In 2017, RSG evaluated traditional and emerging origin-destination data sources for the Federal Highway Administration’s Travel Model Improvement Program (TMIP). RSG compared a large dataset from Cuebiq, a national location-based data provider that leverages smartphone app data, to a household travel survey dataset collected via RSG’s proprietary smartphone app, rMove™. The work studied how representative big data from location-based services (LBS) was compared to the controlled random sample rMove survey data. RSG developed a trip inference algorithm and device-matching methodology to associate trips and user demographics of the rMove household travel survey dataset, with known travel and demographic details, with data and trips inferred from Cuebiq. Cuebiq data often excluded shorter-distance travel (trips under 10 minutes) but effectively captured travel over 30 minutes. The project also confirmed demographic bias in the Cuebiq data toward people under age 35. At the same time, the Cuebiq data’s relatively high penetration rate provided vast and complete spatial coverage not obtainable from survey data. The study found promise in blending these two types of data—combining the coverage and completeness of LBS data with the representativeness of survey data. A full report on the methodology and results will be released through TMIP in 2018.