Taking the rail transit transfer stations in Qingyang, Wuhou, and Chenghua Districts of Chengdu as the research objects, this study constructs a static-dynamic coupled analytical framework by integrating the input-oriented three-stage super-efficiency SBM model and the output-oriented Malmquist-Luenberger (ML) index to systematically evaluate rail transit transfer efficiency. The findings reveal that the transfer efficiency of Chengdu Metro exhibited a fluctuating growth pattern from 2017 to 2023, with significant variations corresponding to periods of network expansion and operational adjustments. Improvements in technical efficiency and management optimization have been key drivers of overall efficiency gains. The three-stage super-efficiency SBM model effectively filters out the impacts of environmental variables and random noise, uncovering inter-station efficiency disparities and resource redundancy issues. Decomposition of the ML index indicates that both technical efficiency and technological progress jointly drive total factor productivity (TFP) changes. On average, technical efficiency has been the more stable and prominent contributor to productivity growth. However, the reasons for TFP declines at certain stations are varied; some under-performed due to lagging technological progress, while others faced constraints in technical or scale efficiencies. The study confirms that the synergistic application of the three-stage model and the ML index can accurately identify bottlenecks and provide theoretical support and practical pathways for optimizing resource allocation and dynamic management in urban rail transit systems. Findings and methods from Chengdu’s practice provide a replicable paradigm for evaluating, planning and optimizing rail transit transfer hubs in Chinese cities at different development stages, and offer empirical references for advancing urban public transport and sustainable development of comprehensive transportation systems.
Wang et al. (Sat,) studied this question.