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 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.

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.

Pedestrian Injury Severity vs. Vehicle Impact Speed: Uncertainty Quantification and Calibration to Local Conditions

2019 ◽  
Vol 2673 (11) ◽  
pp. 583-592 ◽  
Author(s):  
Gary A. Davis ◽  
Christopher Cheong
Keyword(s):  
Uncertainty Quantification ◽  
Injury Severity ◽  
Likelihood Estimation ◽  
Monte Carlo Sampling ◽  
National Sample ◽  
Local Conditions ◽  
Impact Speed ◽  
Crash Data ◽  
Vehicle Impact ◽  
Pedestrian Crashes

This paper describes a method for fitting predictive models that relate vehicle impact speeds to pedestrian injuries, in which results from a national sample are calibrated to reflect local injury statistics. Three methodological issues identified in the literature, outcome-based sampling, uncertainty regarding estimated impact speeds, and uncertainty quantification, are addressed by (i) implementing Bayesian inference using Markov Chain Monte Carlo sampling and (ii) applying multiple imputation to conditional maximum likelihood estimation. The methods are illustrated using crash data from the NHTSA Pedestrian Crash Data Study coupled with an exogenous sample of pedestrian crashes from Minnesota’s Twin Cities. The two approaches produced similar results and, given a reliable characterization of impact speed uncertainty, either approach can be applied in a jurisdiction having an exogenous sample of pedestrian crash severities.

scholarly journals Spatial Equity Analysis of Nighttime Pedestrian Safety: Role of Land Use and Alcohol Establishments in Albuquerque, NM

2021 ◽  
pp. 036119812110302
Author(s):  
Benson Long ◽  
Nicholas N. Ferenchak
Keyword(s):  
Land Use ◽  
Past Research ◽  
The United States ◽  
Sociodemographic Factors ◽  
Severe Injury ◽  
Severe Injuries ◽  
Lighting Condition ◽  
Crash Data ◽  
Pedestrian Crashes ◽  
Pedestrian Collisions

The United States experienced a 53% increase in pedestrian fatalities between 2009 and 2018, with 2018 having a 3.4% increase from 2017. Of the 2018 pedestrian fatalities with known lighting conditions, 76% occurred in dark/nighttime conditions, with 50% occurring between 6:00 and 11:59 p.m. Despite past research exploring several contributing characteristics for nighttime pedestrian crashes, there is limited research that investigates the spatial aspects of land use attributes and sociodemographic factors. Have these nighttime pedestrian collisions been concentrated in certain land uses? Could an establishment with the capacity to serve alcohol invoke a greater risk of pedestrian crashes? Does sociodemographic status correlate with clustering for fatal crashes, severe crashes, or both? To better understand the spatial characteristics of the recent increase in pedestrian collisions, we analyzed crash data from Albuquerque, New Mexico for pedestrian fatalities and severe injuries from 2013 to 2018 relative to lighting condition, land use (with a focus on alcohol establishments), and race/ethnicity on the block group level. We used confidence intervals and Getis-Ord Gi* statistics to verify the statistical integrity of the trends. Findings suggested that pedestrian fatality and severe injury rates were higher within a quarter mile of bars at night and in areas with elevated concentrations of minority populations. Pedestrian fatality and severe injury hot spots appeared to have higher percentages of non-white residents, coupled with lower sidewalk coverage and more arterials or collectors.

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 A Multilevel Model Approach for Investigating Individual Accident Characteristics and Neighborhood Environment Characteristics Affecting Pedestrian-Vehicle Crashes

2020 ◽  
Vol 17 (9) ◽  
pp. 3107 ◽  
Author(s):  
Seunghoon Park ◽  
Dongwon Ko
Keyword(s):  
South Korea ◽  
Multilevel Model ◽  
Injury Severity ◽  
Traffic Signals ◽  
Individual Characteristics ◽  
Neighborhood Environment ◽  
Vehicle Crashes ◽  
Pedestrian Injuries ◽  
Pedestrian Crashes ◽  
The Individual

Walking is the most basic movement of humans and the most fundamental mode of transportation. To promote walking, it is necessary to create a safe environment for pedestrians. However, pedestrian-vehicle crashes still remain relatively high in South Korea. This study employs a multilevel model to examine the differences between the lower-level individual characteristics of pedestrian crashes and the upper-level neighborhood environmental characteristics in Seoul, South Korea. The main results of this study are as follows. The individual characteristics of pedestrian-vehicle crashes are better at explaining pedestrian injury severity than built environment characteristics at the neighborhood level. Older pedestrians and drivers suffer more severe pedestrian injuries. Larger vehicles such as trucks and vans are more likely to result in a high severity of pedestrian injuries. Pedestrian injuries increase during inclement weather and at night. The severity of pedestrian injuries is lower at intersections and crosswalks without traffic signals than at crosswalks and intersections with traffic signals. Finally, school zones and silver zones, which are representative policies for pedestrian safety in South Korea, fail to play a significant role in reducing the severity of pedestrian injuries. The results of this study can guide policymakers and planners when making decisions on how to build neighborhoods that are safer for pedestrians.

scholarly journals Hierarchical ordered model for injury severity of pedestrian crashes in South Korea

2017 ◽  
Vol 61 ◽  
pp. 33-40 ◽  
Author(s):  
Myeonghyeon Kim ◽  
Seung-Young Kho ◽  
Dong-Kyu Kim
Keyword(s):  
South Korea ◽  
Injury Severity ◽  
Pedestrian Crashes

scholarly journals Morning Peak-Period Pricing Surcharge of Elderly Passengers Taking Express Buses

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Jingxu Chen ◽  
Chengxin He ◽  
Xinlian Yu ◽  
Wendong Chen
Keyword(s):  
Choice Behavior ◽  
Solution Method ◽  
The Elderly ◽  
Trial And Error ◽  
Peak Period ◽  
Morning Peak ◽  
Level Model ◽  
Upper Level ◽  
Passenger Assignment ◽  
Special Case

This study deals with the elderly fare pricing issue for taking express buses in the morning peak period. As many elderly passengers are not commuters, fare discount policy may not be an opportune option when buses get overcrowded. Imposing surcharge on the elderly becomes a potentially beneficial measure that encourages an appropriate number of elderly passengers to circumvent the most crowded buses. The elderly pricing surcharge problem is formulated as a bilevel model, in which the upper-level model is to make the pricing surcharge decision, and the lower-level model is the equilibrium passenger assignment that represents passengers’ bus choice behavior. It is classified into the special case and the generic case depending on the number of buses that impose surcharge. Several useful properties of two cases are analyzed, and a trial-and-error solution method is later developed to solve these two cases. Numerical experiments show that the elderly pricing surcharge scheme is not always applicable to all the demand scenarios, which owns a certain effective interval.

scholarly journals Investigating the Risk Factors Associated with the Severity of the Pedestrians Injured on Spanish Crosstown Roads

2019 ◽  
Vol 11 (19) ◽  
pp. 5194 ◽  
Author(s):  
Natalia Casado-Sanz ◽  
Begoña Guirao ◽  
Antonio Lara Galera ◽  
Maria Attard
Keyword(s):  
Risk Factors ◽  
Injury Severity ◽  
Motor Vehicle ◽  
Weather Conditions ◽  
Crash Data ◽  
Factors Associated ◽  
The Road ◽  
Pedestrian Injury ◽  
On The Road ◽  
Pedestrian Crashes

According to the Spanish General Traffic Accident Directorate, in 2017 a total of 351 pedestrians were killed, and 14,322 pedestrians were injured in motor vehicle crashes in Spain. However, very few studies have been conducted in order to analyse the main factors that contribute to pedestrian injury severity. This study analyses the accidents that involve a single vehicle and a single pedestrian on Spanish crosstown roads from 2006 to 2016 (1535 crashes). The factors that explain these accidents include infractions committed by the pedestrian and the driver, crash profiles, and infrastructure characteristics. As a preliminary tool for the segmentation of 1535 pedestrian crashes, a k-means cluster analysis was applied. In addition, multinomial logit (MNL) models were used for analysing crash data, where possible outcomes were fatalities and severe and minor injured pedestrians. According to the results of these models, the risk factors associated with pedestrian injury severity are as follows: visibility restricted by weather conditions or glare, infractions committed by the pedestrian (such as not using crossings, crossing unlawfully, or walking on the road), infractions committed by the driver (such as distracted driving and not respecting a light or a crossing), and finally, speed infractions committed by drivers (such as inadequate speed). This study proposes the specific safety countermeasures that in turn will improve overall road safety in this particular type of road.

Epidemiological profile and outcomes of snakebite injuries treated in emergency departments in South Korea, 2011–2016: a descriptive study

2019 ◽  
Vol 113 (10) ◽  
pp. 590-598
Author(s):  
Mohd Zaki Fadzil Senek ◽  
So Yeon Kong ◽  
Sang Do Shin ◽  
Kyong Min Sun ◽  
Jungeun Kim ◽  
...  
Keyword(s):  
South Korea ◽  
Emergency Departments ◽  
Injury Severity ◽  
Global Public Health ◽  
Severe Injuries ◽  
Essential Information ◽  
Health Crisis ◽  
Mortality Ratio ◽  
Epidemiological Profile ◽  
And Control

Abstract Background Snakebite is a global public health crisis, but there are no nationwide data on snakebite in South Korea. The aim of this study was to describe the epidemiological profile and outcomes of snakebite cases in South Korea seasonally. Methods The selected subjects were patients of all ages with a chief complaint of snakebite who presented to participating emergency departments (EDs) between 1 January 2011 and 31 December 2016. Results A total of 1335 patients were eligible for the study. There were an average of 223 snakebite cases reported each year. Most snakebites occurred during the summer months (55.9%) in patients aged 40–59 y (36.3%) and males (61.5%). Snakebites occurred most frequently on Mondays (22.9%) between 12:00 and 17:59 h (42.0%) outdoors (57.9%) and in farm areas (20.7%). Over 82% of the bites were by venomous snakes across all seasons, and 66% of the patients visited EDs without using emergency medical services. Based on the excess mortality ratio-adjusted injury severity score, 88, 9.2 and 2.8% had mild, moderate and severe injuries, respectively. There were 10 fatalities during the study period. Conclusion This study provides essential information to understand and assess the burden and distribution of snakebites in South Korea and provides valuable information for developing appropriate prevention and control interventions to address it.

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