Curriculum System Innovation and Teaching Practice for Digital-Intelligent Rail Transit Operation Talents in the Guangdong-Hong Kong-Macao Greater Bay Area
- 1 Guangzhou Railway Polytechnic, Guangzhou, China
- 2 Guangzhou Railway Polytechnic, Guangzhou, China
Abstract
Against the background of rapid digital-intelligent transformation of rail transit in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), traditional vocational education curricula for rail transit operation are difficult to match the strong demand for composite talents with professional operation capabilities and digital technologies. Based on the collaborative education concept of government-university-enterprise-research, this paper constructs a competency-oriented modular curriculum system and carries out systematic teaching reforms. A three-dimensional competency model including technical capability, management capability and professional literacy is established through enterprise demand surveys and the Delphi method. A “1 + 3 + N” modular curriculum structure is designed to integrate general core courses, regional characteristic modules and digital-intelligent micro-majors. Supporting reforms include virtual simulation teaching, enterprise dual-tutor system, project-driven teaching and a multi-dimensional evaluation mechanism. Empirical verification is conducted on 326 sophomores and juniors (2022 and 2023 grades) majoring in Rail Transit Operation Management of Guangzhou Railway Polytechnic (stratified random sampling based on academic performance, gender and professional interest, with no significant difference in baseline competency test scores, P > 0.05) and 18 rail transit enterprises in the GBA selected by purposive sampling (covering core business types of high-speed railway, intercity railway and urban subway, including state-owned, Hong Kong SAR-funded and mixed-ownership enterprises). The results show that the reform improves students’ digital technology application ability by 37.2% (p < 0.001, Cohen’s d = 1.89), professional operation ability by 26.5% (p < 0.001, Cohen’s d = 1.52), post adaptability by 42.5% (p < 0.001, Cohen’s d = 2.13), and enterprise satisfaction reaches 94.3%. This study provides a replicable and promotable curriculum reform scheme for vocational colleges cultivating rail transit digital-intelligent talents, and promotes the deep integration of education chain, talent chain and industrial chain.
- İnaç, H. (2022). Digital Transformation Model for intelligent Transportation Systems ( iTS ) in Smart Cities . Ph.D. Thesis, Marmara Universitesi (Türkiye).
- Liu, G., Huang, J., & Zhu, T. (2025). Assessment and Suggestions on the Digital Transformation Path of Guangzhou’s Smart Transportation. Innovative Applications of AI, 2, 111-121. https://doi.org/10.70695/aa1202502a08
- Tong, X., Sun, Z., & Sun, L. (2019). Research on Digital Intelligent Scheduling of Urban Rail Vehicle Mixed-Model Assembly Line. In 2019 IEEE International Conference on Smart Manufacturing, Industrial & Logistics Engineering (SMILE) (pp. 200-204). IEEE. https://doi.org/10.1109/smile45626.2019.8965275
- Wang, J., & Lu, X. (2026). Railway Transport Enterprises: Operational Realities, Strategic Challenges, and Future Pathways. In J. Wang, & M. Song (Eds.), Resources, Climate and Sustainable Development (pp. 21-40). Springer. https://doi.org/10.1007/978-981-95-5289-4_2
- Wang, K., Zhou, X., & Guan, J. (2025a). The Construction of an Integrated Cloud Network Digital Intelligence Platform for Rail Transit Based on Artificial Intelligence. Scientific Reports, 16, Article No. 393. https://doi.org/10.1038/s41598-025-29732-6
- Wang, Z., Zhou, S., Zhuang, K., Chen, H., Yang, N., & Liu, L. (2025b). From Vision to Application: Development and Future Roadmap for Digital Intelligent Resilient Grid Technologies. In 2025 IEEE 3rd International Conference on Power Science and Technology (ICPST) (pp. 1214-1220). IEEE. https://doi.org/10.1109/icpst65050.2025.11089443
- Yang, H., Lu, J., Lu, H., Gao, Y., Liu, X., & Liu, H. (2022). Key Technologies of Low-Carbon-Oriented Intelligent Travel Service for Urban Rail Transit Based on Maas. In International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2021) (Vol. 12165, pp. 172-176). SPIE. https://doi.org/10.1117/12.2628002
- Yuan, C., Liu, J., & Fan, Y. (2025). Exploring the Dynamics of Urban Digital Intelligent Transformation: Sustainable Development through the National AI Innovation Pilot Zone. Environment, Development and Sustainability, 1-36. https://doi.org/10.1007/s10668-025-06526-4
- Zheng, H. (2021). Research and Analysis on the Application of Digital Twin Technology in Urban Rail Transit. In 2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC) (pp. 1067-1070). IEEE. https://doi.org/10.1109/ipec51340.2021.9421186