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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Veröffentlicht in:IEEE access Jg. 11; S. 19762 - 19774
Hauptverfasser: Aghaabbasi, Mahdi, Ali, Mujahid, Jasinski, Michal, Leonowicz, Zbigniew, Novak, Tomas
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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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Zusammenfassung: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3247448