Optimization of Evaluation Indicators for Driver’s Traffic Literacy: An Improved Principal Component Analysis Method

Zhuo Chen and Kang Tian

SAGE Open, 2022, vol. 12, issue 2, 21582440221105262

Abstract: The traditional traffic concept seems to be unable to adapt to the traffic problems brought by cities’ rapid development. People must cultivate new modern traffic literacy to deal with traffic problems. Based on traffic literacy, this paper constructs a traffic literacy evaluation indicator system including 13 evaluation indicators such as traffic rules and mechanical knowledge by summarizing relevant literature. We propose an Improved Principal Component Analysis (I-PCA) method, introduce the concept of information contribution sensitivity, and optimize and empower the traffic literacy indicator system. The primary research is to construct a traffic literacy evaluation indicator system including 13 evaluation indicators such as traffic rules and mechanical knowledge. The top 10 indicators that satisfy the cumulative information contribution rate value greater than 90% are retained, and the three indicators with low contribution rate are excluded. The optimization method can retain the indicator with a relatively large information contribution rate so that the indicator’s weight can genuinely reflect the information content of the corresponding indicator. The optimization method can retain the indicator with a relatively large information contribution rate so that the indicator’s weight can genuinely reflect the information content of the corresponding indicator.

Keywords: traffic literacy; evaluation indicator; principal component analysis; optimization; empowerment (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:12:y:2022:i:2:p:21582440221105262

DOI: 10.1177/21582440221105262

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