  Traffic congestion is a complex, nonlinear spatiotemporal modeling problem.
By collecting and analyzing a vast quantity and different categories of
information, traffic flow, and road congestion can be predicted and controlled
on an intelligent transportation system. This report provides an analysis of
traveling time across Taiwan from North to South, vice versa. We analyze
traffic in a national freeway between Tainan and Kaohsiung section, which
represents the common trip of the population in Southern Taiwan. The data is
recorded using the Electronic Toll Collection System (ETC) provided by Ministry
of Transportation in Taiwan. We use MapReduce framework to process data into a
smaller task which can be distributed on several computer clusters to speed up
the process. The results show that the spatiotemporal model of traffic flow is
strongly influenced by direction, working hour, and holidays with a recurring
pattern for each week. The distinctive pattern inside the spatiotemporal
dataset can be used on an AI-powered decision-making system for future
development.
