Crash severity analysis of rear-end crashes in California using statistical and machine learning classification methods

Ahmadi, Alidad; Jahangiri, Arash; Berardi, Vincent; Machiani, Sahar Ghanipoor

doi:10.1080/19439962.2018.1505793

Cited by 58 publications

(22 citation statements)

References 39 publications

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“…These drivers tend to be more aware of the danger and are better prepared to slow down or take other measures to reduce the crash risk. These findings are consistent with a number of previous researches [31,[38][39][40][41]. However, Zhao and Garber found no major differences between the day and nighttime crashes in the work zone [42].…”

Section: Model Estimation Resultssupporting

confidence: 93%

“…This finding is deemed to be reasonable for the unique attitude of male drivers to take more risks, drive over the speed-limit, and drive more aggressively. Accordingly, a number of previous studies [16,30,31] are in agreement with these findings. However, some studies [32,33] have argued this aspect, while claiming that similar circumstances tend to affect female drivers more than male drivers .…”

Section: Model Estimation Resultssupporting

confidence: 88%

“…It is interpreted based on the fact that the reckless attitude of drivers is not notable during adverse weather period as compared to clear and dry weather; i.e., the drivers are intrinsically cautious while travelling in adverse weather conditions. However, some studies report the contrary findings that foggy condition increases the rate of fatal crashes [31,36].…”

Section: Model Estimation Resultsmentioning

confidence: 93%

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Identifying the Factors Contributing to Injury Severity in Work Zone Rear-End Crashes

Zhang

Hassan

2019

Journal of Advanced Transportation

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Egypt’s National Road Project is a large infrastructure project aiming to upgrade the existing network of 2500 kilometers as well as constructing new roads of 4000 kilometers to meet today’s need. Increasing highway work zones eventually direct the challenges for traffic safety and mobility. Realizing the need for mitigating the impact of such a challenging scenario, this paper aims to investigate and identify the factors of work zone rear-end crash severity. In this regard, a random parameter ordered probit model was applied to analyze data on the Egyptian long-term highway work zone projects during the period of 2010 to 2017. The factors of speeding and foggy weather conditions are found to be the key indicators for modeling the random parameters. Besides, during the weekend and at nighttime, there is a higher risk of rear-end crash in work zones, while heavy and passenger vehicles are at greater risk in this regard. It is anticipated that the findings of this study would facilitate transport agencies in developing effective measures to ensure safe mobility across work zones.

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Section: Model Estimation Resultssupporting

confidence: 93%

Section: Model Estimation Resultssupporting

confidence: 88%

Section: Model Estimation Resultsmentioning

confidence: 93%

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Identifying the Factors Contributing to Injury Severity in Work Zone Rear-End Crashes

Zhang

Hassan

2019

Journal of Advanced Transportation

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“…Random Forest (RF), Support Vector Machine (SVM), and Back-Propagation Neural Network (BPNN) had all been widely and successfully used in predicting the possibility of injury-severity outcome [ 25 , 26 , 27 , 28 ]. Choosing a suitable method for the crash prediction is critical.…”

Section: Methodsmentioning

confidence: 99%

Determinants and Prediction of Injury Severities in Multi-Vehicle-Involved Crashes

Song

et al. 2021

IJERPH

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Multi-vehicle (MV) crashes, which can lead to great damages to society, have always been a serious issue for traffic safety. A further understanding of crash severity can help transportation engineers identify the critical reasons and find effective countermeasures to improve transportation safety. However, studies involving methods of machine learning to predict the possibility of injury-severity of MV crashes are rarely seen. Besides that, previous studies have rarely taken temporal stability into consideration in MV crashes. To bridge these knowledge gaps, two kinds of models: random parameters logit model (RPL), with heterogeneities in the means and variances, and Random Forest (RF) were employed in this research to identify the critical contributing factors and to predict the possibility of MV injury-severity. Three-year (2016–2018) MV data from Washington, United States, extracted from the Highway Safety Information System (HSIS), were applied for crash injury-severity analysis. In addition, a series of likelihood ratio tests were conducted for temporal stability between different years. Four indicators were employed to measure the prediction performance of the selected models, and four categories of crash-related characteristics were specifically investigated based on the RPL model. The results showed that the machine learning-based models performed better than the statistical models did when taking the overall accuracy as an evaluation indicator. However, the statistical models had a better prediction performance than the machine learning models had considering crash costs. Temporal instabilities were present between 2016 and 2017 MV data. The effect of significant factors was elaborated based on the RPL model with heterogeneities in the means and variances.

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“…Another study modeled and compared crash severity using the MNL, mixed multinomial logit (MMNL), and SVM models using rear-end crash data for California. The study found that SVM outperformed other models in terms of prediction accuracy [22]. Singh et al [23] modeled the traffic crash severity using MNL and two non-parametric techniques -RF and DTfor a dataset in Haryana, India.…”

Section: Literature Reviewmentioning

confidence: 99%

Crash Severity Prediction Using Two-Layer Ensemble Machine Learning Model for Proactive Emergency Management

et al. 2020

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Many unfortunate victims in road traffic crashes do not receive ideal treatment because their injury severity is not understood at an early stage. Swift crash severity prediction enables trauma and emergency centers to estimate the potential damage resulting from a road traffic crash and accordingly dispatch the proper emergency units to provide appropriate emergency treatment. A two-layer ensemble machine learning model is proposed in this study to predict road traffic crash severity. The first layer integrates four base machine learning models: k-nearest neighbor, decision tree, adaptive boosting, and support vector machine; the second layer classifies the crash severity based on the feedforward neural network model. The models are developed using road traffic crash data of road intersections over 6 years (2011-2016) obtained from Great Britain's Department of Transport online database. Only the crash features that can be instantaneously and easily obtained are used as an input. To simplify the two-layer ensemble model, principal component analysis technique is used for dimensionality reduction in the second layer of the model. The performance of the two-layer ensemble model is compared with five base models: k-nearest neighbor, decision tree, adaptive boosting, support vector machine, and feedforward neural network. The prediction results reveal that the two-layer ensemble model outperforms the five base classification models based on two performance indicators: testing accuracy and F1 score. The transferability of the developed model is tested using the 3-year crash dataset for Canada obtained from the National Crash Database Online. The outcome indicates that the two-layer ensemble model shows the best performance for the Canadian dataset also. The proposed two-layer ensemble model would be beneficial in predicting crash severity with high accuracy based on limited initial crash information obtained from the crash location. Using this information, trauma centers would be able to prepare for appropriate and prompt medical treatment.

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Crash severity analysis of rear-end crashes in California using statistical and machine learning classification methods

Cited by 58 publications

References 39 publications

Identifying the Factors Contributing to Injury Severity in Work Zone Rear-End Crashes

Identifying the Factors Contributing to Injury Severity in Work Zone Rear-End Crashes

Determinants and Prediction of Injury Severities in Multi-Vehicle-Involved Crashes

Crash Severity Prediction Using Two-Layer Ensemble Machine Learning Model for Proactive Emergency Management

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