Since they're using a small geography--block groups--spatial autocorrelation may also be an issue. Local Indicators of Spatial Autocorrelation (LISA) at the level of each city, then all together could be helpful. Also, since block groups are often separated by street centerlines, transit trip characteristics may get inappropriately split between block groups, muddling effects. Geographically weighted regression may help address autocorrelation, but has several requirements.
Anselin, L. (1995). Local Indicators of Spatial Association-LISA. Geographical Analysis, 27(2).
Anselin, L., Syabri, I., & Kho, Y. (2006). GeoDa: An Introduction to Spatial Data Analysis. Geographical Analysis, 38(1), 5–22. https://doi.org/10.1111/j.0016-7363.2005.00671.x
Leung, Y., Mei, C. L., & Zhang, W. X. (2000). Statistical tests for spatial nonstationarity based on the geographically weighted regression model. Environment and Planning A, 32(1), 9-32.