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Title	A Guided Genetic Algorithm-Based Ensemble Voting Of Polynomial Regression And Lstm (Gga-Polreg-Lstm) For Congestion Prediction Using Iot And Air Quality Data In Sustainable Cities
ID_Doc	2008
Authors	Jlifi B.; Medini M.; Duvallet C.
Year	2024
Published	Journal of Supercomputing, 80, 13
DOI	http://dx.doi.org/10.1007/s11227-024-06186-7
Abstract	A sustainable city is a smart city with a minimal impact on the environment, by incorporating technologies to reduce pollution. Traffic congestion which is a major concern contributes to global warming and climate change. Traffic forecasting projects future traffic patterns, using historical and current data to enhance traffic flow management. We propose a whole novel approach for predicting traffic congestion rate based on air quality data. We developed a new ensemble voting model based on Long Short Term Memory (LSTM) and Polynomial Regression (PolReg) models that use a new voting thresholded algorithm instead of the existing voting ones. The hyperparameters were optimized with the Genetic Agorithm, to overcome the non-stationarity of time series. A comparative study with the literature confirmed that our framework outperforms existing researches by keeping an absolute effectiveness according to learning curves, with Mean Absolute Error of 0.04, R-Squared of 0.93, and Root Mean Square Error (RMSE) of 0.05. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Author Keywords	Air quality data; Artificial intelligence; Climate change; Genetic algorithm (GA); Internet of Things (IoT) data; Short-term traffic congestion prediction; Smart sustainable city

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