A Temporal Investigation of Crash Severity Factors in Worker-Involved Work Zone Crashes: Random Parameters and Machine Learning Approaches:
Reason:
In the context of work zone safety, worker presence and its impact on crash severity has been less explored. Moreover, there is a lack of research on contributing factors by time-of-day. To accomplish this, first a mixed logit model was used to determine statistically significant crash severity contributing factors and their effects. Significant factors in both models included work-zone-specific characteristics and crash-specific characteristics, where environmental characteristics were only significant in the daytime model. In addition, results from parameter transferability test provided evidence that daytime and nighttime crashes need to be modeled separately. Further, to explore the nonlinear relationship between crash severity levels and time-of-day, as well as compare the effects of variables to that of the logit model and assess prediction performance, a Support Vector Machines (SVM)
What is meant by machine learning approach?
Machine Learning is an AI technique that teaches computers to learn from experience. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model.
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Answer: 40 years of age
Explanation:
In the absence of age as a bona fide occupational qualification (BFOQ), the Age Discrimination in Employment Act of 1967 prohibits discrimination against an individual who is 40 years of age or older
This discrimination is base on age because it is unlawful to discriminate against a person because of his/her age with respect to any term, condition, or privilege of employment, including hiring, firing, promotion, layoff, compensation, benefits, job assignments, and training. The ADEA permits employers to favour older workers based on age even when doing so affect in return the younger worker.
This Act does not support the discrimination of age when it comes to job applications