Factor Identification and Prediction for Teen Driver Crash Severity Using Machine Learning: A Case Study

Lin, Ciyun; Wu, Dayong; Liu, Hongchao; Xia, Xueting; Bhattarai, Nischal

doi:10.3390/app10051675

Cited by 33 publications

(15 citation statements)

References 24 publications

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“…On the other hand, one hot encoder converts the categorical variable into a sparse binary matrix. The results of this study indicate that the combination of label encoder and XGBoost appeared to yield better accuracy and computation time [14]. Along the same line of thought, Zhang et al compared the prediction performance of two discrete choice models and four ML models to predict the injury severity in crashes at freeway diverge areas in Florida.…”

Section: Summary Of the Literature Reviewmentioning

confidence: 75%

“…To address the potential shortcoming related to missing features, the input data was expanded by adding the reactive data, specifically the driver age and vehicle age variables. As noted in the literature review, the studies have shown that driver and vehicle characteristics have a significant impact on crash occurrence and their severity outcomes [14][15][16]. Despite the critical impact of the factors described by reactive data on the crash severity outcomes, the main challenge of using the reactive data for operational crash prediction is that they are not available in real-time.…”

Section: Application Of Reactive Data In the Crash Severity Modelmentioning

confidence: 99%

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Short-Term Segment-Level Crash Risk Prediction Using Advanced Data Modeling with Proactive and Reactive Crash Data

et al. 2022

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Highway crashes, along with the property damage, personal injuries, and fatalities that they cause, continue to present one of the most significant and critical transportation problems. At the same time, provision of safe travel is one of the main goals of any transportation system. For this reason, both in transportation research and practice much attention has been given to the analysis and modeling of traffic crashes, including the development of models that can be applied to predict crash occurrence and crash severity. In general, such models assess short-term crash risks at a given highway facility, thus providing intelligence that can be used to identify and implement traffic operations strategies for crash mitigation and prevention. This paper presents several crash risk and injury severity assessment models applied at a highway segment level, considering the input data that is typically collected or readily available to most transportation agencies in real-time and at a regional network scale, which would render them readily applicable in practice. The input data included roadway geometry characteristics, traffic flow characteristics, and weather condition data. The paper develops, tests, and compares the performance of models that employ Random effects Bayesian Logistics Regression, Gaussian Naïve Bayes, K-Nearest Neighbor, Random Forest, and Gradient Boosting Machine methods. The paper applies random oversampling examples (ROSE) method to deal with the problem of data imbalance associated with the injury severity analysis. The models were trained and tested using a dataset of 10,155 crashes that occurred on two interstate highways in New Jersey over a two-year period. The paper also analyzes the potential improvement in the prediction abilities of the tested models by adding reactive data to the analysis. To that end, traffic crashes were classified in multiple classes based on the driver age and the vehicle age to assess the impact of these attributes on driver injury severity outcomes. The results of this analysis are promising, showing that the simultaneous use of reactive and proactive data can improve the prediction performance of the presented models.

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Section: Summary Of the Literature Reviewmentioning

confidence: 75%

Section: Application Of Reactive Data In the Crash Severity Modelmentioning

confidence: 99%

Short-Term Segment-Level Crash Risk Prediction Using Advanced Data Modeling with Proactive and Reactive Crash Data

et al. 2022

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“…Machine learning has recently received attention due to the emergence of big data generated by multiple sources and the availability of computational power [26]. It performs well in solving several complex and nonlinear problems [27,28] and is widely applied in the prediction of drivers' behavior and some traffic-safety-related researches [29].…”

Section: Literature Reviewmentioning

confidence: 99%

Factors Identification and Prediction for Mind Wandering Driving Using Machine Learning

Lin

Zhang

Gong

et al. 2021

Journal of Advanced Transportation

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Traffic safety is affected by many complex factors. Mind wandering (MW) is a fatal cause affecting driving safety and is hard to be detected and prevented due to its uncertain and complex occurrence mechanism. The aim of this study was to propose a framework for analyzing and predicting MW based on readily available driving status data. The data used in this study are the single-trip information collected by the questionnaire, which includes drivers’ personal characteristics, contextual information in which MW occurs, and in-vehicle environmental factors. After investigating the extent of factors that influence MW, these chosen factors are used to forecast MW. Based on these results, we select factors reliable to be obtained in real life to forecast MW. To verify that the new factors explored are useful in improving the forecast accuracy, the compared analysis is conducted with the results found by our approach and the existing approaches. We compare results obtained by four machine-learning-enabled forecasting approaches on a real-life data set. The result shows that the factors found in this paper can significantly improve forecast accuracy. The confusion matrix, ROC curves, and AUC are conducted, and the performance of the gradient boosting decision tree algorithm is better than other forecast approaches. The importance rankings of most factors obtained by the Gradient Boosting Decision Tree and questionnaire are the same.

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“…Deep learning, according to the study, improved prediction performance for both fatal and non-fatal injuries. Lin et al [ 21 ] employed four machine learning classifiers to forecast the injury severity caused by juvenile driving incidents on West Texas’ rural roadways. The speed limit, road class, and the first detrimental occurrence were the three most influential elements impacting injury severity, according to the experimental data.…”

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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Factor Identification and Prediction for Teen Driver Crash Severity Using Machine Learning: A Case Study

Cited by 33 publications

References 24 publications

Short-Term Segment-Level Crash Risk Prediction Using Advanced Data Modeling with Proactive and Reactive Crash Data

Short-Term Segment-Level Crash Risk Prediction Using Advanced Data Modeling with Proactive and Reactive Crash Data

Factors Identification and Prediction for Mind Wandering Driving Using Machine Learning

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

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