A statistical assessment of temporal instability in the factors determining motorcyclist injury severities

Alnawmasi, Nawaf; Mannering, Fred

doi:10.1016/j.amar.2019.100090

Cited by 129 publications

(74 citation statements)

References 52 publications

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“…Based on local policies on crash data disclosure in Guizhou Province, only the data from 2018 was adopted in this study. Future works could benefit from applying multi-year data and study the temporal effects for CC severity [ 23 ]. Besides, the temporal threshold for the identification of CCs in this study is subject to minimum time interval (1 min) between two crash records in the database.…”

Section: Discussionmentioning

confidence: 99%

“…Unobserved heterogeneity has been shown to widely exist in road crash modeling [ 20 , 21 , 23 , 24 , 25 , 26 , 27 ], as factors that were not accounted for by the independent variables may induce individual heterogenous effects on the outcome variable [ 26 ]. To address the unobserved heterogeneity possibly existing in our dataset, a RE modeling approach was adopted by incorporating a random intercept term,

, which was normally distributed across individual observations with a mean of 0 and a standard deviation of

.…”

Section: Methodsmentioning

confidence: 99%

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Influential Factors Associated with Consecutive Crash Severity: A Two-Level Logistic Modeling Approach

Meng

Song

et al. 2020

IJERPH

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A consecutive crash series is composed by a primary crash and one or more subsequent secondary crashes that occur immediately within a certain distance. The crash mechanism of a consecutive crash series is distinctive, as it is different from common primary and secondary crashes mainly caused by queuing effects and chain-reaction crashes that involve multiple collisions in one crash. It commonly affects a large area of road space and possibly causes congestions and significant delays in evacuation and clearance. This study identified the influential factors determining the severity of primary and secondary crashes in a consecutive crash series. Basic, random-effects, random-parameters, and two-level binary logistic regression models were established based on crash data collected on the freeway network of Guizhou Province, China in 2018, of which 349 were identified as consecutive crashes. According to the model performance metrics, the two-level logistic model outperformed the other three models. On the crash level, double-vehicle primary crash had a negative association with the severity of secondary consecutive crashes, and the involvement of trucks in the secondary consecutive crash had a positive contribution to its crash severity. On a road segment level, speed limit, traffic volume, tunnel, and extreme weather conditions such as rainy and cloudy days had positive effects on consecutive crash severity, while the number of lanes was negatively associated with consecutive crash severity. Policy suggestions are made to alleviate the severity of consecutive crashes by reminding the drivers with real-time potential hazards of severe consecutive crashes and providing educative programs to specific groups of drivers.

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Section: Discussionmentioning

confidence: 99%

, which was normally distributed across individual observations with a mean of 0 and a standard deviation of

.…”

Section: Methodsmentioning

confidence: 99%

Influential Factors Associated with Consecutive Crash Severity: A Two-Level Logistic Modeling Approach

Meng

Song

et al. 2020

IJERPH

View full text Add to dashboard Cite

show abstract

“…To account for the possibility of unobserved heterogeneity in the means and variances of parameters, let β in be a vector of estimable parameters that varies across crashes defined as (a similar formulation used by ( 28 , 29 , 32 – 34 ) in other injury-severity contexts)…”

Section: Methodological Backgroundmentioning

confidence: 99%

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

Islam

Pande

2020

Transportation Research Record

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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.

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“…The most commonly used method to investigate the injury severity under various conditions is mixed logit model [6][7][8][9][10][11][12][13][14][15][16][17], which can analyze the relationship between injury severity and the influencing factors as well as addressing the heterogeneity issue appropriately; Anastasopoulos and Mannering [18] provided a comparison of fixed and random parameter logit models using two types of injury severity data. The results showed that random parameter logit model was superior to the fixed-parameter one, and the models based on individual crash data provided better overall fit relative to the models based on the proportion of crashes by severity type; then Xie et al [19] extended the injury severity analysis to Bayesian binary logit model with random effects, but some of the findings were counterintuitive, and comparison with mixed logit model or random parameter model is recommended [20][21][22][23] so as to investigate real-time data more effectively.…”

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Analysis of Injury Severity with Geographically Weighted Panel Logistic Regression Model

Xiao

Duan

2019

Journal of Advanced Transportation

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This study is intended to investigate the influencing factors of injury severity by considering the heterogeneity issue of unobserved factors at different arterials and the spatial attributes in geographically weighted regression models. To achieve the objectives, geographically weighted panel logistic regression model was developed, in which the geographically weighted logistic regression model addressed the injury severity from the spatial perspective, while the panel data model accommodated the heterogeneity attributed to unobserved factors from the temporal perspective. The geo-crash data of Las Vegas metropolitan area from 2014 to 2016 was collected, involving 27 arterials with 25,029 injury samples. By comparing the conventional logistic regression model and geographically weighted logistic regression models, the geographically weighted panel logistic regression model showed preference to the other models. Results revealed that four main factors, human-beings (drivers/pedestrians/cyclists), vehicles, roadway, and environment, were potentially significant factors of increasing the injury severity. The findings provide useful insights for practitioners and policy makers to improve safety along arterials.

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A statistical assessment of temporal instability in the factors determining motorcyclist injury severities

Cited by 129 publications

References 52 publications

Influential Factors Associated with Consecutive Crash Severity: A Two-Level Logistic Modeling Approach

Influential Factors Associated with Consecutive Crash Severity: A Two-Level Logistic Modeling Approach

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

Spatial-Temporal Analysis of Injury Severity with Geographically Weighted Panel Logistic Regression Model

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