIX-PLS is particularly useful for determining the number of segments to extract from the data. Research limitations/implications Since the introduction of FIMIX-PLS, a range of alternative latent class techniques has appeared. These techniques address some of the limitations of the approach relating to, for example, its failure to handle heterogeneity in measurement models, or its distributional assumptions. This research discusses alternative latent class techniques and calls for the joint use of FIMIX-PLS and PLS prediction-oriented segmentation. Originality/value This article is the first to offer researchers, who have not been exposed to the method, an introduction to FIMIX-PLS. Based on a state-of-the-art review of the technique, the paper offers a step-by-step tutorial on how to use FIMIX-PLS by using the SmartPLS 3 software.

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