  While benefiting people's daily life in so many ways, smartphones and their
location-based services are generating massive mobile device location data that
has great potential to help us understand travel demand patterns and make
transportation planning for the future. While recent studies have analyzed
human travel behavior using such new data sources, limited research has been
done to extract multimodal travel demand patterns out of them. This paper
presents a data-driven analytical framework to bridge the gap. To be able to
successfully detect travel modes using the passively collected location
information, we conduct a smartphone-based GPS survey to collect ground truth
observations. Then a jointly trained single-layer model and deep neural network
for travel mode imputation is developed. Being "wide" and "deep" at the same
time, this model combines the advantages of both types of models. The framework
also incorporates the multimodal transportation network in order to evaluate
the closeness of trip routes to the nearby rail, metro, highway and bus lines
and therefore enhance the imputation accuracy. To showcase the applications of
the introduced framework in answering real-world planning needs, a separate
mobile device location data is processed through trip end identification and
attribute generation, in a way that the travel mode imputation can be directly
applied. The estimated multimodal travel demand patterns are then validated
against typical household travel surveys in the same Washington D.C. and
Baltimore Metropolitan Regions.
