Development of a Road Surface Conditions Prediction Model for Snow Removal Decision-Making

Gyeonghoon Ma, Min-Cheol Park, Junchul Kim, Han Jin Oh and Jin-Hoon Jeong ()
Additional contact information
Gyeonghoon Ma: Division of Safety and Infrastructure Research, Seoul Institute, Seoul 03909, Republic of Korea
Min-Cheol Park: Division of Safety and Infrastructure Research, Seoul Institute, Seoul 03909, Republic of Korea
Junchul Kim: Division of AI and Big Data Research, Seoul Institute, Seoul 03909, Republic of Korea
Han Jin Oh: Division of Safety and Infrastructure Research, Seoul Institute, Seoul 03909, Republic of Korea
Jin-Hoon Jeong: Department of Civil Engineering, Inha University, Incheon 22212, Republic of Korea

Sustainability, 2025, vol. 17, issue 19, 1-23

Abstract: Snowfall and road surface freezing cause traffic disruptions and skidding accidents. When widespread extreme cold events or sudden heavy snowfalls occur, the continuous monitoring and management of extensive road networks until the restoration of traffic operations is constrained by the limited personnel and resources available to road authorities. Consequently, road surface condition prediction models have become increasingly necessary to enable timely and sustainable decision-making. This study proposes a road surface condition prediction model based on CCTV images collected from roadside cameras. Three databases were constructed based on different definitions of moisture-related surface classes, and models with the same architecture were trained and evaluated. The results showed that the best performance was achieved when ice and snow were combined into a single class rather than treated separately. The proposed model was designed with a simplified structure to ensure applicability in practical operations requiring computational efficiency. Compared with transfer learning using deeper and more complex pre-trained models, the proposed model achieved comparable prediction accuracy while requiring less training time and computational resources. These findings demonstrate the reliability and practical utility of the developed model, indicating that its application can support sustainable snow removal decision-making across extensive road networks.

Keywords: road surface condition; convolutional neural networks; class definition; snow removal; decision-making (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/19/8794/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/19/8794/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:19:p:8794-:d:1762085

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().