Performance Evaluation of GPS Trajectory Rasterization Methods
Necip Enes Gengec Ergin Tari
The availability of the Global Positioning System (GPS) trajectory data is
increasing along with the availability of different GPS receivers and with the
increasing use of various mobility services. GPS trajectory is an important
data source which is used in traffic density detection, transport mode
detection, mapping data inferences with the use of different methods such as
image processing and machine learning methods. While the data size increases,
efficient representation of this type of data is becoming difficult to be used
in these methods. A common approach is the representation of GPS trajectory
information such as average speed, bearing, etc. in raster image form and
applying analysis methods. In this study, we evaluate GPS trajectory data
rasterization using the spatial join functions of QGIS, PostGIS+QGIS, and our
iterative spatial structured grid aggregation implementation coded in the
Python programming language. Our implementation is also parallelizable, and
this parallelization is also included as the fourth method. According to the
results of experiment carried out with an example GPS trajectory dataset, QGIS
method and PostGIS+QGIS method showed relatively low performance with respect
to our method using the metric of total processing time. PostGIS+QGIS method
achieved the best results for spatial join though its total performance
decreased quickly while test area size increases. On the other hand, both of
our methods' performances decrease directly proportional to GPS point. And our
methods' performance can be increased proportional to the increase with the
number of processor cores and/or with multiple computing clusters.