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

Abstract

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.

An active adaptation strategy for streaming time series classification based on elastic similarity measures

“…In many application scenarios, time series are produced by dynamic processes, where data instances are streamed continuously at high speed, generating large volumes of samples that must be analyzed as fast as possible to comply with the limited memory and processing capabilities of current computer architectures [1,2]. Illustrative examples of such continuously generated streaming time series (STS) include electricity supply data [3], human activity signals issued from wearable sensors [4] or car flow sequences [5], among many others.…”