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Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
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When a solid cylinder rolls steadily on a rigid surface, the normal force applied by the surface on the cylinder is perpendicular to the tangent at the contact point. However, since no materials are entirely rigid, the surface's reaction to the cylinder involves a range of normal pressures.
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Related Experiment Video

Updated: Sep 5, 2025

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Applying machine learning to construct braking emission model for real-world road driving.

Ning Wei1, Zhengyu Men1, Chunzhe Ren1

  • 1Tianjin Key Laboratory of Urban Transport Emission Research & State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China.

Environment International
|July 8, 2022
PubMed
Summary

Vehicle brake emissions are rising. A new machine learning model accurately predicts PM2.5 brake emissions using brake energy intensity and metal content, outperforming existing models and offering solutions for emission control.

Keywords:
Brake emission modelMachine learningPM(2.5)Real road conditions

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Area of Science:

  • Environmental Science
  • Transportation Engineering
  • Data Science

Background:

  • Vehicle brake emissions are a growing concern, yet under-researched compared to exhaust emissions.
  • Real-world data on brake emission intensity and characteristics is limited.
  • Existing models like MOVES show limitations in accurately predicting brake emissions across different road types.

Purpose of the Study:

  • To develop a predictive model for PM2.5 brake emissions using real-world braking data.
  • To identify key features influencing brake emission intensity.
  • To compare the performance of machine learning models against traditional methods and provide insights for emission control.

Main Methods:

  • A dataset of 600 real-world braking events was created.
  • Five algorithms, including multiple linear regression and machine learning, were used to map segment features to PM2.5 emission intensity.
  • Categorical Boosting (CatBoost) was selected for its superior prediction performance.

Main Results:

  • Brake energy intensity (BEI) and metal content (MC) were identified as the most significant predictors of brake emissions.
  • The CatBoost model achieved a mean R² of 0.83 and RMSE of 0.039 in tenfold cross-validation.
  • The CatBoost model demonstrated superior accuracy over the MOVES model, with MOVES overpredicting on urban roads and underpredicting on motorways.

Conclusions:

  • Machine learning, particularly CatBoost, offers a powerful tool for modeling and predicting vehicle brake emissions.
  • BEI and MC have a non-linear, monotonic increasing relationship with braking emissions.
  • Strategies like promoting smoother driving and using low-metal brake pads can effectively reduce brake emissions.