Mixed Effects Logistic Model to Address Demographics and Neighborhood Environment on Pedestrian Injury Severity

2017 ◽  
Vol 2659 (1) ◽  
pp. 174-181 ◽  
Author(s):  
Rui Guo ◽  
Chunfu Xin ◽  
Pei-Sung Lin ◽  
Achilleas Kourtellis
Keyword(s):  
Low Income ◽  
Logistic Model ◽  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
Pedestrian Safety ◽  
Mixed Effects ◽  
Neighborhood Environment ◽  
Severe Injury ◽  
Pedestrian Injury ◽  
Unsafe Behaviors

This paper examines the effects of demographics and neighborhood environment on pedestrian injury severity to inform proactive countermeasures for improving pedestrian safety. A mixed effects logistic model addressing unobserved heterogeneity was developed from 3,948 pedestrian-involved crashes that occurred in Florida from 2011 to 2014. Six normally distributed random parameters were identified to reflect random effects on the pedestrian injury severity. The heterogeneity of two demographic factors (older and male pedestrians) suggested the need for more customized education programs to improve pedestrian safety awareness and knowledge, especially for older pedestrians. Relative to low-income areas, 67.7% of pedestrians involved in crashes in higher-income areas were less likely to sustain severe injury. Analysis of sample data also indicated that low-income areas tended to have had more unsafe behaviors by pedestrians related to higher injury severity (e.g., crossing at dark in unlighted areas). Higher-income areas tended to have had more unsafe behaviors by drivers related to higher injury severity (e.g., distracted driving). Other significant factors included lighting conditions (daylight, darkness without lighting), speed limit, alcohol or drug impairment, dart or dash behavior, crossing indicator, and traffic control device indicator. Regarding neighborhood land use types, two indicators about the presence of bus stops and department stores or supermarkets nearby were significant, and their effects were also random. Further investigations are needed to identify systematically the need for effective countermeasures in severe injury crash clusters in the future.

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.

Insights from Integrated Geo-Location Data for Pedestrian Crashes, Demographics, and Land Uses

2020 ◽  
Vol 2674 (8) ◽  
pp. 720-731
Author(s):  
Rui Guo ◽  
Zhiqiang Wu ◽  
Yu Zhang ◽  
Pei-Sung Lin ◽  
Zhenyu Wang
Keyword(s):  
Low Income ◽  
Fast Food ◽  
Negative Binomial ◽  
Unobserved Heterogeneity ◽  
Pedestrian Safety ◽  
Marginal Effect ◽  
Land Uses ◽  
Crash Frequency ◽  
Location Data ◽  
Pedestrian Crashes

This study investigates the effects of demographics and land uses on pedestrian crash frequency by integrating the contextual geo-location data. To address the issue of heterogeneity, three negative binomial models (with fixed parameters, with observed heterogeneity, and with both observed and unobserved heterogeneities) were examined. The best fit with the data was obtained by explicitly incorporating the observed and unobserved heterogeneity into the model. This highlights the need to accommodate both observed heterogeneity across neighborhood characteristics and unobserved heterogeneity in pedestrian crash frequency modeling. The marginal effect results imply that some land-use types (e.g., discount department stores and fast-food restaurants) could be candidate locations for the education campaigns to improve pedestrian safety. The observed heterogeneity of the area indicator suggests that priority should be given to more populated low-income areas for pedestrian safety, but attention is also needed for the higher-income areas with larger densities of bus stops and hotels. Moreover, three normally distributed random parameters (proportion of older adults, proportion of lower-speed roads, and density of convenience stores in the area) were identified as having random effects on the probability of pedestrian crash occurrences. Finally, the identification of pedestrian crash hot zone provides practitioners with prioritized neighborhoods (e.g., a list of areas) for developing effective pedestrian safety countermeasures.

scholarly journals Effects Influencing Pedestrian–Vehicle Crash Frequency by Severity Level: A Case Study of Seoul Metropolitan City, South Korea

Safety ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. 25
Author(s):  
Seung-Hoon Park ◽  
Min-Kyung Bae
Keyword(s):  
Built Environment ◽  
Traffic Safety ◽  
Injury Severity ◽  
Small Sample Size ◽  
Pedestrian Safety ◽  
Small Sample ◽  
Traffic Characteristics ◽  
Effective Interventions ◽  
Pedestrian Injury ◽  
Vehicular Crashes

This study aimed to determine how built environments affect pedestrian–vehicle collisions. The study examined pedestrian–vehicular crashes that occurred between 2013 and 2015 in Seoul, Korea, by comparing and analyzing different effects of the built environment on pedestrian–vehicle crashes. Specifically, the study analyzed built environment attributes, land use environment, housing types, road environment, and traffic characteristics to determine how these factors affect the severity of pedestrian injury. The results of the statistical analysis appear to infer that the built environment attributes had dissimilar impacts on pedestrian collisions, depending on the injury severity. In general, both incapacitating and non-incapacitating injuries appear to be more likely to be caused by the built environment than fatal and possible injuries. These results highlight the need to consider injury severity when implementing more effective interventions and strategies for ensuring pedestrian safety. However, because of the small sample size, an expanded research project regarding this issue should be considered, as it would contribute to the development and implementation of effective policies and interventions for pedestrian safety in Korea. This study therefore offers practical information regarding the development of such an expanded study to inform future traffic safety policies in Seoul to establish a “safe walking city.”

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.

Age and pedestrian injury severity in motor-vehicle crashes: A heteroskedastic logit analysis

2008 ◽  
Vol 40 (5) ◽  
pp. 1695-1702 ◽  
Author(s):  
Joon-Ki Kim ◽  
Gudmundur F. Ulfarsson ◽  
Venkataraman N. Shankar ◽  
Sungyop Kim
Keyword(s):  
Injury Severity ◽  
Motor Vehicle ◽  
Motor Vehicle Crashes ◽  
Logit Analysis ◽  
Vehicle Crashes ◽  
Pedestrian Injury

scholarly journals Analysis of Severe Injury Accident Rates on Interstate Highways Using a Random Parameter Tobit Model

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Minho Park ◽  
Dongmin Lee
Keyword(s):  
Regression Model ◽  
Unobserved Heterogeneity ◽  
Random Parameter ◽  
Tobit Model ◽  
Severe Injury ◽  
Parameter Estimates ◽  
Random Parameters ◽  
Tobit Regression ◽  
Accident Rates ◽  
Tobit Regression Model

In this study, a random parameter Tobit regression model approach was used to account for the distinct censoring problem and unobserved heterogeneity in accident data. We used accident rate data (continuous data) instead of accident frequency data (discrete count data) to address the zero cell problems from data where roadway segments do not have any recorded accidents over the observed time period. The unobserved heterogeneity problem is also considered by using random parameters, which are parameter estimates that vary across observations instead of fixed parameters, which are parameter estimates that are fixed/constant over observations. Nine years (1999–2007) of panel data related to severe injury accidents in Washington State, USA, were used to develop the random parameter Tobit model. The results showed that the Tobit regression model with random parameters is a better approach to explore factors influencing severe injury accident rates on roadway segments under consideration of unobserved heterogeneity problems.

The association between HIV/AIDS and food insecurity at the US–Mexico border: Experiences of low-income patients in the Rio Grande Valley

2020 ◽  
pp. 095646242093060
Author(s):  
Jennifer Tabler ◽  
Laryssa Mykyta ◽  
Jason M Nagata
Keyword(s):  
Food Insecurity ◽  
Low Income ◽  
Rio Grande Valley ◽  
Rio Grande ◽  
Neighborhood Environment ◽  
Mexico Border ◽  
Aids Diagnosis ◽  
Living With Hiv ◽  
Us Mexico Border ◽  
Hiv Aids

US–Mexico border communities are uniquely vulnerable to human immunodeficiency virus (HIV) transmission given the economic and social challenges these communities face. We surveyed low-income, predominantly Latinx residents receiving sexually transmitted infection testing and/or HIV/acquired immune deficiency syndrome (AIDS) care in the lower Rio Grande Valley of southernmost Texas about their experiences of food insecurity. Participants aged 18 years and over took a self-administered survey available in English or Spanish in a clinic waiting room ( N = 251). Ordinary least squares regression results suggested that those with a prior HIV/AIDS diagnosis reported a response for food insecurity that was approximately 0.67 points higher than peers without a prior HIV/AIDS diagnosis (coefficient = 0.67; p < 0.05), even when adjusting for sociodemographic characteristics, social support, perceived discrimination, and neighborhood environment. Interaction results between age and HIV status indicated that younger individuals living with HIV/AIDS experienced uniquely higher food insecurity; those who reported a prior HIV/AIDS diagnosis experienced an additional reduction in food insecurity by approximately 0.06 points for each additional year of age (age × HIV/AIDS interaction coefficient = −0.06; p < 0.05). Community programs serving low-income populations should consider screening for and intervening on food insecurity, especially among young adults living with HIV/AIDS.

Sign in / Sign up

Export Citation Format

Share Document