Designing Transit Agency Job Descriptions for Optimal Roles: An Analytical Text-Mining Approach
Ahmadreza Mahmoudzadeh, Zachary Elgart, Sara Arezoumand, Todd C. Hansen, Subasish Das · International Conference on Transportation and Development 2020 · 2020
In an effort to help the transit industry prepare better job descriptions for staff positions, this study looks at various job descriptions posted by the human resources department of a transit agency. The goal of the study was to establish a baseline for developing the job descriptions for each staff position. The implemented method identified the major keywords and concepts within the job descriptions. By detecting the keywords and keyword frequency within the job descriptions, the split of responsibilities between each position is visualized. The study then performed a causality analysis to identify the patterns between ranking within the organization and the corresponding keywords. This nested analysis has been rarely utilized in the literature, and the hierarchical organization is the best-matched algorithm for applying these methods to create meaningful patterns. The methodology is applied to a case study, a transportation sub-department within a county government department in Texas housing nine administrative, managerial, or supervisory roles. The results of this analysis help transform the data into knowledge for decision-makers and transit agencies. Comparison of the analysis outcomes highlights complementary staff positions (to facilitate effective teaming and project management) and/or overlap in duties and responsibilities that may present an opportunity for increased efficiency.