  With the era of big data, an explosive amount of information is now
available. This enormous increase of Big Data in both academia and industry
requires large-scale data processing systems. A large body of research is
behind optimizing Spark's performance to make it state of the art, a fast and
general data processing system. Many science and engineering fields have
advanced with Big Data analytics, such as Biology, finance, and transportation.
Intelligent transportation systems (ITS) gain popularity and direct benefit
from the richness of information. The objective is to improve the safety and
management of transportation networks by reducing congestion and incidents. The
first step toward the goal is better understanding, modeling, and detecting
congestion across a network efficiently and effectively. In this study, we
introduce an efficient congestion detection model. The underlying network
consists of 3017 segments in I-35, I-80, I-29, and I-380 freeways with an
overall length of 1570 miles and averaged (0.4-0.6) miles per segment. The
result of congestion detection shows the proposed method is 90% accurate while
has reduced computation time by 99.88%.
