Analyzing single-vehicle and multi-vehicle freeway crashes with unobserved heterogeneity

2021 ◽  
pp. 1-23
Author(s):  
Mingjie Feng ◽  
Xuesong Wang ◽  
Yan Li
Keyword(s):  
Unobserved Heterogeneity ◽  
Single Vehicle

Analysis of Single-Vehicle Roadway Departure Crashes on Rural Curved Segments Accounting for Unobserved Heterogeneity

2020 ◽  
Vol 2674 (10) ◽  
pp. 146-157
Author(s):  
Mouyid Islam ◽  
Anurag Pande
Keyword(s):  
Environmental Conditions ◽  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
Safety Information ◽  
Mixed Logit ◽  
Highway Safety ◽  
Improvement Program ◽  
Cloudy Weather ◽  
Adverse Weather ◽  
Single Vehicle

Roadway departure crashes are one of the core emphasis areas in Strategic Highway Safety Plans (SHSP). These crashes, especially on rural roads, lead to a disproportionately higher number of fatalities and serious injuries. The focus of this study is to identify and quantify the factors affecting injury-severity outcomes for single-vehicle roadway departure (SV-RwD) crashes on rural curved segments in Minnesota. The crash data are extracted from the Highway Safety Information System (HSIS) from 2010 to 2014. This study applied a mixed logit with heterogeneity in means and variances approach to model driver-injury severity. The approach accounts for possible unobserved heterogeneity in the data resulting from driver, roadway, traffic, environmental conditions, or any combination of these attributes. This analysis adds value to the growing body of literature because it uncovers some unobserved heterogeneity in the form the attributes specific to driver-injury severities in contrast to the standard mixed logit approach. The model results indicate that there is a complex interaction of driver characteristics and actions (male drivers, aged below 30 years of age, and unsafe speed), roadway and traffic characteristics (two-lane undivided road, county roadways, and low traffic volume), environmental conditions (adverse weather, cloudy weather, dark conditions, and dry surface conditions), and vehicle characteristics (vehicle type—sport utility vehicle involved in rollover crashes). The results also provide some evidence of the effectiveness of a highway curve safety improvement program implemented in one of the Minnesota Department of Transportation (DOT) districts.

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.

scholarly journals Investigating Rural Single-Vehicle Crash Severity by Vehicle Types Using Full Bayesian Spatial Random Parameters Logit Model

2021 ◽  
Vol 11 (17) ◽  
pp. 7819
Author(s):  
Fulu Wei ◽  
Zhenggan Cai ◽  
Zhenyu Wang ◽  
Yongqing Guo ◽  
Xin Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
Spatial Correlation ◽  
Traffic Safety ◽  
Logit Model ◽  
Unobserved Heterogeneity ◽  
Crash Severity ◽  
Random Parameters ◽  
Crash Data ◽  
Random Parameters Logit ◽  
Single Vehicle

The effect of risk factors on crash severity varies across vehicle types. The objective of this study was to explore the risk factors associated with the severity of rural single-vehicle (SV) crashes. Four vehicle types including passenger car, motorcycle, pickup, and truck were considered. To synthetically accommodate unobserved heterogeneity and spatial correlation in crash data, a novel Bayesian spatial random parameters logit (SRP-logit) model is proposed. Rural SV crash data in Shandong Province were extracted to calibrate the model. Three traditional logit approaches—multinomial logit model, random parameter logit model, and random intercept logit model—were also established and compared with the proposed model. The results indicated that the SRP-logit model exhibits the best fit performance compared with other models, highlighting that simultaneously accommodating unobserved heterogeneity and spatial correlation is a promising modeling approach. Further, there is a significant positive correlation between weekend, dark (without street lighting) conditions, and collision with fixed object and severe crashes and a significant negative correlation between collision with pedestrians and severe crashes. The findings can provide valuable information for policy makers to improve traffic safety performance in rural areas.

University Dropout

2008 ◽  
Vol 29 (3) ◽  
pp. 134-147 ◽  
Author(s):  
Manuel C. Voelkle ◽  
Nicolas Sander
Keyword(s):  
Grade Point Average ◽  
Structural Equation ◽  
Unobserved Heterogeneity ◽  
School Grade ◽  
Grade Point ◽  
High School Grade ◽  
Hands On ◽  
University Dropout ◽  
The Individual ◽  
A Prospective Study

University dropout is a politically and economically important factor. While a number of studies address this issue cross-sectionally by analyzing different cohorts, or retrospectively via questionnaires, few of them are truly longitudinal and focus on the individual as the unit of interest. In contrast to these studies, an individual differences perspective is adopted in the present paper. For this purpose, a hands-on introduction to a recently proposed structural equation (SEM) approach to discrete-time survival analysis is provided ( Muthén & Masyn, 2005 ). In a next step, a prospective study with N = 1096 students, observed across four semesters, is introduced. As expected, average university grade proved to be an important predictor of future dropout, while high-school grade-point average (GPA) yielded no incremental predictive validity but was completely mediated by university grade. Accounting for unobserved heterogeneity, three latent classes could be identified with differential predictor-criterion relations, suggesting the need to pay closer attention to the composition of the student population.

Migration and the Internet

2017 ◽  
Vol 14 (3) ◽  
pp. 331-342 ◽  
Author(s):  
Thomas John Cooke ◽  
Ian Shuttleworth
Keyword(s):  
United States ◽  
Information And Communication Technologies ◽  
Unobserved Heterogeneity ◽  
Panel Study ◽  
The United States ◽  
Negative Effect ◽  
Information And Communication ◽  
Using Data ◽  
And Migration ◽  
Dynamics Models

It is widely presumed that information and communication technologies, or ICTs, enable migration in several ways; primarily by reducing the costs of migration. However, a reconsideration of the relationship between ICTs and migration suggests that ICTs may just as well hinder migration; primarily by reducing the costs of not moving.  Using data from the US Panel Study of Income Dynamics, models that control for sources of observed and unobserved heterogeneity indicate a strong negative effect of ICT use on inter-state migration within the United States. These results help to explain the long-term decline in internal migration within the United States.

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