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Aggregates Classification01:29

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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When two or more objects collide with each other, they can stick together to form one single composite object (after collision). The total mass of the object after the collision is the sum of the masses of the original objects, and it moves with a velocity dictated by the conservation of momentum. Although the system's total momentum remains constant, the kinetic energy decreases, and thus such a collision is an inelastic collision. Most of the collisions between objects in daily life are...
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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
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Force Classification01:22

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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Elastic Collisions: Case Study01:15

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Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
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Elastic Collisions: Introduction01:00

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An elastic collision is one that conserves both internal kinetic energy and momentum. Internal kinetic energy is the sum of the kinetic energies of the objects in a system. Truly elastic collisions can only be achieved with subatomic particles, such as electrons striking nuclei. Macroscopic collisions can be very nearly, but not quite, elastic, as some kinetic energy is always converted into other forms of energy such as heat transfer due to friction and sound. An example of a nearly...
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Improve Aggressive Driver Recognition Using Collision Surrogate Measurement and Imbalanced Class Boosting.

Ke Wang1,2,3, Qingwen Xue1, Yingying Xing1,2

  • 1College of Transportation Engineering, Tongji University, Shanghai 201804, China.

International Journal of Environmental Research and Public Health
|April 5, 2020
PubMed
Summary
This summary is machine-generated.

This study enhances aggressive driver recognition by using imbalanced class boosting algorithms. CUSBoost demonstrated superior performance in identifying risky driving behavior from vehicle trajectory data.

Keywords:
collision surrogate measurementdriving aggressivenessimbalanced class boostingvehicle trajectory

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Area of Science:

  • Machine Learning
  • Transportation Safety
  • Data Science

Background:

  • Real-time recognition of risky driving behavior is crucial for road safety.
  • Aggressive drivers are infrequent in traffic, posing a challenge for standard machine learning models.
  • Existing methods often struggle to accurately identify aggressive drivers due to imbalanced datasets.

Purpose of the Study:

  • To evaluate the effectiveness of imbalanced class boosting algorithms for aggressive driver recognition.
  • To introduce a novel surrogate measure for collision risk, Average Crash Risk (ACR).
  • To identify key features for distinguishing aggressive drivers from normal ones.

Main Methods:

  • Proposed Average Crash Risk (ACR) as a measure of collision risk.
  • Utilized three anomaly detection methods to determine driver aggressiveness based on ACR.
  • Trained and compared imbalanced class boosting algorithms (SMOTEBoost, RUSBoost, CUSBoost) against cost-sensitive AdaBoost and XGBoost.
  • Investigated the impact of resampling techniques on AdaBoost and XGBoost performance.

Main Results:

  • CUSBoost consistently achieved high performance, indicated by the Area Under Precision-Recall Curve (AUPRC).
  • Discrete Fourier coefficients of the gap were identified as a significant feature for aggressive driver detection.
  • Imbalanced class boosting algorithms showed advantages over standard methods in this specific task.

Conclusions:

  • Imbalanced class boosting algorithms, particularly CUSBoost, are effective for recognizing aggressive drivers.
  • The proposed ACR metric and identified features offer a promising approach for improving driver behavior analysis.
  • This research contributes to developing safer intelligent transportation systems.