To identity the key factors affecting ridership share of urban rail transit（URT）， with the panel data of 14 cities from 2010 to 2021， the share of the URT ridership in the total public transit ridership is analyzed here to identify the key influencing factors. Considering the possible heterogeneities over city and year， a Beta regression model with city-level and yearly random effects is built to identify the key influencing factors from conventional bus， URT， and socio-economic aspects. The model is estimated with the integrated nested Laplace approximation method. The estimation results show that the number of conventional buses per 10 000 people， the URT length per 10 000 people， and the URT connectivity have significantly positive effects on the URT ridership share， while the length of bus routes per 10 000 people， GDP per capita， and road area per capita are insignificant. Besides， both city and year show significant random effects： the URT ridership shares are found to vary differently across cities， but they generally show an increasing trend during the survey period.