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Mobile Device Location Data (MDLD) has been popularly utilized in variousfields. Yet its large-scale applications are limited because of either biasedor insufficient spatial coverage of the data from individual data vendors. Oneapproach to improve the data coverage is to leverage the data from multipledata vendors and integrate them to build a more representative dataset. Fordata integration, further treatments on the multi-sourced dataset are requireddue to several reasons. First, the possibility of carrying more than one devicecould result in duplicated observations from the same data subject.Additionally, when utilizing multiple data sources, the same device might becaptured by more than one data provider. Our paper proposes a data integrationmethodology for multi-sourced data to investigate the feasibility ofintegrating data from several sources without introducing additional biases tothe data. By leveraging the uniqueness of travel pattern of each device,duplicate devices are identified. The proposed methodology is shown to becost-effective while it achieves the desired accuracy level. Our findingssuggest that devices sharing the same imputed home location and the top fivemost-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 theaforementioned attribute in common are observed at the same locationsimultaneously. Finally, the proposed algorithm has been successfully appliedto the national-level MDLD of 2020 to produce the national passengerorigin-destination data for the NextGeneration National Household Travel Survey(NextGen NHTS) program.

A Big Data Driven Framework for Duplicate Device Detection from Multi-sourced Mobile Device Location Data