  This study models and examines commuter's preferences for short-distance
transportation modes, namely: walking, taking a bus or riding a metro. It is
used a unique dataset from a large-scale field experiment in Singapore that
provides rich information about tens of thousands of commuters' behavior. In
contrast to the standard approach, this work does not relay on survey data.
Conversely, the chosen transportation modes are identified by processing raw
data (latitude, longitude, timestamp). The approach of this work exploits the
information generated by the smart transportation system in the city that make
suitable the task of obtaining granular and nearly real-time data. Novel
algorithms are proposed with the intention to generate proxies for walkability
and public transport attributes. The empirical results of the case study
suggest that commuters do no differentiate between public transport choices
(bus and metro), therefore possible nested structures for the public transport
modes are rejected.

Physics and Society;Machine Learning