  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.
