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This paper presents a methodology for actively discovering knowledge in transport hotline databases by analyzing complaints reported by citizens, aiming to assist transportation management departments in planning actions to investigate and improve service quality. The proposed model uses text mining techniques and applies latent Dirichlet allocation (LDA) to identify topics that are related to transportation services. Consequently, we actively analyzed over 230,000 phone calls occurring in a certain province between 1st January and 31st December 2021. Specifically, we actively analyzed nearly 22,000 phone calls about the taxi industry within a selected city, and identified six topics, including lost and found (27.1%), car blocking (20.6%), attitude and behavior (17.1%), online car-hailing (12.8%), illegal operations (11.2%), and fare issues (11.2%). By actively referring to past and ongoing best practices, we actively recommend several policy implications. The proposed method thus actively transforms the service center record into a customer feedback-based assessment system to intently monitor drivers’ professionalism while efficiently addressing customers’ complaints and concerns.