Crash Severity Analysis for Low-Speed Roads Using Structural Equation Modeling Considering Shoulder- and Pavement-Distress Conditions

Barman, Santanu; Bandyopadhyaya, Ranja

doi:10.1061/jtepbs.0000373

Cited by 12 publications

(3 citation statements)

References 25 publications

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“…In total, they caused 45 deaths, 216 serious injuries, and 520 accidents. It was found like previous research [38]- [40] that curved and straight roads are the most likely to be black spots when considering the road layout, also the same results were obtained by Katre et al [41] who found that the maximum number of accidents occurred on straight roads, slight curves, and sharp curves. Furthermore, the findings revealed that a one-meter increase in shoulder width was linked to an increase in BS severity level.…”

Section: Black Spot Analysissupporting

confidence: 83%

Black spots identification on rural roads based on extreme learning machine

Mbarek

Jiber

Yahyaouy

et al. 2023

IJECE

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<span lang="EN-US">Accident black spots are usually defined as road locations with a high risk of fatal accidents. A thorough analysis of these areas is essential to determine the real causes of mortality due to these accidents and can thus help anticipate the necessary decisions to be made to mitigate their effects. In this context, this study aims to develop a model for the identification, classification and analysis of black spots on roads in Morocco. These areas are first identified using extreme learning machine (ELM) algorithm, and then the infrastructure factors are analyzed by ordinal regression. The XGBoost model is adopted for weighted severity index (WSI) generation, which in turn generates the severity scores to be assigned to individual road segments. The latter are then classified into four classes by using a categorization approach (high, medium, low and safe). Finally, the bagging extreme learning machine is used to classify the severity of road segments according to infrastructures and environmental factors. Simulation results show that the proposed framework accurately and efficiently identified the black spots and outperformed the reputable competing models, especially in terms of accuracy 98.6%. In conclusion, the ordinal analysis revealed that pavement width, road curve type, shoulder width and position were the significant factors contributing to accidents on rural roads.</span>

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Section: Black Spot Analysissupporting

confidence: 83%

Black spots identification on rural roads based on extreme learning machine

Mbarek

Jiber

Yahyaouy

et al. 2023

IJECE

View full text Add to dashboard Cite

show abstract

“… Xie et al (2018) proposed SEMs to jointly model the presence of secondary collisions and crash severity levels and found that the SEM with no constraint did better performance in investigating the contributing factors to secondary collisions. They found that thirteen variables were contributing to the presence of secondary collisions, including alcohol, drugs, inattention, inexperience, sleep, control disregarded, speeding, etc.. Barman and Bandyopadhyaya (2020) used SEMs to assess the direct and latent influence of driver, vehicle, crash along with environmental factors. They found that the SEM model showed a better overall model fit compared to the ordered probit model.…”

Section: Literature Reviewmentioning

confidence: 99%

How did COVID-19 impact driving behaviors and crash Severity? A multigroup structural equation modeling

Dong

Xie

Yang

2022

Accident Analysis & Prevention

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“…Besides the focus on contributing factors related to injury severity at HRGCs, selecting suitable statistical models to determine these factors is also important. Ordered probit [ 15 , 16 ], ordered logit [ 17 , 18 ], multinomial logit [ 19 , 20 ], and mixed logit [ 21 , 22 ] models have been widely adopted to determine injury severity in the past decade. Haleem [ 22 ] compared a mixed logit model with the binary logit model to investigate the determinant factors that result in accidents at private HRGCs.…”

Section: Introductionmentioning

confidence: 99%

Identifying Risk Factors for Autos and Trucks on Highway-Railroad Grade Crossings Based on Mixed Logit Model

Shen

2022

IJERPH

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This study aimed to determine different influencing factors associated with the injury outcomes of heavy vehicle and automobile drivers at highway–rail grade crossings (HRGCs). A mixed logit model was adopted using the Federal Railroad Administration (FRA) dataset (n = 194,385 for 2011–2020). The results show that drivers’ injury severities at HRGCs are enormously different between automobile and truck/truck–trailer drivers. It was found that vehicle speed and train speed significantly affect the injury severity in automobile and truck drivers. Driver characteristics such as gender and driver actions significantly impact the injury severity in automobile drivers, while HRGC attributes such as open space, rural areas, and type of warning device become significant factors in truck models. This study gives us a better understanding of the differences in the types of determinants between automobiles and trucks and their implications on differentiated policies for car and truck drivers.

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Crash Severity Analysis for Low-Speed Roads Using Structural Equation Modeling Considering Shoulder- and Pavement-Distress Conditions

Cited by 12 publications

References 25 publications

Black spots identification on rural roads based on extreme learning machine

Black spots identification on rural roads based on extreme learning machine

How did COVID-19 impact driving behaviors and crash Severity? A multigroup structural equation modeling

Identifying Risk Factors for Autos and Trucks on Highway-Railroad Grade Crossings Based on Mixed Logit Model

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