Building trust for traffic flow forecasting components in intelligent transportation systems via interpretable ensemble learning

Le résumé fourni par la source

Traffic flow forecasting constitutes a crucial component of intelligent transportation systems (ITSs). Numerous studies have been conducted for traffic flow forecasting during the past decades. However, most existing studies have concentrated on developing advanced algorithms or models to attain state-of-the-art forecasting accuracy. For real-world ITS applications, the interpretability of the developed models is extremely important but has largely been ignored. This study presents an interpretable traffic flow forecasting framework based on popular tree-ensemble algorithms. The framework comprises multiple key components integrated into a highly flexible and customizable multi-stage pipeline, enabling the seamless incorporation of various algorithms and tools. To evaluate the effectiveness of the framework, the developed tree-ensemble models and another three typical categories of baseline models, including statistical time series, shallow learning, and deep learning, were compared on three datasets collected from different types of roads (i.e., arterial, expressway, and freeway). Further, the study delves into an in-depth interpretability analysis of the most competitive tree-ensemble models using six categories of interpretable machine learning methods. Experimental results highlight the potential of the proposed framework. The tree-ensemble models developed within this framework achieve competitive accuracy while maintaining high inference efficiency similar to statistical time series and shallow learning models. Meanwhile, these tree-ensemble models offer interpretability from multiple perspectives via interpretable machine-learning techniques. The proposed framework is anticipated to provide reliable and trustworthy decision support across various ITS applications.

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