Examining driver injury severity in two vehicle crashes – A copula based approach

Yasmin, Shamsunnahar; Eluru, Naveen; Pinjari, Abdul Rawoof; Tay, Richard

doi:10.1016/j.aap.2014.01.018

Cited by 94 publications

(36 citation statements)

References 30 publications

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“…Some recent studies have comprehensively summarized different families of bivariate copula models [47][48][49], which are provided in Table 2. Some studies also applied different families of copula in transportation data analysis [50][51][52][53]. The commonly used families of bivariate copulas are summarized in Table 2.…”

Section: Linking the Wildlife-vehicle Collision Model And The Underrementioning

confidence: 99%

A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis

et al. 2019

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Wildlife‒vehicle collision (WVC) data usually contain two types: the reported WVC data and carcass removal data. Previous studies often found a discrepancy between the number of reported WVC and carcass removal data, and the quality of both datasets is affected by underreporting. Underreporting means the number of WVCs is not fully recorded in the database; neglecting the underreporting in WVC data may result in biased parameter estimation results. In this study, a copula regression model linking wildlife‒vehicle collisions and the underreporting outcome was proposed to consider the underreporting in WVC data. The WVC data collected from 10 highways in Washington State were analyzed using the copula regression model and the Negative Binomial (NB) model. The main findings from this study are as follows: (1) the Gaussian copula model can provide different modeling results when compared with the conventional modeling approach; (2) the hotspot identification results indicate that the Gaussian copula-based Empirical Bayes (EB) method can more accurately identify hotspots than the NB-based EB method. Thus, the proposed copula model may be a better alternative to the conventional NB model for modeling underreported WVC data.

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Section: Linking the Wildlife-vehicle Collision Model And The Underrementioning

confidence: 99%

A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis

et al. 2019

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“…A few studies have examined injury severity of multiple occupants jointly. Yasmin et al (17) and Abay et al (18) modeled the injury severity of the two drivers involved in a two-vehicle crash, while Eluru et al (19) modeled the injury severity of all occupants. They found correlated unobserved factors in injury outcomes across occupants and recommend that crash studies adopt approaches in which injury outcomes are modeled simultaneously across all vehicle occupants.…”

Section: Modeling Injury Severitymentioning

confidence: 99%

Introducing Latent Psychological Constructs in Injury Severity Modeling: Multivehicle and Multioccupant Approach

Lavieri

Bhat

Pendyala

et al. 2016

Transportation Research Record: Journal of the Transportation R

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This paper presents a comprehensive model of injury severity that accounts for unmeasured driver behavior attributes. The results of the model have important implications for the design of safety interventions and advanced vehicular features and technologies. Engineering designs that accommodate the diminished capabilities of older drivers, include rear seat safety features, and alert drivers to frontal collisions before they occur (collision warning systems and automated braking systems) would contribute to substantial reductions in injury severity for various vehicular occupants.

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“…Road tra c injuries are one of the main causes of death in the world [1,2]. Every year 1.24 million.…”

Section: Introductionmentioning

confidence: 99%

The Role of Journey Purpose in Road Traffic Injuries: A Bayesian Network Approach

Febres

Mohamadi

Saldaña

et al. 2019

Journal of Advanced Transportation

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Introduction. Road traffic injuries are now regarded as the eighth leading cause of death globally. For example, in 2016, 102,362 traffic injuries took place in Spain in which 174,679 drivers suffered injuries. These findings necessitated the development of the current study which focuses on the prime factors that cause this type of injuries. The aim of this study, therefore, is to explore the behavioral factors that entail a higher risk of suffering either a serious or a fatal injury for drivers. Methods. The findings are based on information and data provided by “Dirección General de Tráfico” (DGT) in Spain on traffic injuries that occurred in the year 2016. Reviewing a wide range of the literature, the authors identified the most influential variables and created a model using the Bayesian networks. The variables that define the model are grouped into four factors: vehicle factor, road factor, circumstantial factor and human factor. Results. The results suggest that the principal variables that determine a higher probability of serious or fatal injuries in traffic injuries are: lack of using appropriate safety accessories, high-speed violations, distractions as well as errors. Finally, the research shows the severity probability based on reason of displacement (“in itinere,” on business, or in leisure).

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Examining driver injury severity in two vehicle crashes – A copula based approach

Cited by 94 publications

References 30 publications

A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis

A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis

Introducing Latent Psychological Constructs in Injury Severity Modeling: Multivehicle and Multioccupant Approach

The Role of Journey Purpose in Road Traffic Injuries: A Bayesian Network Approach

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