Mining the Situation: Spatiotemporal Traffic Prediction with Big Data

With the vast availability of traffic sensors from which traffic information can be derived, a lot of research effort has been devoted to developing traffic prediction techniques, which in turn improve route navigation, traffic regulation, urban area planning etc. One key challenge in traffic prediction is how much to rely on prediction models that are constructed using historical data in real-time traffic situations, which may differ from that of the historical data and change over time. In this paper, we propose a novel online framework that could learn from the current traffic situation (or context) in real-time and predict the future traffic by matching the current situation to the most effective prediction model trained using historical data. As real-time traffic arrives, the traffic context space is adaptively partitioned in order to efficiently estimate the effectiveness of each base predictor in different situations. We obtain and prove both short-term and long-term performance guarantees (bounds) for our online algorithm. The proposed algorithm also works effectively in scenarios where the true labels (i.e. realized traffic) are missing or become available with delay. Using the proposed framework, the context dimension that is the most relevant to traffic prediction can also be revealed, which can further reduce the implementation complexity as well as inform traffic policy making. Our experiments with real-world data in reallife conditions show that the proposed approach significantly outperforms existing solutions.