United States Department of Transportation
i
The Promise and Limitations of Locational App Data for Origin-Destination Analysis: A Case Study
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2017-10-01
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[PDF-1.26 MB]
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Abstract:Large, passively collected datasets from location-based services are a potential asset to transportation planning and modeling. These data have near-real-time availability and can capture travel behavior of a wide swath of the population. These data clearly hold promise for multiple transportation modeling methods, but practitioners should be aware of and account for various biases and other gaps inherent to the data. This volume seeks to further understand these gaps by comparing data from one large passively-collected data provider, Cuebiq, to data collected during a smartphone-based GPS household travel survey. As part of this comparison, the authors developed an algorithm to infer trips from the Cuebiq location data and identified smartphone users present in both datasets. Results from this comparison identify potential gaps in Cuebiq's representation of travel behavior, including demographic biases regarding traveler age and income and a bias toward trips longer than 9 miles (15 kilometers). The comparison also highlights the promising capability to capture detailed location behavior for a wide swath of the population, given the Cuebiq dataset's pervasive spatial coverage. This volume also summarizes persisting uncertainties regarding data from location-based services and describes potential future work to measure and account for inherent biases as these data are introduced to planning and modeling applications.
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