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Title	Traffic Congestion Prediction: A Machine Learning Approach
ID_Doc	58545
Authors	Geromichalou O.; Mystakidis A.; Tjortjis C.
Year	2024
Published	Lecture Notes in Networks and Systems, 1093 LNNS
DOI	http://dx.doi.org/10.1007/978-3-031-67426-6_16
Abstract	In this study, in order to forecast traffic flow, we employed univariable and multivariable regression models, including Linear Regression (LR), Random Forest (RF), Multi-Layer Perceptron (MLP), Gradient Boosting (GB), Long short-term memory (LSTM), and Gated Recurrent Unit (GRU). With timestamp information being unavailable, we use temporal characteristics and seasonality time gaps between timeseries data to improve the results. GB outperformed the other models with regards to the coefficient of determination (R2), Mean Absolute Error (MAE), Mean Squared Error (MSE), 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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Author Keywords	city intelligence; Deep Learning; Machine Learning; Mobility; Smart Cities; traffic prediction

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