Multilevel Learning for Enhanced Traffic Congestion Prediction Using Anomaly Detection and Ensemble Learning.

Traffic congestion is a major challenge in modern transportation systems, leading to increased travel time, greater fuel consumption, and higher levels of harmful emissions. Accurately forecasting traffic congestion at a high level of precision is crucial for implementing effective traffic control strategies, such as managing traffic lights. To generate accurate and reliable predictions, it is crucial to precisely detect abnormal traffic patterns in the data, particularly in densely populated urban areas. This study presents a novel approach for forecasting traffic congestion by employing a multilevel learning technique that integrates both anomaly detection and ensemble learning methods. As an initial step in the learning process, we evaluate different anomaly detection techniques to identify unusual traffic patterns in different locations over time. After predicting the anomaly, the data pattern is cleaned accordingly, and a set of baseline learner models is trained as a secondary learning process. The top-performing models are chosen and undergo an ensemble process to combine their results, evaluating both stacking and voting ensemble methods as a third learning process. We evaluate the efficacy of the proposed strategy by employing a real-world traffic dataset and diverse evaluation metrics. The dataset undergoes several preprocessing techniques, including the windowing process with various settings, to convert the time series data into frequency patterns and produce a more generalized model. The comparative results of this study demonstrate that the proposed multilevel learning approach achieves superior prediction accuracy compared to the baseline models, underscoring the influence of the multilevel learning strategy on the accuracy of predictions. This study highlights the utilization of anomaly detection and ensemble learning to enhance the precision of traffic congestion prediction, thereby promoting further exploration of this approach in intelligent transportation systems.