  The growth in availability of large-scale GPS mobility data from mobile
devices has the potential to aid traditional travel demand models (TDMs) such
as the four-step planning model, but those processing methods are not commonly
used in practice. In this study, we show the application of trip generation and
trip distribution modeling using GPS data from smartphones in the state of
Indiana. This involves extracting trip segments from the data and inferring the
phone users' home locations, adjusting for data representativeness, and using a
data-driven travel time-based cost function for the trip distribution model.
The trip generation and interchange patterns in the state are modeled for 2025,
2035, and 2045. Employment sectors like industry and retail are observed to
influence trip making behavior more than other sectors. The travel growth is
predicted to be mostly concentrated in the suburban regions, with a small
decline in the urban cores. Further, although the majority of the growth in
trip flows over the years is expected to come from the corridors between the
major urban centers of the state, relative interzonal trip flow growth will
likely be uniformly spread throughout the state. We also validate our results
with the forecasts of two travel demand models, finding a difference of 5-15%
in overall trip counts. Our GPS data-based demand model will contribute towards
augmenting the conventional statewide travel demand model developed by the
state and regional planning agencies.
