  The paper adopts parallel computing systems for predictive analysis in both
CPU and GPU leveraging Spark Big Data platform. The traffic dataset is adopted
to predict the traffic jams in Los Angeles County. It is collected from a
popular platform in the USA for tracking information on the road using the
device information and reports shared by the users. Large-scale traffic data
set can be stored and processed using both GPU and CPU in this Scalable Big
Data systems. The major contribution of this paper is to improve the
performance of machine learning in distributed parallel computing systems with
GPU to predict the traffic congestion. We show that the parallel computing can
be achieve using both GPU and CPU with the existing Apache Spark platform. Our
method can be applicable to other large scale datasets in different domains.
The process modeling, as well as results, are interpreted using computing time
and metrics: AUC, Precision and Recall. It should help the traffic management
in Smart City.
