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Related Experiment Videos

WTConv-TimesNet for Road Icing State Classification with IWOA-Based Hyperparameter Optimization.

Lingqiu Cui^1,2, Yuxun Ji³, Lijuan Zhang^1,2

¹Guizhou Communications Polytechnic University, Guiyang 551400, China.

Sensors (Basel, Switzerland)

|May 27, 2026

Summary

This summary is machine-generated.

A new model enhances road icing detection by integrating wavelet transform convolution (WTConv) and an Improved Whale Optimization Algorithm (IWOA) into TimesNet. This advanced system significantly improves accuracy and stability for winter road safety.

Keywords:

TimesNet hyperparameter optimization icing state classification multivariate time series road icing wavelet transform convolution

Related Experiment Videos

Area of Science:

Environmental Science
Artificial Intelligence
Road Safety Engineering

Background:

Road icing presents a significant challenge to winter road safety due to its complex, dynamic nature.
Accurate identification of road icing states from time-series data is difficult because of nonlinear evolution and multiscale temporal dependencies.

Purpose of the Study:

To develop an advanced road icing state classification model.
To improve the accuracy and stability of road icing detection in complex winter environments.

Main Methods:

A novel road icing classification model was developed by integrating wavelet transform convolution (WTConv) into the TimesNet architecture.
An Improved Whale Optimization Algorithm (IWOA) with Pearson correlation analysis was employed for hyperparameter optimization and feature selection.

Main Results:

The proposed IWOA-TimesNet-WTConv model achieved an overall accuracy of 98.83%, a significant improvement from the baseline TimesNet model's 92.72%.
Feature selection further enhanced overall accuracy by 1.04% and improved the Macro F1-score from 0.9691 to 0.9809.
The model demonstrated stable discrimination under transitional icing conditions, reducing data redundancy.

Conclusions:

The developed data-driven model offers a practical and effective solution for intelligent road icing monitoring and early warning systems.
The integration of WTConv and IWOA enhances the model's ability to capture complex temporal dynamics and optimize feature extraction for road icing classification.