AUTOMATED MACHINE LEARNING METHODS FOR TRAFFIC ACCIDENTS CLASSIFICATION
Rubrics: ARTICLES
Authors:
1.
Saint Petersburg Electrotechnical University
2.
St. Petersburg Federal Research Center of the Russian Academy of Sciences
3.
St. Petersburg Federal Research Center of the Russian Academy of Sciences
Type:
Article
Pages:
from 15
to 26
Status:
Published
Received:
11.01.2025
Accepted:
11.01.2025
Published:
11.01.2025
Subject area:
UDK УДК
Language:
Russian
Keywords:
AutoML, machine learning, hyperparameters optimization, CASH problem
Abstract (English):
Automated Machine Learning is an approach for automating the machine learning process by automatically selection of the most suitable machine learning algorithm and tuning its hyperparameters to create a machine learning model. Use of AutoML methods for prediction of traffic accidents severity can help improve the quality of models used to estimate the probability of different accidents based on various factors such as weather conditions, road and vehicle types, and driver behavior. The use of AutoML can significantly reduce the time required to create and tune models, as well as improve the accuracy of traffic accidents severity predictions, which in turn can lead to more efficient traffic management and fewer accidents. In this work we explore the applicability of different Auto ML libraries to the task of traffic accidents prediction and compare them with manually selected and tuned algorithms.
Automated Machine Learning is an approach for automating the machine learning process by automatically selection of the most suitable machine learning algorithm and tuning its hyperparameters to create a machine learning model. Use of AutoML methods for prediction of traffic accidents severity can help improve the quality of models used to estimate the probability of different accidents based on various factors such as weather conditions, road and vehicle types, and driver behavior. The use of AutoML can significantly reduce the time required to create and tune models, as well as improve the accuracy of traffic accidents severity predictions, which in turn can lead to more efficient traffic management and fewer accidents. In this work we explore the applicability of different Auto ML libraries to the task of traffic accidents prediction and compare them with manually selected and tuned algorithms.
Keywords:
AutoML, machine learning, hyperparameters optimization, CASH problem
AutoML, machine learning, hyperparameters optimization, CASH problem
1.
