Real Time Change Detection and Alerts from Highway Traffic Data

Authors:
Robert Grossman;Michal Sabala;Anushka Aanand;Steve Eick;Leland Wilkinson;Pei Zhang;John Chaves;Steve Vejcik;John Dillenburg;Peter Nelson;Doug Rorem;Javid Alimohideen;Jason Leigh;Mike Papka;Rick Stevens
Affiliations:
National Center for Data Minin, University of Illinois at Chicago;National Center for Data Minin, University of Illinois at Chicago;National Center for Data Mining, University of Illinois at Chicago;National Center for Data Mining, University of Illinois at Chicago;National Center for Data Mining, University of Illinois at Chicago;National Center for Data Mining, University of Illinois at Chicago;Open Data Research;Open Data Research;Department of Computer Science, University of Illinois at Chicago;Department of Computer Science, University of Illinois at Chicago;Department of Computer Science, University of Illinois at Chicago;Electronic Visualization Laboratory, University of Illinois at Chicago;Electronic Visualization Laboratory, University of Illinois at Chicago;Argonne National Laboratory;Argonne National Laboratory
Venue:
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Year:
2005

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Abstract

We developed a testbed containing: real time data from over 830 highway traffic sensors in the Chicago region, data about weather, and text data about events that might affect traffic. The goal was to detect in real time interesting changes in traffic conditions. Given the size and complexity of the data, we choose to build a large number of separate baseline models. We built a separate baseline for each hour in the day, for each day in the week, and for every 2 or 3 traffic sensors, resulting in over 42,000 separate baseline models. We also built a baseline engine to build the necessary baselines automatically. We modified an open source scoring engine to process in real time each new sensor reading, update the appropriate feature vectors, score the updated feature vectors using the baseline models, and send out real time alerts when deviations from the baselines were detected.