Constructing a comprehensive assessment indicator set: an overview analysis of train driver performance indicators based on on-train-data-recorder (OTDR) data

The rapid development and application of train data recorders have introduced new approaches for assessing train driver performance. Extracting key performance indicators from on-train-data-recorder (OTDR) data enhances performance appraisals by offering more robust metrics. The highly objective and real-time nature of OTDR data enables a comprehensive assessment of train drivers' actual operational performance under varied operating conditions. Although scholars have already explored and analyzed OTDR data for the railway transport industry, there is a relative paucity of literature on this subject, indicating a need for further research. Existing studies often present performance indicators in isolation, overlooking the benefits of a unified approach to comprehensive assessment. Moreover, the scattered distribution of indicators can lead to issues of duplication or interdependent relationships, which limits the full potential of OTDR data in assessing driver performance. This paper explores the necessity and feasibility of constructing an integrated driver performance assessment indicator system, utilizing OTDR data through a systematic literature review. Additionally, the study synthesizes findings from existing research to formulate a comprehensive set of performance indicators. The study concludes with an identification of the potential and limitations of OTDR data, providing insights essential for formulating consistent evaluation criteria, enhancing the scientific validity of the assessments, and guiding practical applications.

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Published
2024-08-23
DOI
https://doi.org/10.1145/3695652.3695697
Primary Topic
Human-Automation Interaction and Safety
Type
article
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article

Constructing a comprehensive assessment indicator set: an overview analysis of train driver performance indicators based on on-train-data-recorder (OTDR) data

Jingyu Zhang, Xiaoyu Li, Lei Shi, Chenxi Zhao et al.
Human-Automation Interaction and Safety
article

Constructing a comprehensive assessment indicator set: an overview analysis of train driver performance indicators based on on-train-data-recorder (OTDR) data

Jingyu Zhang, Xiaoyu Li, Lei Shi, Chenxi Zhao, Li Wenlu, Huining An, Han Zhang
article en

Abstract

The rapid development and application of train data recorders have introduced new approaches for assessing train driver performance. Extracting key performance indicators from on-train-data-recorder (OTDR) data enhances performance appraisals by offering more robust metrics. The highly objective and real-time nature of OTDR data enables a comprehensive assessment of train drivers' actual operational performance under varied operating conditions. Although scholars have already explored and analyzed OTDR data for the railway transport industry, there is a relative paucity of literature on this subject, indicating a need for further research. Existing studies often present performance indicators in isolation, overlooking the benefits of a unified approach to comprehensive assessment. Moreover, the scattered distribution of indicators can lead to issues of duplication or interdependent relationships, which limits the full potential of OTDR data in assessing driver performance. This paper explores the necessity and feasibility of constructing an integrated driver performance assessment indicator system, utilizing OTDR data through a systematic literature review. Additionally, the study synthesizes findings from existing research to formulate a comprehensive set of performance indicators. The study concludes with an identification of the potential and limitations of OTDR data, providing insights essential for formulating consistent evaluation criteria, enhancing the scientific validity of the assessments, and guiding practical applications.

Institute of Psychology, Chinese Academy of Sciences (CN), China Railway Group (China) (CN), University of Chinese Academy of Sciences (CN), Southwest Jiaotong University (CN)
Openalex Percentile: Top 77%
Human-Automation Interaction and Safety
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