Cities often lack up-to-date data analytics to evaluate and implement
transport planning interventions to achieve sustainability goals, as
traditional data sources are expensive, infrequent, and suffer from data
latency. Mobile phone data provide an inexpensive source of geospatial
information to capture human mobility at unprecedented geographic and temporal
granularity. This paper proposes a method to estimate updated mode of
transportation usage in a city, with novel usage of mobile phone application
traces to infer previously hard to detect modes, such as bikes and
ride-hailing/taxi. By using data fusion and matrix factorisation, we integrate
socioeconomic and demographic attributes of the local resident population into
the model. We tested the method in a case study of Santiago (Chile), and found
that changes from 2012 to 2020 in mode of transportation inferred by the method
are coherent with expectations from domain knowledge and the literature, such
as ride-hailing trips replacing mass transport.Comment: 19 pages, 8 figure