  The research examined predicting short-duration traffic flow counts with the
Kalman filtering technique (KFT), a computational filtering method. Short-term
traffic prediction is an important tool for operation in traffic management and
transportation system. The short-term traffic flow value results can be used
for travel time estimation by route guidance and advanced traveler information
systems. Though the KFT has been tested for homogeneous traffic, its efficiency
in heterogeneous traffic has yet to be investigated. The research was conducted
on Mirpur Road in Dhaka, near the Sobhanbagh Mosque. The stream contains a
heterogeneous mix of traffic, which implies uncertainty in prediction. The
propositioned method is executed in Python using the pykalman library. The
library is mostly used in advanced database modeling in the KFT framework,
which addresses uncertainty. The data was derived from a three-hour traffic
count of the vehicle. According to the Geometric Design Standards Manual
published by Roads and Highways Division (RHD), Bangladesh in 2005, the
heterogeneous traffic flow value was translated into an equivalent passenger
car unit (PCU). The PCU obtained from five-minute aggregation was then utilized
as the suggested model's dataset. The propositioned model has a mean absolute
percent error (MAPE) of 14.62, indicating that the KFT model can forecast
reasonably well. The root mean square percent error (RMSPE) shows an 18.73%
accuracy which is less than 25%; hence the model is acceptable. The developed
model has an R2 value of 0.879, indicating that it can explain 87.9 percent of
the variability in the dataset. If the data were collected over a more extended
period of time, the R2 value could be closer to 1.0.
