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The rise of location positioning technologies has generated enormous volumes of digital footprints. Translating this big data into understandable trip patterns plays a crucial role in estimating infrastructure demands. Previous studies were unable to correctly represent commuting patterns on smaller urban scales due to insufficient spatial accuracy. In this study, we investigated if, and to what extent, estimated commuting patterns identified from GPS data can replicate the results from transportation surveys and to what degree these estimates improve the estimates of trips distribution pattern on census tract level using higher resolution data. We inferred average daily home-to-work trips by analyzing phone GPS data and compared these patterns with U.S. Census summary tables. We found that trips detected by GPS data highly correlate with census trips. Furthermore, GPS data is a better proxy for Census tract-pairs with larger numbers of trips.

Estimating Commuting Patterns from High Resolution Phone GPS Data