Using Slightly Imbalanced Binary Classification to Predict the Efficiency of Winter Road Maintenance
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https://hdl.handle.net/10037/23939Date
2021-11-30Type
Journal articleTidsskriftartikkel
Peer reviewed
Abstract
The prediction of efficiency scores for winter road maintenance (WRM) is a challenging
and serious issue in countries with cold climates. While effective and efficient WRM is a key contributor
to maximizing road transportation safety and minimizing costs and environmental impacts, it has not yet
been included in intelligent prediction methods. Therefore, this study aims to design a WRM efficiency
classification prediction model that combines data envelopment analysis and machine learning techniques
to improve decision support systems for decision-making units. The proposed methodology consists of six
stages and starts with road selection. Real data are obtained by observing road conditions in equal time
intervals via road weather information systems, optical sensors, and road-mounted sensors. Then, data
preprocessing is performed, and efficiency scores are calculated with the data envelopment analysis method
to classify the decision-making units into efficient and inefficient classes. Next, the WRM efficiency classes
are considered targets for machine learning classification algorithms, and the dataset is split into training
and test datasets. A slightly imbalanced binary classification case is encountered since the distributions
of inefficient and efficient classes in the training dataset are unequal, with a low ratio between classes.
The proposed methodology includes a comparison of different machine learning classification techniques.
The graphical and numerical results indicate that the combination of a support vector machine and genetic
algorithm yields the best generalization performance. The results include analyzing the variables that affect
the WRM and using efficiency classes to drive future insights to improve the process of decision-making.
Publisher
IEEECitation
Hatamzad, Polanco Pinerez, Cesselgren. Using Slightly Imbalanced Binary Classification to Predict the Efficiency of Winter Road Maintenance. IEEE Access. 2021;9:160048-160063Metadata
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