Airplane Crash Severity Prediction Using Machine Learning

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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Severity Prediction of COVID-19 Patients Using Machine Learning Classification Algorithms: A Case Study of Small City in Pakistan with Minimal Health Facility

2020 IEEE 6th International Conference on Computer and Communications (ICCC) ◽

10.1109/iccc51575.2020.9344984 ◽

2020 ◽

Author(s):

Hina Gull ◽

Gomathi Krishna ◽

May Issa Aldossary ◽

Sardar Zafar Iqbal

Keyword(s):

Machine Learning ◽

Health Facility ◽

Small City ◽

Classification Algorithms ◽

Machine Learning Classification ◽

Severity Prediction

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Exploring the Mechanism of Crashes with Autonomous Vehicles Using Machine Learning

Mathematical Problems in Engineering ◽

10.1155/2021/5524356 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Hengrui Chen ◽

Hong Chen ◽

Ruiyu Zhou ◽

Zhizhen Liu ◽

Xiaoke Sun

Keyword(s):

Machine Learning ◽

Autonomous Vehicles ◽

Classification And Regression Tree ◽

Gradient Boosting ◽

Support Vector ◽

Crash Severity ◽

Apriori Algorithm ◽

Driving Mode ◽

Extreme Gradient Boosting ◽

The Impact

The safety issue has become a critical obstacle that cannot be ignored in the marketization of autonomous vehicles (AVs). The objective of this study is to explore the mechanism of AV-involved crashes and analyze the impact of each feature on crash severity. We use the Apriori algorithm to explore the causal relationship between multiple factors to explore the mechanism of crashes. We use various machine learning models, including support vector machine (SVM), classification and regression tree (CART), and eXtreme Gradient Boosting (XGBoost), to analyze the crash severity. Besides, we apply the Shapley Additive Explanations (SHAP) to interpret the importance of each factor. The results indicate that XGBoost obtains the best result (recall = 75%; G-mean = 67.82%). Both XGBoost and Apriori algorithm effectively provided meaningful insights about AV-involved crash characteristics and their relationship. Among all these features, vehicle damage, weather conditions, accident location, and driving mode are the most critical features. We found that most rear-end crashes are conventional vehicles bumping into the rear of AVs. Drivers should be extremely cautious when driving in fog, snow, and insufficient light. Besides, drivers should be careful when driving near intersections, especially in the autonomous driving mode.

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Comparative Analysis of Machine Learning Models for the Prediction of Pedestrian Crash Severity: Focused on Balancing Pedestrian Crash Dataset

Journal of Korean Society for Geospatial Information System ◽

10.7319/kogsis.2021.29.2.003 ◽

2021 ◽

Vol 29 (2) ◽

pp. 3-15

Author(s):

Hojun Lee ◽

Sugie Lee

Keyword(s):

Machine Learning ◽

Comparative Analysis ◽

Crash Severity ◽

Learning Models ◽

Machine Learning Models

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Machine learning method using position-specific mutation based classification outperforms one hot coding for disease severity prediction in haemophilia ‘A’

Genomics ◽

10.1016/j.ygeno.2020.09.020 ◽

2020 ◽

Vol 112 (6) ◽

pp. 5122-5128

Author(s):

Vikalp Kumar Singh ◽

Neha Shree Maurya ◽

Ashutosh Mani ◽

Rama Shankar Yadav

Keyword(s):

Machine Learning ◽

Disease Severity ◽

Haemophilia A ◽

Machine Learning Method ◽

Learning Method ◽

Specific Mutation ◽

Severity Prediction

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Severity prediction of motorcycle crashes with machine learning methods

International Journal of Crashworthiness ◽

10.1080/13588265.2019.1616885 ◽

2019 ◽

Vol 25 (5) ◽

pp. 485-492 ◽

Cited By ~ 7

Author(s):

Lukuman Wahab ◽

Haobin Jiang

Keyword(s):

Machine Learning ◽

Learning Methods ◽

Machine Learning Methods ◽

Severity Prediction ◽

Motorcycle Crashes

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

Applied Sciences ◽

10.3390/app10051675 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1675 ◽

Cited By ~ 3

Author(s):

Ciyun Lin ◽

Dayong Wu ◽

Hongchao Liu ◽

Xueting Xia ◽

Nischal Bhattarai

Keyword(s):

Machine Learning ◽

Traffic Safety ◽

Rural Areas ◽

Computational Cost ◽

The United States ◽

Crash Severity ◽

Rural Roads ◽

Factors Affecting ◽

West Texas ◽

Teen Drivers

Crashes among young and inexperienced drives are a major safety problem in the United States, especially in an area with large rural road networks, such as West Texas. Rural roads present many unique safety concerns that are not fully explored. This study presents a complete machine leaning pipeline to find the patterns of crashes involved with teen drivers no older than 20 on rural roads in West Texas, identify factors that affect injury levels, and build four machine learning predictive models on crash severity. The analysis indicates that the major causes of teen driver crashes in West Texas are teen drivers who failed to control speed or travel at an unsafe speed when they merged from rural roads to highways or approached intersections. They also failed to yield on the undivided roads with four or more lanes, leading to serious injuries. Road class, speed limit, and the first harmful event are the top three factors affecting crash severity. The predictive machine learning model, based on Label Encoder and XGBoost, seems the best option when considering both accuracy and computational cost. The results of this work should be useful to improve rural teen driver traffic safety in West Texas and other rural areas with similar issues.

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Predicting Crash Injury Severity with Machine Learning Algorithm Synergized with Clustering Technique: A Promising Protocol

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17155497 ◽

2020 ◽

Vol 17 (15) ◽

pp. 5497

Author(s):

Khaled Assi ◽

Syed Masiur Rahman ◽

Umer Mansoor ◽

Nedal Ratrout

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Clustering Algorithm ◽

Injury Severity ◽

Support Vector ◽

Severity Level ◽

Feed Forward ◽

Fuzzy C Means ◽

Severity Prediction ◽

Crash Injury Severity

Predicting crash injury severity is a crucial constituent of reducing the consequences of traffic crashes. This study developed machine learning (ML) models to predict crash injury severity using 15 crash-related parameters. Separate ML models for each cluster were obtained using fuzzy c-means, which enhanced the predicting capability. Finally, four ML models were developed: feed-forward neural networks (FNN), support vector machine (SVM), fuzzy C-means clustering based feed-forward neural network (FNN-FCM), and fuzzy c-means based support vector machine (SVM-FCM). Features that were easily identified with little investigation on crash sites were used as an input so that the trauma center can predict the crash severity level based on the initial information provided from the crash site and prepare accordingly for the treatment of the victims. The input parameters mainly include vehicle attributes and road condition attributes. This study used the crash database of Great Britain for the years 2011–2016. A random sample of crashes representing each year was used considering the same share of severe and non-severe crashes. The models were compared based on injury severity prediction accuracy, sensitivity, precision, and harmonic mean of sensitivity and precision (i.e., F1 score). The SVM-FCM model outperformed the other developed models in terms of accuracy and F1 score in predicting the injury severity level of severe and non-severe crashes. This study concluded that the FCM clustering algorithm enhanced the prediction power of FNN and SVM models.

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