Explainable Stacking-Based Learning Model for Traffic Forecasting
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 150, Issue 4
Abstract
This paper implements a two-staged ensemble learning model for traffic forecasting, focusing on the interpretability of predictions. The stacking model leverages the advantages of its diverse component learning models. Experiments on high-dimensional and sparse data validate the stacking model’s superior predictive accuracy compared to baseline models, including LightGBM and XGBoost. In addition to validating the stacking model’s outstanding predictive performance, this paper emphasizes the interpretability of its predictions by proposing an innovative explanation model based on feature contributions. This explanation model addresses the high dimension and sparsity in data prevalent in transportation engineering with its integration of resampling and consensus clustering, offering a scalable, stable, and computationally efficient solution ideal for real-time and large-scale applications. The paper presents theoretical justification, experimental results, and empirical validation of the interpretation model. Extensive experiments demonstrate the model’s enhanced stability compared to traditional shapley additive explanations (SHAP) implementations such as kernel SHAP. Investigating trade-offs between stability and computational efficiency of resampling provides insights for optimal configuration choices. This paper contributes to traffic flow prediction with broad applicability in real-time and large-scale traffic management scenarios, underscoring the vital role of ensemble learning and interpretable machine learning in contemporary data-driven decision making processes.
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Data Availability Statement
The authors confirm that all data, models, and codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
Chengyong Chen and Jinghan Liu contributed to the work equally and should be regarded as cofirst authors.
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© 2024 American Society of Civil Engineers.
History
Received: Aug 3, 2023
Accepted: Nov 15, 2023
Published online: Jan 29, 2024
Published in print: Apr 1, 2024
Discussion open until: Jun 29, 2024
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