Context-aware online spatiotemporal traffic prediction

Jie Xu, Dingxiong Deng, Ugur Demiryurek, Cyrus Shahabi, Mihaela Van Der Schaar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationProceedings - 14th IEEE International Conference on Data Mining Workshops, ICDMW 2014
EditorsZhi-Hua Zhou, Wei Wang, Ravi Kumar, Hannu Toivonen, Jian Pei, Joshua Zhexue Huang, Xindong Wu
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)9781479942749
StatePublished - Jan 26 2015
Externally publishedYes
Event14th IEEE International Conference on Data Mining Workshops, ICDMW 2014 - Shenzhen, China
Duration: Dec 14 2014 → …

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259


Other14th IEEE International Conference on Data Mining Workshops, ICDMW 2014
Period12/14/14 → …

ASJC Scopus subject areas

  • Computer Science Applications
  • Software


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