Analysis of Road Roughness and Driver Comfort in 'Long-Haul' Road Transportation Using Random Forest Approach
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View abstract on PubMed
Summary
This summary is machine-generated.Rough roads significantly decrease long-haul driver comfort and increase safety risks. Improving road surfaces through maintenance is crucial for driver well-being and transportation efficiency.
Area Of Science
- Transportation Engineering
- Machine Learning Applications
- Human Factors in Driving
Background
- Long-haul transportation is vital for global trade, but driver comfort is often overlooked.
- Road roughness negatively impacts driver comfort, leading to fatigue, distraction, and accidents.
Purpose Of The Study
- To investigate the relationship between road roughness and driver comfort using machine learning.
- To identify the influence of road conditions on driver well-being and safety.
Main Methods
- Utilized Random Forest regression, Support Vector Regression, and XGBoost models.
- Collected 1,048,576 data points on vehicle acceleration and International Roughness Index (IRI) via the MIRANDA mobile application.
- Compared the performance of different machine learning models for real-time application and accuracy.
Main Results
- A significant correlation was found between increased road roughness and decreased driver comfort.
- Rougher roads correlated with higher vertical acceleration (SD-0.73) and lower comfort levels (Mean-10.01, SD-0.64).
- Random Forest was selected for its real-time deployability and lower memory usage, despite XGBoost's superior training speed and accuracy.
Conclusions
- Smooth road surfaces and effective maintenance are essential for enhancing road safety and reducing driver fatigue.
- Data-driven insights can inform transportation authorities and policymakers to improve road conditions and driver welfare.
- Prioritizing road quality supports efficient transportation and the well-being of long-haul drivers.
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