Smart Journey in Istanbul: A Mobile Application in Smart Cities for Traffic Estimation by Harnessing Time Series
This work addresses traffic estimation for citizens in smart cities, but it is incremental as it applies existing methods to a new dataset.
The researchers tackled traffic congestion forecasting in Istanbul by developing an AI-powered mobile application that uses time series models (LSTM, Transformer, and XGBoost) on traffic and meteorological data, with the Transformer model achieving the most accurate predictions based on metrics like MAPE, MAE, and RMSE.
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.