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Title	Data Mining For Smart Cities: Traffic Congestion Prediction
ID_Doc	17272
Authors	Mystakidis A.; Geromichalou O.; Tjortjis C.
Year	2023
Published	14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023
DOI	http://dx.doi.org/10.1109/IISA59645.2023.10345857
Abstract	In this work, we utilized univariable and multivari-able regression models, including Linear Regression (LR), Ran-dom Forest (RF), Multi-Layer Perceptron (MLP), and Gradient Boosting (GB), to predict traffic flow at six intersections. We incorporate temporal features and seasonality time intervals be-tween the observations of the time series to achieve better results. GB outperformed the other models with regards to the coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), as well as Explained Variance Score (EVS). One extra univariable approach was utilized, combining data from other highly correlated intersections. This strategy led to better models with regard to several metrics. All in all, the multivariable and highly correlated approach produced better results compared to univariable regression. © 2023 IEEE.
Author Keywords	city intelligence; Machine Learning; Mobility; Smart cities; traffic prediction

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