Intelligent Road Icing Early Warning System Based On Machine Learning

被引：0
作者：
Rao Zhongyang [1 ]
Feng Chunyuan [2 ]
Liu Wenjiang [2 ]
机构：
[1] Shandong Jiaotong Univ, Shandong Teaching Steering Comm, Jinan, Shandong, Peoples R China
[2] Shandong Jiaotong Univ, Jinan, Shandong, Peoples R China
来源：
ENGINEERING LETTERS | 2024年 / 32卷 / 04期
关键词：
Index Terms-Road Icing Predicting; Machine Learning (ML); Road Surface Temperature; Long Short-Term Memory (LSTM);
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
the winter seasons, road icing is amongst the most significant threats towards road safety, which is considered as a super dangerous weather condition. This study aims to optimize road deicing predictions using machine learning (ML) techniques. By collecting data from sensors including pavement temperature (PT), pavement friction coefficient (PFC), pavement condition (PC), thickness of water film (TWF), freezing temperature, ice content (IC) and ice warning value (IWV), we analyzed crucial parameters affecting road deicing - road surface temperature. Predicting pavement icing (PI) is critical in the transportation field. To achieve this, we utilized the Long Short -Term Memory (LSTM) ML model to estimate icing conditions on the 2nd Ring South Road in Jinan, China. By considering relevant parameters of the road surface within a specific timeframe, we attempted to forecast road temperature, providing a novel approach for predicting road icing. Experimental results demonstrated the model's ability to accurately predict icing conditions. Furthermore, by utilizing observed data from the current road condition, we were able to precisely predict road temperature and thereby forecast road icing occurrences.
引用
收藏
页码：806 / 811
页数：6
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