Using GPS Data to Understand
Driving Behavior
Joe Grengs, Xiaoguang Wang, and Lidia Kostyniuk
T
RAVEL-DEMAND modeling is evolving away from
relying on crude data aggregated at large urban zones
toward using highly disaggregated approaches where
individual travelers are modeled by interacting with fine-grained
spatial settings represented by parcel data. And the conceptual
framework that supports travel-demand modeling is moving away
from understanding travel as a series of trips toward a view of
people interacting with their surroundings as a series of activities.
This evolution in travel-demand modeling has given rise to analysis that combines highly detailed travel data collected through
global positioning systems (GPS) with techniques in geographic
information systems (GIS). Collecting travel-behavior data by
GPS offers several important advantages over conventional trip
diary surveys: GPS data can be collected over much longer
periods of time than the typical two-day diary; they do not rely
on the memory and estimates of a survey respondent; and they
provide linkages among complex trips, tours, and daily travel patterns. The most important advantage of GPS data is that they allow
us to address the dynamic properties of travel behavior by capturing detailed spatial, temporal, and attribute conditions throughout
the full length of the traveling experience. Unlike conventional
travel diaries that provide no information between origins and destinations, GPS data offer insights into the traveler’s choices and
decisions while en route. However, despite these advantages,