Severity analysis for large truck rollover crashes using a random parameter ordered logit model

Azimi, Ghazaleh; Rahimi, Alireza; Asgari, Hamidreza; Jin, Xia

doi:10.1016/j.aap.2019.105355

Cited by 141 publications

(68 citation statements)

References 51 publications

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“…The research methods of the influence of road factors on vehicle rollover are mostly concentrated on accident-prone incidents [16,[18][19][20][21][22][23][24]. It is difficult to determine the underlying causes behind the formation of black spots, and the results are not intuitive.…”

Section: Introductionmentioning

confidence: 99%

The Influence of Road Geometry on Vehicle Rollover and Skidding

Yin

Wen

Sun

et al. 2020

IJERPH

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This paper analyzes the influence of single and combined unfavorable road geometry on rollover and skidding risks of D-class mid-sized sport utility vehicles (SUVs) with front-wheel drive for roads with design speeds at 80 km/h. A closed-loop simulation model of human-vehicle-road interactions is established to examine the systematic influence of road geometry on vehicle rollover and skidding. The effects of different road geometry on rollover and skidding on SUVs are studied for pavement surface with good and poor friction when vehicles are in the action of steady state cornering. The rollover and skidding risks of the most unfavorable road segments are assessed. The critical wheel is defined by the threshold of skidding during curve negotiation. The results found that SUVs are not easy to rollover on the most unfavorable roads, regardless of good or poor friction of pavement surface. The safety margin of rollover is greater than that of skidding. The safety margin of skidding is minimal on poor friction roads. Therefore, for the sake of driving safety, it is not recommended to design the roads with these unfavorable road geometry combinations

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Section: Introductionmentioning

confidence: 99%

The Influence of Road Geometry on Vehicle Rollover and Skidding

Yin

Wen

Sun

et al. 2020

IJERPH

View full text Add to dashboard Cite

show abstract

“…To accommodate the discrete nature of crash severity (no injury, slight injury, serious injury, and fatality), various regression approaches-random parameters logit (RP-logit) model [38,39], random parameters probit model [40], random intercept logit model [41], latent class logit model [10], and finite mixture random parameters model [16,42]-have been widely recommended due to their high flexibility [43][44][45]. Alternatively, random parameters ordered logit model [46] and random parameters ordered probit model [47] were applied to handle the intuitive ordering of crash severity. For example, Wu et al [8] established the RP-logit model to analyze the risk factors of single-and multi-vehicle crash severity on rural highways.…”

Section: Statistical Techniques For Unobserved Heterogeneity and Spatial Correlationmentioning

confidence: 99%

Modeling of Low Visibility-Related Rural Single-Vehicle Crashes Considering Unobserved Heterogeneity and Spatial Correlation

et al. 2021

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Accident analysis and prevention are helpful to ensure the sustainable development of transportation. The aim of this research was to investigate the factors associated with the severity of low-visibility-related rural single-vehicle crashes. Firstly, a latent class clustering model was implemented to partition the whole-dataset into a relatively homogeneous sub-dataset. Then, a spatial random parameters logit model was established for each dataset to capture unobserved heterogeneity and spatial correlation. Analysis was conducted based on the crash data (2014–2019) from 110 two-lane road segments. The results show that the proposed method is a superior crash severity modeling approach to accommodate the unobserved heterogeneity and spatial correlation. Three variables—seatbelt not used, motorcycle, and collision with fixed object—have a stable positive correlation with crash severity. Motorcycle leads to a 12.8%, 23.8%, and 12.6% increase in the risk of serious crashes in the whole-dataset, cluster 3, and cluster 4, respectively. In the whole-dataset, cluster 2, and cluster 3, the risk of serious crashes caused by seatbelt not used increased by 5.5%, 0.1%, and 30.6%, respectively, and caused by collision with fixed object increased by 33.2%, 1.2%, and 13.2%, respectively. The results can provide valuable information for engineers and policy makers to develop targeted measures.

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“…Crash injury severity is usually represented by discrete categories such as fatal, incapacitating injury, capacitating injury, possible injuries, and property damage only (PDO). Due to the discrete nature of injury severity classes, it is often analyzed by discrete outcome statistical models such as binary, multinomial, probit, and logit models [ 26 , 27 , 28 , 29 ]. It is widely agreed that crash data may exhibit unobserved heterogeneity, which may be tackled by adopting other advanced statistical models such as ordered logit models [ 26 , 30 , 31 , 32 ], bivariate/multivariate models [ 33 , 34 , 35 , 36 ], random parameter model, [ 37 , 38 ], nested logit model [ 39 , 40 ], and Bayesian hierarchical models [ 41 , 42 ].…”

Section: Related Workmentioning

confidence: 99%

Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network

Jamal

Umer

2020

IJERPH

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A better understanding of circumstances contributing to the severity outcome of traffic crashes is an important goal of road safety studies. An in-depth crash injury severity analysis is vital for the proactive implementation of appropriate mitigation strategies. This study proposes an improved feed-forward neural network (FFNN) model for predicting injury severity associated with individual crashes using three years (2017–2019) of crash data collected along 15 rural highways in the Kingdom of Saudi Arabia (KSA). A total of 12,566 crashes were recorded during the study period with a binary injury severity outcome (fatal or non-fatal injury) for the variable to be predicted. FFNN architecture with back-propagation (BP) as a training algorithm, logistic as activation function, and six number of hidden neurons in the hidden layer yielded the best model performance. Results of model prediction for the test data were analyzed using different evaluation metrics such as overall accuracy, sensitivity, and specificity. Prediction results showed the adequacy and robust performance of the proposed method. A detailed sensitivity analysis of the optimized NN was also performed to show the impact and relative influence of different predictor variables on resulting crash injury severity. The sensitivity analysis results indicated that factors such as traffic volume, average travel speeds, weather conditions, on-site damage conditions, road and vehicle type, and involvement of pedestrians are the most sensitive variables. The methods applied in this study could be used in big data analysis of crash data, which can serve as a rapid-useful tool for policymakers to improve highway safety.

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Severity analysis for large truck rollover crashes using a random parameter ordered logit model

Cited by 141 publications

References 51 publications

The Influence of Road Geometry on Vehicle Rollover and Skidding

The Influence of Road Geometry on Vehicle Rollover and Skidding

Modeling of Low Visibility-Related Rural Single-Vehicle Crashes Considering Unobserved Heterogeneity and Spatial Correlation

Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network

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