Traffic Prediction Based on Air Quality in IoT-Based Smart City Using Regression and Ensemble Techniques: Bagging and Stacking
Keywords:
Regression, Air Quality, Ensemble, Stacking, Bagging, Smart CityAbstract
Traffic forecast implies determining the volume and thickness of the traffic stream, typically for the reason of controlling vehicle development, decreasing congestion, and producing the ideal routes with the least amount of time or energy consumed. Accurate street traffic flow determination is among the foremost essential factors in smart cities. In this research, we utilized air quality data and ensemble regression methods to establish a predictive model for traffic patterns, recognizing the correlation between air pollution levels and congested traffic conditions. This study was conducted in two distinct stages. In the first phase, we compared the performance of 10 different regression models (Decision Tree, KNN, Cat Boost, Linear Regression, Lasso, Elastic Net, Kernel Ridge, Gradient Boost, XGB, and LGBM), and K-Nearest Neighbour gave the best result with RMSE 2.80 and Lasso gave the least performance with 5.28 RMSE. In the second phase, we developed models based on ensemble techniques: bagging and stacking. Depending on the performance of the regressors in the first phase, we attempted numerous permutations of distinctive models in bagging and stacking till we got the most excellent conceivable results. Finally, out of many arrangements, the Stacking Model with CatBoost, KNN, and Decision Tree as base learners and Lasso as meta learner performed better than KNN and Bagging Ensemble Regression models with RMSE 2.09.
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