Context-Aware Online Spatiotemporal Traffic Prediction

Abstract—With the availability of traffic sensors data, vari-ous techniques have been proposed to make congestion predic-tion by utilizing those datasets. One key challenge in predicting traffic congestion is how much to rely on the historical data v.s. the real-time data. To better utilize both the historical and real-time data, in this paper we propose a novel online framework that could learn the current situation from the real-time data and predict the future using the most effective predictor in this situation from a set of predictors that are trained using historical data. In particular, the proposed framework uses a set of base predictors (e.g. a Support Vector Machine or a Bayes classifier) and learns in real-time the most effective one to use in different contexts (e.g. time, location, weather condition). As real-time traffic data arrives, the context space is adaptively partitioned in order to efficiently estimate the effectiveness of each predictor in different contexts. We obtain and prove both short-term and long-term performance guarantees (bounds) for our online algorithm. Our experiments with real-world data in real-life conditions show that the proposed approach significantly outperforms existing solutions. I