  Traffic congestion research is on the rise, thanks to urbanization, economic
growth, and industrialization. Developed countries invest a lot of research
money in collecting traffic data using Radio Frequency Identification (RFID),
loop detectors, speed sensors, high-end traffic light, and GPS. However, these
processes are expensive, infeasible, and non-scalable for developing countries
with numerous non-motorized vehicles, proliferated ride-sharing services, and
frequent pedestrians. This paper proposes a novel approach to collect traffic
data from Google Map's traffic layer with minimal cost. We have implemented
widely used models such as Historical Averages (HA), Support Vector Regression
(SVR), Support Vector Regression with Graph (SVR-Graph), Auto-Regressive
Integrated Moving Average (ARIMA) to show the efficacy of the collected traffic
data in forecasting future congestion. We show that even with these simple
models, we could predict the traffic congestion ahead of time. We also
demonstrate that the traffic patterns are significantly different between
weekdays and weekends.
