Injury Severity Prediction From Two-Vehicle Crash Mechanisms With Machine Learning and Ensemble Models

Ji, Ang; Levinson, David

doi:10.1109/ojits.2020.3033523

Cited by 20 publications

(13 citation statements)

References 58 publications

(64 reference statements)

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“…The following references appear in the Supplemental information: Alkheder et al., 2017 , Assi, 2020 , Chen et al., 2015 , Chen et al., 2016 , He et al., 2018 , Ji and Levinson, 2020 , Liu et al., 2020 , Lubbe and Kiuchi, 2015 , Mansoor et al., 2020 , Rezapour et al., 2020 , Rezapour and Ksaibati, 2020 , Sameen and Pradhan, 2017 , Tang et al., 2019 , Wahab and Jiang, 2020 , Wang and Kim, 2019 , Zheng et al., 2019 .…”

Section: Supporting Citationsmentioning

confidence: 99%

Human injury-based safety decision of automated vehicles

et al. 2022

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Section: Supporting Citationsmentioning

confidence: 99%

Human injury-based safety decision of automated vehicles

et al. 2022

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“…[42] 2000 483 [41] 2003 323 [43] 2002 395 [48] 2010 248 [49] 2018 152 [50] 2011 137 [55] 2016 135 [51] 2019 132 [52] 2015 104 [56] 2018 101 [25] 2017 92 [53] 2014 83 [54] 2020 67…”

Section: Papers Year Citationsmentioning

confidence: 99%

“…Young et al [53] explore the types of data used to develop, calibrate and validate CSP models. Finally, Ji et al [54] explore the predictive potential of variables related to the collision mechanism using ensemble learning models.…”

mentioning

confidence: 99%

A Bibliometric Analysis and Benchmark of Machine Learning and AutoML in Crash Severity Prediction: The Case Study of Three Colombian Cities

Angarita-Zapata

Maestre-Góngora

Calderín

2021

Sensors

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Traffic accidents are of worldwide concern, as they are one of the leading causes of death globally. One policy designed to cope with them is the design and deployment of road safety systems. These aim to predict crashes based on historical records, provided by new Internet of Things (IoT) technologies, to enhance traffic flow management and promote safer roads. Increasing data availability has helped machine learning (ML) to address the prediction of crashes and their severity. The literature reports numerous contributions regarding survey papers, experimental comparisons of various techniques, and the design of new methods at the point where crash severity prediction (CSP) and ML converge. Despite such progress, and as far as we know, there are no comprehensive research articles that theoretically and practically approach the model selection problem (MSP) in CSP. Thus, this paper introduces a bibliometric analysis and experimental benchmark of ML and automated machine learning (AutoML) as a suitable approach to automatically address the MSP in CSP. Firstly, 2318 bibliographic references were consulted to identify relevant authors, trending topics, keywords evolution, and the most common ML methods used in related-case studies, which revealed an opportunity for the use AutoML in the transportation field. Then, we compared AutoML (AutoGluon, Auto-sklearn, TPOT) and ML (CatBoost, Decision Tree, Extra Trees, Gradient Boosting, Gaussian Naive Bayes, Light Gradient Boosting Machine, Random Forest) methods in three case studies using open data portals belonging to the cities of Medellín, Bogotá, and Bucaramanga in Colombia. Our experimentation reveals that AutoGluon and CatBoost are competitive and robust ML approaches to deal with various CSP problems. In addition, we concluded that general-purpose AutoML effectively supports the MSP in CSP without developing domain-focused AutoML methods for this supervised learning problem. Finally, based on the results obtained, we introduce challenges and research opportunities that the community should explore to enhance the contributions that ML and AutoML can bring to CSP and other transportation areas.

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“…Ensemble learning models combine the outputs of multiple statistical and machine learning models to obtain a better prediction estimate and range. Ji and Levinson [ 25 ] used ensemble machine learning models to predict injury severity. The results showed that the stacking model with a linear blender is preferable for the designed ensemble combination.…”

Section: Introductionmentioning

confidence: 99%

Hybrid feature selection-based machine learning Classification system for the prediction of injury severity in single and multiple-vehicle accidents

et al. 2022

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To undertake a reliable analysis of injury severity in road traffic accidents, a complete understanding of important attributes is essential. As a result of the shift from traditional statistical parametric procedures to computer-aided methods, machine learning approaches have become an important aspect in predicting the severity of road traffic injuries. The paper presents a hybrid feature selection-based machine learning classification approach for detecting significant attributes and predicting injury severity in single and multiple-vehicle accidents. To begin, we employed a Random Forests (RF) classifier in conjunction with an intrinsic wrapper-based feature selection approach called the Boruta Algorithm (BA) to find the relevant important attributes that determine injury severity. The influential attributes were then fed into a set of four classifiers to accurately predict injury severity (Naive Bayes (NB), K-Nearest Neighbor (K-NN), Binary Logistic Regression (BLR), and Extreme Gradient Boosting (XGBoost)). According to BA’s experimental investigation, the vehicle type was the most influential factor, followed by the month of the year, the driver’s age, and the alignment of the road segment. The driver’s gender, the presence of a median, and the presence of a shoulder were all found to be unimportant. According to classifier performance measures, XGBoost surpasses the other classifiers in terms of prediction performance. Using the specified attributes, the accuracy, Cohen’s Kappa, F1-Measure, and AUC-ROC values of the XGBoost were 82.10%, 0.607, 0.776, and 0.880 for single vehicle accidents and 79.52%, 0.569, 0.752, and 0.86 for multiple-vehicle accidents, respectively.

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Injury Severity Prediction From Two-Vehicle Crash Mechanisms With Machine Learning and Ensemble Models

Cited by 20 publications

References 58 publications

Human injury-based safety decision of automated vehicles

Human injury-based safety decision of automated vehicles

A Bibliometric Analysis and Benchmark of Machine Learning and AutoML in Crash Severity Prediction: The Case Study of Three Colombian Cities

Hybrid feature selection-based machine learning Classification system for the prediction of injury severity in single and multiple-vehicle accidents

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