  An overwhelming majority of the world's human population lives in urban areas
and cities. Understanding a city's transportation typology is immensely
valuable for planners and policy makers whose decisions can potentially impact
millions of city residents. Despite the value of understanding a city's
typology, labeled data (city and it's typology) is scarce, and spans at most a
few hundred cities in the current transportation literature. To break this
barrier, we propose a supervised machine learning approach to predict a city's
typology given the information in its Wikipedia page. Our method leverages
recent breakthroughs in natural language processing, namely sentence-BERT, and
shows how the text-based information from Wikipedia can be effectively used as
a data source for city typology prediction tasks that can be applied to over
2000 cities worldwide. We propose a novel method for low-dimensional city
representation using a city's Wikipedia page, which makes supervised learning
of city typology labels tractable even with a few hundred labeled samples.
These features are used with labeled city samples to train binary classifiers
(logistic regression) for four different city typologies: (i) congestion, (ii)
auto-heavy, (iii) transit-heavy, and (iv) bike-friendly cities resulting in
reasonably high AUC scores of 0.87, 0.86, 0.61 and 0.94 respectively. Our
approach provides sufficient flexibility for incorporating additional variables
in the city typology models and can be applied to study other city typologies
as well. Our findings can assist a diverse group of stakeholders in
transportation and urban planning fields, and opens up new opportunities for
using text-based information from Wikipedia (or similar platforms) as data
sources in such fields.
