标题： 在线学习在交通数据上用于拥塞水平预测的新观点 显示英文标题

标题： New Perspectives on the Use of Online Learning for Congestion Level Prediction over Traffic Data

摘要： 这项工作专注于时间序列数据的分类。 当时间序列由非平稳现象生成时，与要预测的类相关的序列模式可能会随时间演变（概念漂移）。 因此，旨在学习此模式的预测模型可能最终过时，从而无法维持实际使用所需的性能水平。 为了克服这种模型退化，在线学习方法从随时间到来的新数据样本中逐步学习，并通过实施各种概念漂移策略来适应数据流中的变化。 在本文中，我们详细阐述了在线学习方法在基于交通速度时间序列数据预测道路拥堵水平的适用性。 我们得出了关于当预测范围增加时性能退化的有趣见解。 与大多数文献中的做法相反，我们提供了在设计和调整学习模型之前评估类别分布的重要性证据。 这之前的练习可能暗示了在目标下不同拥堵水平的可预测性。 实验结果基于在西雅图（美国）部署的感应线圈捕获的真实交通速度数据进行讨论。 分析了几种在线学习方法，从传统的增量学习算法到更复杂的深度学习模型。 如报告的结果所示，当增加预测范围时，所有模型的性能由于类别沿时间的分布而严重退化，这支持了我们关于在模型设计之前分析此分布重要性的主张。 显示英文摘要

摘要： This work focuses on classification over time series data. When a time series is generated by non-stationary phenomena, the pattern relating the series with the class to be predicted may evolve over time (concept drift). Consequently, predictive models aimed to learn this pattern may become eventually obsolete, hence failing to sustain performance levels of practical use. To overcome this model degradation, online learning methods incrementally learn from new data samples arriving over time, and accommodate eventual changes along the data stream by implementing assorted concept drift strategies. In this manuscript we elaborate on the suitability of online learning methods to predict the road congestion level based on traffic speed time series data. We draw interesting insights on the performance degradation when the forecasting horizon is increased. As opposed to what is done in most literature, we provide evidence of the importance of assessing the distribution of classes over time before designing and tuning the learning model. This previous exercise may give a hint of the predictability of the different congestion levels under target. Experimental results are discussed over real traffic speed data captured by inductive loops deployed over Seattle (USA). Several online learning methods are analyzed, from traditional incremental learning algorithms to more elaborated deep learning models. As shown by the reported results, when increasing the prediction horizon, the performance of all models degrade severely due to the distribution of classes along time, which supports our claim about the importance of analyzing this distribution prior to the design of the model.