Optimisation of Driver’s Traffic Literacy Evaluation Index from the Perspective of Information Contribution Sensitivity

Abstract

The practice has proven that relying solely on large-scale transportation facilities cannot fundamentally alleviate urban transportation problems. Since the motor vehicle drivers are the main participants of transportation, they should improve their knowledge of transportation. Moreover, the drivers should equally cope with transportation problems. This paper establishes an evaluation index system for traffic literacy of urban drivers. In addition, it proposes a method of information contribution sensitivity to optimise the index system. The main results and achievement of this paper include the following: (1) A traffic literacy evaluation index system including 13 evaluation indexes such as traffic rules and general knowledge of machinery has been constructed. (2) Based on the calculation results of the sensitivity of the information contribution, the first 10 indexes that satisfy the cumulative information contribution rate’s value of greater than 90% are retained and 3 indexes with lower contribution rates are eliminated. This study provides a theoretical framework and basic methods to evaluate the traffic literacy of urban drivers.

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

“… Source . Wang and Tian (2021). Note. The weights in the last column correspond to the indicators in the second column. …”

“…The drivers are the most direct participant in traffic, and they not only need to consider the safety of passengers and the safety of other people and economic property around Source. Wang and Tian (2021).…”

“…The indicator’s Information contribution rate reflects the indicator’s information contribution to the overall evaluation of traffic literacy and can be used as an essential basis for indicator optimization. This study draws on the method of Wang and Tian (2021), and the specific optimization steps are as follows.…”

“…The eigenvectors of the orthogonally normalized correlation coefficient matrix Source. Wang and Tian (2021).…”

“…Indicator Screening and Weighting Based on Cumulative Information Contribution Sensitivity Wang and Tian (2021)…”