A Novel Deep Stacking-Based Ensemble Approach for Short-Term Traffic Speed Prediction

Advanced technologies, driven by extensive data analysis, support the concept of intelligent cities, which aim to enhance the quality of people's lives, minimize the consumption of energy, reduce pollution, and promote economic growth. The transportation network is a crucial component of this vision in urbanized cities. However, a massive increase in road traffic poses a significant challenge to achieving this vision. Developing an intelligent transportation system requires accurately predicting the traffic speed. This paper proposes a novel deep stacking-based Ensemble model with a two-layer structure to address the problem of forecasting traffic speed in urbanized transportation networks to solve traffic congestion problems. Firstly, advanced machine learning such as eXtreme Gradient Boosting(XGB), Random Forest(RF), and Extra Tree(ET) as base learners are used to predict short-term traffic speed. In the next phase, the Multi-Layer Perceptron (MLP) as a meta-learner technique, employing various combinations of the aforementioned approaches is used to enhance the accuracy of traffic speed prediction. The proposed stacking-based approach has the capability to analyze, extract, and aggregate various features from primary traffic speed data in order to generate more refined and accurate forecasts. This study used a publicly available dataset of Floating Cars Data collected from real transportation networks for evaluation. Mutual information regression is used as a feature selection technique to obtain the features from the dataset for the training of these models. The performance results are compared with state-of-the-art traffic prediction models. Results show that the proposed stacking-based ensemble strategy outperforms conventional approaches by a large margin such as HA, KNN, SVR, DT, T-GCN, and A3TGCN models. The results demonstrate a notable reduction of 9.71% in RMSE and 15.4% in MAE, indicating enhanced accuracy. Furthermore, our approach achieved a substantial improvement of 13.80% in $\mathbb {R}^{2}$ and 11.64% in EV for the 15-minute prediction horizon.