The article proposes the Incremental Ensemble of Batch and Stream Models (IEBSM) method for predicting preferred modes of transportation. The method aims to address the challenge of evolving travel behavior patterns by combining drift detectors with batch learning and online learning models.
The key highlights and insights are:
Travel mode choice (TMC) prediction can be formulated as a classification task, but factors like seasonality and evolving user preferences suggest that incorporating concept drift detection and adaptation could be beneficial.
The authors investigate whether statistically significant changes occur in travel mode choice data across multiple real-world datasets, and confirm that such changes do occur in each dataset.
The proposed IEBSM method builds an ensemble of batch and online learning models, utilizing drift detectors to monitor changes in data distribution and model performance. It dynamically updates the ensemble by replacing underperforming batch models with newly retrained models.
Experiments on various travel mode choice datasets show that the IEBSM method outperforms standalone batch and online learning approaches. The ensemble-based approach effectively mitigates the challenge of selecting the optimal learning method and drift detection settings.
The authors analyze the role of drift detection and model replacement within the IEBSM approach, finding that both components are vital for the best-performing method (DS-RF).
The results demonstrate the importance of continuously monitoring and retraining TMC models to adapt to evolving travel behavior patterns, and highlight the benefits of combining batch and online learning approaches.
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