Real Time Change Detection and Alerts from Highway Traffic Data
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Calibrating a real-time traffic crash-prediction model using archived weather and ITS traffic data
IEEE Transactions on Intelligent Transportation Systems
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Road intersections have become the places of high road incidents and car collisions. Our hypothesis is that a system can be made aware of dangerous situations at road intersections and warn drivers accordingly. Moreover, over time, the system can learn (or re-learn) such "patterns" of danger for specific intersections given a history of rich collision data collected via sensors (that exist today). Based on the assumption that such a history of sensory data about colliding vehicles can be obtained, we show useful patterns that can be extracted. This paper presents our framework for intersection understanding, presenting simulated results suggesting that a fragment of the world (i.e. intersections) can be more deeply understood by mining appropriate sensor data. The simulated environment of the road intersections forming the basis of a real-world implementation and testing of the framework are discussed here. The recent results of mining traffic and collision data generated by the simulation are also included in this paper.