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.

翻译：出行方式选择（TMC）预测可被形式化为分类任务，有助于理解居民在个体出行中选择不同交通方式的成因。这也是推动可持续交通发展的关键步骤。由于人类行为可能随时间演变，我们还需面对数据中的概念漂移检测问题。这要求采用适当的方法来处理潜在的概念漂移，特别是需要决策应采用批处理挖掘还是流式挖掘方法来开发周期性更新的TMC模型。针对TMC模型开发面临的挑战，我们提出了一种新型增量集成批处理与流模型方法（IEBSM），旨在使出行方式选择分类器适应数据中可能出现的概念漂移。该方法通过结合漂移检测器与批量学习及流式挖掘模型实现。我们将其与批量学习器、增量学习器（包括依赖主动漂移检测的方法）进行对比。基于城市与乡村层面多组出行方式数据集的实验表明，IEBSM方法不仅能检测出行数据中的漂移现象，还能成功使模型适应不断演化的出行方式选择数据。该方法在性能排序上优于批处理学习器和流式学习器。