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相关概念视频

Stereotype Content Model02:16

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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在数据受限制的场景中实现CMF估计:语义编码知识挖掘模型

Yanlin Qi1, Jia Li2, Michael Zhang1

  • 1Institute of Transportation Studies, University of California, Davis, CA 95616, USA.

Accident; analysis and prevention
|June 19, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种使用自然语言处理 (NLP) 来预测崩修改因子 (CMF) 的新框架. 这种方法通过减少对大量事故数据的依赖来提高道路安全评估.

关键词:
撞车修改因子 (CMF) 预测预测知识开采知识开采.机器学习是机器学习.自然语言处理自然语言处理.道路交通安全问题 道路安全问题

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科学领域:

  • 运输工程 运输工程
  • 道路安全分析 道路安全分析
  • 数据科学数据科学数据科学

背景情况:

  • 准确的碰撞修改因子 (CMF) 对道路安全处理评估和基础设施投资至关重要.
  • 传统的CMF估计方法在很大程度上依赖于特定站点的崩数据,这限制了它们对新实施的应用,并阻碍了知识转移.
  • 现有的方法难以探索各种安全对策场景之间的内在相似性.

研究的目的:

  • 开发一种新的知识挖掘框架,用于预测机修改因子 (CMF).
  • 减少对广泛的撞车数据和手动收集对CMF估计的依赖.
  • 利用自然语言处理 (NLP) 来从现有的CMF知识中提取模式,并建模场景-CMF关系.

主要方法:

  • 开发了一个以人类理解为灵感的知识挖掘框架.
  • 应用先进的自然语言处理 (NLP) 技术,从非结构化的对策场景中提取变化和模式.
  • 将场景编码为机器可读的表示形式,以建模场景与CMF值之间的关系.
  • 通过CMF清算中心的现实数据验证了框架.

主要成果:

  • 与基线方法相比,新框架显著提高了CMF预测的准确性.
  • 证明了数据驱动方法在补充特定案例CMF估计方面的有效性.
  • 展示了利用积累的运输知识来改进道路安全分析的能力.

结论:

  • 拟议的基于NLP的框架为CMF估计提供了成本效益和适应性的解决方案,特别是当碰撞数据有限时.
  • 这种方法通过探索不同安全对策情景之间的联系,提高了CMF知识的可转移性.
  • 该研究为在各种道路安全应用中利用现有的运输知识开辟了新的途径.