Multivariate copula temporal modeling of intersection crash consequence metrics: A joint estimation of injury severity, crash type, vehicle damage and driver error

Wang, Kai; Bhowmik, Tanmoy; Yasmin, Shamsunnahar; Zhao, Shanshan; Eluru, Naveen; Jackson, Eric

doi:10.1016/j.aap.2019.01.036

Cited by 59 publications

(17 citation statements)

References 40 publications

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“…Unobserved heterogeneity and spatial correlation were addressed, and the results helped to prioritize area-wide safety initiatives and programs. Besides bivariate regression models, different multivariate regression models, for example, multivariate tobit analysis [29][30][31][32], Bayesian multivariate approach [33,34], multivariate spatial or/and temporal models [35][36][37][38][39], and mixture of abovementioned models, have been presented to address correlation and unobserved heterogeneity among injury severities.…”

Section: Introductionmentioning

confidence: 99%

Insights on Crash Injury Severity Control from Novice and Experienced Drivers: A Bivariate Random-Effects Probit Analysis

Xiao

Yuan

Kang

et al. 2021

Discrete Dynamics in Nature and Society

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This study intended to investigate the crash injury severity from the insights of the novice and experienced drivers. To achieve this objective, a bivariate panel data probit model was initially proposed to account for the correlation between both time-specific and individual-specific error terms. The geocrash data of Las Vegas metropolitan area from 2014 to 2017 were collected. In order to estimate two (seemingly unrelated) nonlinear processes and to control for interrelations between the unobservables, the bivariate random-effects probit model was built up, in which injury severity levels of novice and experienced drivers were addressed by bivariate (seemingly unrelated) probit simultaneously, and the interrelations between the unobservables (i.e., heterogeneity issue) were accommodated by bivariate random-effects model. Results revealed that crash types, vehicle types of minor responsibility, pedestrians, and motorcyclists were potentially significant factors of injury severity for novice drivers, while crash types, driver condition of minor responsibility, first harm, and highway factor were significant for experienced drivers. The findings provide useful insights for practitioners to improve traffic safety levels of novice and experienced drivers.

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Section: Introductionmentioning

confidence: 99%

Insights on Crash Injury Severity Control from Novice and Experienced Drivers: A Bivariate Random-Effects Probit Analysis

Xiao

Yuan

Kang

et al. 2021

Discrete Dynamics in Nature and Society

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show abstract

“…As traffic incident type and severity classification is a multivariate problem and both the target variables are highly interdependent, therefore, both the target variables could be classified simultaneously by stacking them at various levels of model stacking. (6) Existing MFC-based approaches [18,23] train the classifiers using the entire training dataset for each target variable independently and thus involve high training overhead whereas the proposed model stacking approach reduces the training overhead by exploiting the predictions of intermediate levels to obtain the final predictions. ( 7) e results of the proposed approach could be enhanced near to reality by deploying various real vehicular features in the datasets extracted from the smart vehicle, smart road infrastructure, and smart city environment as the traffic parameters show a high dynamicity in real-time.…”

Section: Results Evaluation Summarymentioning

confidence: 99%

“…Incidents are considered as outliers, which limits it to take binary classification decisions. A copula-based approach is employed in [23] with a multivariate temporal ordered probit model to simultaneously predict injury and damage severity.…”

Section: Ml-based Aid Systems Can Be Categorized Into Two Classesmentioning

confidence: 99%

An Efficient Traffic Incident Detection and Classification Framework by Leveraging the Efficacy of Model Stacking

et al. 2021

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Automatic incident detection (AID) plays a vital role among all the safety-critical applications under the parasol of Intelligent Transportation Systems (ITSs) to provide timely information to passengers and other stakeholders (hospitals and rescue, police, and insurance departments) in smart cities. Moreover, accurate classification of these incidents with respect to type and severity assists the Traffic Incident Management Systems (TIMSs) and stakeholders in devising better plans for incident site management and avoiding secondary incidents. Most of the AID systems presented in the literature are incident type-specific, i.e., either they are designed for the detection of accident or congestion. While traveling along the road, one may come across different types of traffic incidents, such as accidents, congestion, and reckless driving. This necessitates that the AID system detects and classifies not only all the popular traffic incident types, but severity as well that is associated with these incidents. Therefore, this study aims to propose an efficient incident detection and classification (E-IDC) framework for smart cities, by incorporating the efficacy of model stacking, to classify the incidents with respect to their types and severity levels. The experimental results showed that the proposed E-IDC framework achieved performance gains of 5%–56% in terms of incident severity classification and 1%–14% in terms of incident type classification when applied with different classifiers. We have also applied the Wilcoxon test to benchmark the performance of our proposed framework that reflects the significance of our approach over existing individual incident predictors in terms of severity and type classification. Moreover, it has been observed that the proposed E-IDC framework outperforms the existing ensemble technique, such as XGBoost used for the classification of incidents.

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“…In the operation stage, the driver controls the car accelerating, decelerating, turning, and braking according to the decision. Note that over 80% traffic accidents are triggered due to the driver errors [16,27,28]. To avoid the potential traffic accidents, drivers are supposed to take early and correct maneuver activities (i.e., identify risky behaviors, decide the suitable travel behavior, take maneuver activities, etc.)…”

Section: Driving Characteristics Analysismentioning

confidence: 99%

Study and Simulation Analysis of Vehicle Rear-End Collision Model considering Driver Types

Luo

Chen

Yuan

et al. 2020

Journal of Advanced Transportation

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The reasonable distance between adjacent cars is very crucial for roadway traffic safety. For different types of drivers or different driving environments, the required safety distance is different. However, most of the existing rear-end collision models do not fully consider the subjective factor such as the driver. Firstly, the factors affecting driving drivers’ characteristics, such as driver age, gender, and driving experience are analyzed. Then, on the basis of this, drivers are classified according to reaction time. Secondly, three main factors affecting driving safety are analyzed by using fuzzy theory, and the new calculation method of the reaction time is obtained. Finally, the improved car-following safety model is established based on different reaction time. The experimental results have shown that our proposed model obtained more accurate vehicle safety distance with varied traffic kinematic conditions (i.e., different traffic states, varied driver types, etc.). The findings can help traffic regulation departments issue early warnings to avoid potential traffic accidents on roads.

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Multivariate copula temporal modeling of intersection crash consequence metrics: A joint estimation of injury severity, crash type, vehicle damage and driver error

Cited by 59 publications

References 40 publications

Insights on Crash Injury Severity Control from Novice and Experienced Drivers: A Bivariate Random-Effects Probit Analysis

Insights on Crash Injury Severity Control from Novice and Experienced Drivers: A Bivariate Random-Effects Probit Analysis

An Efficient Traffic Incident Detection and Classification Framework by Leveraging the Efficacy of Model Stacking

Study and Simulation Analysis of Vehicle Rear-End Collision Model considering Driver Types

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