scholarly journals Effects of Human-Centered Factors on Crash Injury Severities

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Emmanuel Kofi Adanu ◽  
Steven Jones
Keyword(s):  
Logit Model ◽  
Latent Class ◽  
Unobserved Heterogeneity ◽  
Model Fit ◽  
Contributing Factors ◽  
Traffic Crashes ◽  
Fit Statistics ◽  
Mnl Model ◽  
Serious Injury ◽  
Injury Severities

Factors related to drivers and their driving habits dominate the causation of traffic crashes. An in-depth understanding of the human factors that influence risky driving could be of particular importance to facilitate the application of effective countermeasures. This paper sought to investigate effects of human-centered crash contributing factors on crash outcomes. To select the methodology that best accounts for unobserved heterogeneity between crash outcomes, latent class (LC) logit model and random parameters logit (RPL) model were developed. Model estimation results generally show that serious injury crashes were more likely to involve unemployed drivers, no seatbelt use, old drivers, fatigued driving, and drivers with no valid license. Comparison of model fit statistics shows that the LC logit model outperformed the RPL model, as an alternative to the traditional multinomial logit (MNL) model.

scholarly journals Investigation of Contributing Factors to Traffic Crashes and Violations: A Random Parameter Multinomial Logit Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jaeyoung Lee ◽  
Xing Li ◽  
Suyi Mao ◽  
Wen Fu
Keyword(s):  
Logit Model ◽  
Random Parameter ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
Contributing Factors ◽  
Traffic Crashes ◽  
Factors Affecting ◽  
Effective Strategies ◽  
Traffic Violations ◽  
Afternoon Peak

This study investigates contributing factors to traffic violations by seriousness. The traffic violations are divided into four categories by seriousness (unintentional violation, minor violation, serious violation, and crash with violation). The results of the random parameter multinomial logit model indicate that various factors potentially affect the severity of traffic violations. The key findings include the following: (1) female drivers are more likely to commit minor violations; (2) drivers from an area with a longer travel time to work and a higher proportion of driving to work are more likely to have minor violations and serious violations, while those from the high-income area are less likely; (3) drivers are more likely to be associated with a more minor infraction during the afternoon peak (4 p.m.–6 p.m.). The results from this study are expected to be beneficial for policymakers and traffic police to comprehend the factors affecting violations and implement effective strategies to minimize the number and seriousness of traffic violations.

scholarly journals Investigating the Differences of Single-Vehicle and Multivehicle Accident Probability Using Mixed Logit Model

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Bowen Dong ◽  
Xiaoxiang Ma ◽  
Feng Chen ◽  
Suren Chen
Keyword(s):  
Logit Model ◽  
Road Traffic ◽  
Unobserved Heterogeneity ◽  
Mixed Logit ◽  
Weather Condition ◽  
Geometric Feature ◽  
Mixed Logit Model ◽  
Contributing Factors ◽  
Accident Probability ◽  
Single Vehicle

Road traffic accidents are believed to be associated with not only road geometric feature and traffic characteristic, but also weather condition. To address these safety issues, it is of paramount importance to understand how these factors affect the occurrences of the crashes. Existing studies have suggested that the mechanisms of single-vehicle (SV) accidents and multivehicle (MV) accidents can be very different. Few studies were conducted to examine the difference of SV and MV accident probability by addressing unobserved heterogeneity at the same time. To investigate the different contributing factors on SV and MV, a mixed logit model is employed using disaggregated data with the response variable categorized as no accidents, SV accidents, and MV accidents. The results indicate that, in addition to speed gap, length of segment, and wet road surfaces which are significant for both SV and MV accidents, most of other variables are significant only for MV accidents. Traffic, road, and surface characteristics are main influence factors of SV and MV accident possibility. Hourly traffic volume, inside shoulder width, and wet road surface are found to produce statistically significant random parameters. Their effects on the possibility of SV and MV accident vary across different road segments.

Forced Zero Cross-Loading Misspecifications in Measurement Component of Structural Equation Models

Methodology ◽  
2014 ◽  
Vol 10 (4) ◽  
pp. 138-152 ◽  
Author(s):  
Hsien-Yuan Hsu ◽  
Susan Troncoso Skidmore ◽  
Yan Li ◽  
Bruce Thompson
Keyword(s):  
Structural Equation ◽  
Structural Model ◽  
Structural Equation Models ◽  
Equation Model ◽  
Model Fit ◽  
Model Specification ◽  
Simulation Design ◽  
Measurement Models ◽  
Fit Statistics ◽  
Path Coefficients

The purpose of the present paper was to evaluate the effect of constraining near-zero parameter cross-loadings to zero in the measurement component of a structural equation model. A Monte Carlo 3 × 5 × 2 simulation design was conducted (i.e., sample sizes of 200, 600, and 1,000; parameter cross-loadings of 0.07, 0.10, 0.13, 0.16, and 0.19 misspecified to be zero; and parameter path coefficients in the structural model of either 0.50 or 0.70). Results indicated that factor pattern coefficients and factor covariances were overestimated in measurement models when near-zero parameter cross-loadings constrained to zero were higher than 0.13 in the population. Moreover, the path coefficients between factors were misestimated when the near-zero parameter cross-loadings constrained to zero were noteworthy. Our results add to the literature detailing the importance of testing individual model specification decisions, and not simply evaluating omnibus model fit statistics.

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.

Preferences Toward COVID-19 Diagnostic Testing Features: Results From a National Cross-Sectional Survey

2021 ◽  
pp. 089011712110340
Author(s):  
Bhagyashree Katare ◽  
Shuoli Zhao ◽  
Joel Cuffey ◽  
Maria I. Marshall ◽  
Corinne Valdivia
Keyword(s):  
Logit Model ◽  
Nasal Swab ◽  
Latent Class ◽  
Diagnostic Testing ◽  
Mixed Logit ◽  
Monetary Incentive ◽  
Monetary Incentives ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Nationally Representative

Purpose: Describe preferences toward COVID-19 testing features (method, location, hypothetical monetary incentive) and simulate the effect of monetary incentives on willingness to test. Design: Online cross-sectional survey administered in July 2020. Subjects: 1,505 nationally representative U.S. respondents. Measures: Choice of preferred COVID-19 testing options in discrete choice experiment. Options differed by method (nasal-swab, saliva), location (hospital/clinic, drive-through, at-home), and monetary incentive ($0, $10, $20). Analysis: Latent class conditional logit model to classify preferences, mixed logit model to simulate incentive effectiveness. Results: Preferences were categorized into 4 groups: 34% (n = 517) considered testing comfort (saliva versus nasal swab) most important, 27% (n = 408) were willing to trade comfort for monetary incentives, 19% (n = 287) would only test at convenient locations, 20% (n = 293) avoided testing altogether. Relative to no monetary incentives, incentives of $100 increased the percent of testing avoiders (16%) and convenience seekers (70%) that were willing to test. Conclusion: Preferences toward different COVID-19 testing features vary, highlighting the need to match testing features with individuals to monitor the spread of COVID-19.

Are Older Drivers Safe on Interchanges? Analyzing Driving Errors Causing Crashes

2021 ◽  
pp. 036119812110312
Author(s):  
Denis Elia Monyo ◽  
Henrick J. Haule ◽  
Angela E. Kitali ◽  
Thobias Sando
Keyword(s):  
Latent Class ◽  
Time Of Day ◽  
Older Drivers ◽  
Contributing Factors ◽  
Lighting Condition ◽  
Crash Data ◽  
Traffic Characteristics ◽  
Road Users ◽  
Traffic Conflicts ◽  
Penalized Logistic Regression

Older drivers are prone to driving errors that can lead to crashes. The risk of older drivers making errors increases in locations with complex roadway features and higher traffic conflicts. Interchanges are freeway locations with more driving challenges than other basic segments. Because of the growing population of older drivers, it is vital to understand driving errors that can lead to crashes on interchanges. This knowledge can assist in developing countermeasures that will ensure safety for all road users when navigating through interchanges. The goal of this study was to determine driver, environmental, roadway, and traffic characteristics that influence older drivers’ errors resulting in crashes along interchanges. The analysis was based on three years (2016–2018) of crash data from Florida. A two-step approach involving a latent class clustering analysis and the penalized logistic regression was used to investigate factors that influence driving errors made by older drivers on interchanges. This approach accounted for heterogeneity that exists in the crash data and enhanced the identification of contributing factors. The results revealed patterns that are not obvious without a two-step approach, including variables that were not significant in all crashes, but were significant in specific clusters. These factors included driver gender and interchange type. Results also showed that all other factors, including distracted driving, lighting condition, area type, speed limit, time of day, and horizontal alignment, were significant in all crashes and few specific clusters.

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