RESEARCH ARTICLE
Bridge Frost Prediction Using K-nearest Neighbor Classifier
Jinhwan Jang1, *
Article Information
Identifiers and Pagination:
Year: 2023Volume: 17
E-location ID: e26671212263648
Publisher ID: e26671212263648
DOI: 10.2174/0126671212263648230921115634
Article History:
Received Date: 23/05/2023Revision Received Date: 22/07/2023
Acceptance Date: 26/07/2023
Electronic publication date: 18/10/2023
Collection year: 2023
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background:
Considering the frequent occurrence of accidents on icy bridges during winter nights, it would be advantageous to notify both road managers and drivers regarding the most perilous areas. This notification would allow road managers to address the icy conditions by applying de-icing substances, while drivers could be more adequately prepared for potential hazards.
Methods:
In this study, the focus was on investigating k-nearest neighbor algorithms to predict nighttime icing caused by frost on three distinct bridges located on the National Highways in Korea. The algorithms utilized atmospheric data as input, which was obtained from the weather agency's website through an open API. The input data included relative humidity, air temperature, and dew point temperature, as well as the disparities in air temperature and humidity between two consecutive days.
Results:
In order to assess the effectiveness of the prediction models, reference data were created using the fundamental principle that ice is formed when the temperature of the pavement is below freezing and lower than the dew point temperature. Consequently, the developed algorithm demonstrated favorable performance, achieving an accuracy of 95% when evaluated using a test dataset that occupies 30% of the entire data.
Conclusion:
Considering the increasing focus on preventive maintenance, these newly developed forecasting models can be employed proactively as a preventive measure against icing. This proactive approach will ultimately contribute to improving traffic safety on winter roads.
