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.
翻译:近几十年来,移动应用(apps)已变得非常受欢迎。智能城市的智能服务日益受到关注。拟议研究的主要目标是利用交通密度数据,在伊斯坦布尔交通拥堵预测中提供一个新的AI动力移动应用。它利用时间序列方法(LSTM、变换器和XGBoost),根据过去关于交通负荷数据集的数据以及气象条件,解决研究问题。预测模型的模拟结果分析将根据MAPE、MAE和RMSE等性能指标进行讨论。然后,观察到变换器模型做出了最准确的交通流量预测。开发的交通预测原型预计将成为适合公民日常使用的移动应用的未来产品的起点。