Choice of public transport technologies in cities is not straightforward: while the academy focuses on optimization models to determine which modes should a specific city have, policy makers rely on simple recommendations which are based on city population and income. We estimated six types of classification models that could allow for more precise recommendations yet are simple enough to be applied by the authorities. We considered typical variables as population and Gross Domestic Product of cities but also geographic and morphologic characteristics in a database of 400 cities from North and South America. Ordered Probit and Multinomial Logit models were the most accurate, with a success rate over 80% in the validation subset. Among the explanatory variables, city population and GDP per capita were as expected the most significant, but fare integration, car ownership and city shape were also relevant. Even if existent public transport modes in cities are not necessarily optimal, the classification models developed can give an insight for policy makers, in the sense that cities whose public transportation complexity cannot be explained by the models are more likely to have a suboptimal public transportation system.