A comparative study on machine learning based algorithms for prediction of motorcycle crash severity

Wahab, Lukuman; Jiang, Haobin

doi:10.1371/journal.pone.0214966

Cited by 99 publications

(48 citation statements)

References 71 publications

(107 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In addition to the abovementioned categorizations, the algorithms can be further divided into either parametric or nonparametric groups. 25 The set of parameters in a parametric algorithm is fixed which confines the function to a known form. In nonparametric methods, the algorithm does not make any assumptions about the function to which it will map its variables.…”

Section: Supervised Machine Learning Algorithms: (Common Algorithms Amentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods

et al. 2019

View full text Add to dashboard Cite

Increased interest in the opportunities provided by artificial intelligence and machine learning has spawned a new field of health-care research. The new tools under development are targeting many aspects of medical practice, including changes to the practice of pathology and laboratory medicine. Optimal design in these powerful tools requires cross-disciplinary literacy, including basic knowledge and understanding of critical concepts that have traditionally been unfamiliar to pathologists and laboratorians. This review provides definitions and basic knowledge of machine learning categories (supervised, unsupervised, and reinforcement learning), introduces the underlying concept of the bias-variance trade-off as an important foundation in supervised machine learning, and discusses approaches to the supervised machine learning study design along with an overview and description of common supervised machine learning algorithms (linear regression, logistic regression, Naive Bayes, k-nearest neighbor, support vector machine, random forest, convolutional neural networks).

show abstract

Section: Supervised Machine Learning Algorithms: (Common Algorithms Amentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In 2019, Machine Learning (ML) algorithms for the prediction and classification of motorcycle crash severity were employed in a research by Wahab, L., and Jiang, H. [46]. Machine-learning-based techniques are non-parametric models without any presumption of the relationships between endogenous and exogenous variables.…”

Section: Related Workmentioning

confidence: 99%

Smart Helmet 5.0 for Industrial Internet of Things Using Artificial Intelligence

Campero-Jurado

Márquez-Sánchez

Quintanar-Gómez

et al. 2020

Sensors

View full text Add to dashboard Cite

Information and communication technologies (ICTs) have contributed to advances in Occupational Health and Safety, improving the security of workers. The use of Personal Protective Equipment (PPE) based on ICTs reduces the risk of accidents in the workplace, thanks to the capacity of the equipment to make decisions on the basis of environmental factors. Paradigms such as the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) make it possible to generate PPE models feasibly and create devices with more advanced characteristics such as monitoring, sensing the environment and risk detection between others. The working environment is monitored continuously by these models and they notify the employees and their supervisors of any anomalies and threats. This paper presents a smart helmet prototype that monitors the conditions in the workers’ environment and performs a near real-time evaluation of risks. The data collected by sensors is sent to an AI-driven platform for analysis. The training dataset consisted of 11,755 samples and 12 different scenarios. As part of this research, a comparative study of the state-of-the-art models of supervised learning is carried out. Moreover, the use of a Deep Convolutional Neural Network (ConvNet/CNN) is proposed for the detection of possible occupational risks. The data are processed to make them suitable for the CNN and the results are compared against a Static Neural Network (NN), Naive Bayes Classifier (NB) and Support Vector Machine (SVM), where the CNN had an accuracy of 92.05% in cross-validation.

show abstract

“…The study revealed that ML techniques outperform statistical techniques in terms of accuracy and efficiency. Location type, collision type, day and week of the crash, road surface condition, and shoulder condition were some of the factors that determined the motorcycle crash severity [24]. Decision trees (J48, ID3, and CART) and naïve Bayes algorithms were employed using the WEKA software tool for predicting crash severity.…”

Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

A comparative study on machine learning based algorithms for prediction of motorcycle crash severity

Cited by 99 publications

References 71 publications

Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods

Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods

Smart Helmet 5.0 for Industrial Internet of Things Using Artificial Intelligence

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

Contact Info

Product

Resources

About