Black Ice Formation Prediction Model Based on Public Data in Land, Infrastructure and Transport Domain


KIPS Transactions on Software and Data Engineering, Vol. 10, No. 7, pp. 257-262, Jul. 2021
https://doi.org/10.3745/KTSDE.2021.10.7.257,   PDF Download:
Keywords: Black Ice, Public Data, Data Cleansing, Machine Learning, prediction
Abstract

Accidents caused by black ice occur frequently every winter, and the fatality rate is very high compared to other traffic accidents. Therefore, a systematic method is needed to predict the black ice formation before accidents. In this paper, we proposed a black ice prediction model based on heterogenous and multi-type data. To this end, 12,574,630 cases of 46 types of land, infrastructure, transport public data and meteorological public data were collected. Subsequently, the data cleansing process including missing value detection and normalization was followed by the establishment of approximately 600,000 refined datasets. We analyzed the correlation of 42 factors collected to predict the occurrence of black ice by selecting only 21 factors that have a valid effect on black ice prediction. The prediction model developed through this will eventually be used to derive the route-specific black ice risk index, which will be utilized as a preliminary study for black ice warning alart services.


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Cite this article
[IEEE Style]
J. H. Na, S. Yoon, H. Oh, "Black Ice Formation Prediction Model Based on Public Data in Land, Infrastructure and Transport Domain," KIPS Transactions on Software and Data Engineering, vol. 10, no. 7, pp. 257-262, 2021. DOI: https://doi.org/10.3745/KTSDE.2021.10.7.257.

[ACM Style]
Jeong Ho Na, Sung-Ho Yoon, and Hyo-Jung Oh. 2021. Black Ice Formation Prediction Model Based on Public Data in Land, Infrastructure and Transport Domain. KIPS Transactions on Software and Data Engineering, 10, 7, (2021), 257-262. DOI: https://doi.org/10.3745/KTSDE.2021.10.7.257.