  In recent decades, mobile applications (apps) have gained enormous
popularity. Smart services for smart cities increasingly gain attention. The
main goal of the proposed research is to present a new AI-powered mobile
application on Istanbul's traffic congestion forecast by using traffic density
data. It addresses the research question by using time series approaches (LSTM,
Transformer, and XGBoost) based on past data over the traffic load dataset
combined with meteorological conditions. Analysis of simulation results on
predicted models will be discussed according to performance indicators such as
MAPE, MAE, and RMSE. And then, it was observed that the Transformer model made
the most accurate traffic prediction. The developed traffic forecasting
prototype is expected to be a starting point on future products for a mobile
application suitable for citizens' daily use.
