Context-Aware Online Spatiotemporal Traffic Prediction

With the availability of traffic sensors data, various techniques have been proposed to make congestion prediction 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.