On Hyperparameter Optimization of Machine Learning Methods Using a Bayesian Optimization Algorithm to Predict Work Travel Mode Choice

Prediction of work Travel mode choice is one of the most important parts of travel demand forecasting. Planners can achieve sustainability goals by accurately forecasting how people will get to and from work. In the prediction of travel mode selection, machine learning methods are commonly employed....

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Vydáno v:	IEEE access Ročník 11; s. 19762 - 19774
Hlavní autoři:	Aghaabbasi, Mahdi, Ali, Mujahid, Jasinski, Michal, Leonowicz, Zbigniew, Novak, Tomas
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Bayes methods Bayesian analysis Bayesian optimization algorithm Decision trees Forecasting hyperparameters Machine learning Mathematical models Modal choice Optimization Optimization algorithms Predictive models Support vector machines sustainable mode choice decision Transportation Travel demand Travel modes Trip surveys work travel mode choice
ISSN:	2169-3536, 2169-3536
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Abstract	Prediction of work Travel mode choice is one of the most important parts of travel demand forecasting. Planners can achieve sustainability goals by accurately forecasting how people will get to and from work. In the prediction of travel mode selection, machine learning methods are commonly employed. To fit a machine-learning model to various challenges, the hyperparameters must be tweaked. Choosing the optimal hyperparameter configuration for machine learning models has an immediate effect on the performance of the model. In this paper, optimizing the hyperparameters of common machine learning models, including support vector machines, k-nearest neighbor, single decision trees, ensemble decision trees, and Naive Bayes, is studied using the Bayesian Optimization algorithm. These models were developed and optimized using two datasets from the 2017 National Household Travel Survey. Using several criteria, including average accuracy (%), average area under the receiver operating characteristics, and a simple ranking system, the performance of the optimized models was investigated. The findings of this study show that the BO is an effective model for improving the performance of the k-nearest neighbor model more than other models. This research lays the groundwork for using optimized machine learning methods to mitigate the negative consequences of automobile use.
AbstractList	Prediction of work Travel mode choice is one of the most important parts of travel demand forecasting. Planners can achieve sustainability goals by accurately forecasting how people will get to and from work. In the prediction of travel mode selection, machine learning methods are commonly employed. To fit a machine-learning model to various challenges, the hyperparameters must be tweaked. Choosing the optimal hyperparameter configuration for machine learning models has an immediate effect on the performance of the model. In this paper, optimizing the hyperparameters of common machine learning models, including support vector machines, k-nearest neighbor, single decision trees, ensemble decision trees, and Naive Bayes, is studied using the Bayesian Optimization algorithm. These models were developed and optimized using two datasets from the 2017 National Household Travel Survey. Using several criteria, including average accuracy (%), average area under the receiver operating characteristics, and a simple ranking system, the performance of the optimized models was investigated. The findings of this study show that the BO is an effective model for improving the performance of the k-nearest neighbor model more than other models. This research lays the groundwork for using optimized machine learning methods to mitigate the negative consequences of automobile use.
Author	Leonowicz, Zbigniew Jasinski, Michal Novak, Tomas Ali, Mujahid Aghaabbasi, Mahdi
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SubjectTerms	Algorithms Bayes methods Bayesian analysis Bayesian optimization algorithm Decision trees Forecasting hyperparameters Machine learning Mathematical models Modal choice Optimization Optimization algorithms Predictive models Support vector machines sustainable mode choice decision Transportation Travel demand Travel modes Trip surveys work travel mode choice
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Title	On Hyperparameter Optimization of Machine Learning Methods Using a Bayesian Optimization Algorithm to Predict Work Travel Mode Choice
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