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While benefiting people's daily life in so many ways, smartphones and theirlocation-based services are generating massive mobile device location data thathas great potential to help us understand travel demand patterns and maketransportation planning for the future. While recent studies have analyzedhuman travel behavior using such new data sources, limited research has beendone to extract multimodal travel demand patterns out of them. This paperpresents a data-driven analytical framework to bridge the gap. To be able tosuccessfully detect travel modes using the passively collected locationinformation, we conduct a smartphone-based GPS survey to collect ground truthobservations. Then a jointly trained single-layer model and deep neural networkfor travel mode imputation is developed. Being "wide" and "deep" at the sametime, this model combines the advantages of both types of models. The frameworkalso incorporates the multimodal transportation network in order to evaluatethe closeness of trip routes to the nearby rail, metro, highway and bus linesand therefore enhance the imputation accuracy. To showcase the applications ofthe introduced framework in answering real-world planning needs, a separatemobile device location data is processed through trip end identification andattribute generation, in a way that the travel mode imputation can be directlyapplied. The estimated multimodal travel demand patterns are then validatedagainst typical household travel surveys in the same Washington D.C. andBaltimore Metropolitan Regions.

A Data-Driven Analytical Framework of Estimating Multimodal Travel Demand Patterns using Mobile Device Location Data