Metro prosperity and bus decline: A causal relationship? A quasi-experimental study of 17 key cities in China

Understanding the dynamics of competition and cooperation between metro and bus systems is essential for optimizing urban public transport networks. Using data from 281 Chinese cities from 2010 to 2022, this study employs a quasi-natural experiment to analyze the relationship between metro and bus systems from a macro-network perspective. A dynamic network data envelopment analysis model quantifies the operational benefit of both transit modes in 17 developed cities. Propensity score matching evaluates the impact of network densities on operational benefit, serving as a heterogeneity analysis for developed cities. Finally, the difference in differences method is applied to a large sample to analyze the impact of metro network expansion on bus ridership and obtain generalized conclusions. The analysis shows that a 1 % increase in metro network density results in a 2.3 % decrease in bus ridership and a 1.4 % increase in total public transport ridership. In developed cities, the difference in bus operational benefit between cities with high and low metro network density is not significant. The average metro operational benefit in cities with high bus network density is 0.217 lower than in cities with low density. These findings highlight the importance of metro expansion in developed cities and suggest that redundancy within the bus network may detract from the effectiveness of metro and broader public transport systems. Coordinated metro expansion alongside strategic optimization of bus networks is recommended, highlighting the necessity for integrated multimodal public transport planning to promote sustainable urban transport development, particularly in cities where urban rail density has not yet reached a critical threshold. These insights provide valuable guidance for policymakers seeking to better understand the interactions between urban metro and bus systems in Chinese cities, supporting the development of optimized public transport priority policies.