Travel mode choice (TMC) prediction, which can be formulated as a classification task, helps in understanding what makes citizens choose different modes of transport for individual trips. This is also a major step towards fostering sustainable transportation. As behaviour may evolve over time, we also face the question of detecting concept drift in the data. This necessitates using appropriate methods to address potential concept drift. In particular, it is necessary to decide whether batch or stream mining methods should be used to develop periodically updated TMC models. To address the challenge of the development of TMC models, we propose the novel Incremental Ensemble of Batch and Stream Models (IEBSM) method aimed at adapting travel mode choice classifiers to concept drift possibly occurring in the data. It relies on the combination of drift detectors with batch learning and stream mining models. We compare it against batch and incremental learners, including methods relying on active drift detection. Experiments with varied travel mode data sets representing both city and country levels show that the IEBSM method both detects drift in travel mode data and successfully adapts the models to evolving travel mode choice data. The method has a higher rank than batch and stream learners.

以城市和国家层面的各种出行方式数据集为例，我们提出了一种新颖的增量批处理和流处理模型的组合方法(IEBSM)，旨在适应出现在数据中的概念漂移，该方法结合漂移检测器、批处理学习和流式挖掘模型，与批处理和增量学习器进行比较，包括依赖主动漂移检测的方法，实验证明IEBSM方法能够检测到出行方式数据中的漂移，并成功地将模型适应不断演变的出行方式选择数据，其性能排名高于批处理和流学习器。

混合集成旅行方式预测