  Mobile Device Location Data (MDLD) has been popularly utilized in various
fields. Yet its large-scale applications are limited because of either biased
or insufficient spatial coverage of the data from individual data vendors. One
approach to improve the data coverage is to leverage the data from multiple
data vendors and integrate them to build a more representative dataset. For
data integration, further treatments on the multi-sourced dataset are required
due to several reasons. First, the possibility of carrying more than one device
could result in duplicated observations from the same data subject.
Additionally, when utilizing multiple data sources, the same device might be
captured by more than one data provider. Our paper proposes a data integration
methodology for multi-sourced data to investigate the feasibility of
integrating data from several sources without introducing additional biases to
the data. By leveraging the uniqueness of travel pattern of each device,
duplicate devices are identified. The proposed methodology is shown to be
cost-effective while it achieves the desired accuracy level. Our findings
suggest that devices sharing the same imputed home location and the top five
most-visited locations during a month can represent the same user in the MDLD.
It is shown that more than 99.6% of the sample devices having the
aforementioned attribute in common are observed at the same location
simultaneously. Finally, the proposed algorithm has been successfully applied
to the national-level MDLD of 2020 to produce the national passenger
origin-destination data for the NextGeneration National Household Travel Survey
(NextGen NHTS) program.
