伊斯坦布尔智能旅程：智慧城市中利用时间序列进行交通估计的移动应用
Smart Journey in Istanbul: A Mobile Application in Smart Cities for Traffic Estimation by Harnessing Time Series
AI总结：
本文提出一款AI驱动的伊斯坦布尔交通拥堵预测移动应用，结合交通负荷与气象数据比较LSTM、Transformer和XGBoost，发现Transformer预测最准确。
摘要：
近几十年来，移动应用程序（apps）已获得巨大普及。面向智慧城市的智能服务日益受到关注。本研究的主要目标是提出一款由AI驱动的新型移动应用，利用交通密度数据预测伊斯坦布尔的交通拥堵。该研究通过在结合气象条件的交通负荷数据集上，基于历史数据采用时间序列方法（LSTM、Transformer和XGBoost）来解决研究问题。将依据MAPE、MAE和RMSE等性能指标，讨论预测模型的仿真结果分析。随后观察到，Transformer模型作出了最准确的交通预测。所开发的交通预测原型有望成为未来适合市民日常使用的移动应用产品的起点。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
