التفاصيل البيبلوغرافية
| العنوان: |
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. |
| المؤلفون: |
Jlifi, Boutheina, Medini, Mahdi, Duvallet, Claude |
| المصدر: |
Journal of Supercomputing; Sep2024, Vol. 80 Issue 13, p18797-18837, 41p |
| مصطلحات موضوعية: |
REGRESSION analysis, SUSTAINABLE urban development, AIR quality monitoring, AIR quality, LONG-term memory, TRAFFIC estimation |
| مستخلص: |
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. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
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