Adaptive event detection with time-varying poisson processes
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Temporal Outlier Detection in Vehicle Traffic Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Classification with Unknown Classes
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Better drilling through sensor analytics: a case study in live operational intelligence
Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data
Discovering spatio-temporal causal interactions in traffic data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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Using sensor data from fixed highway traffic detectors, as well as data from highway patrol logs and local weather stations, we aim to answer the domain problem: "A traffic incident just occurred. How severe will its impact be?" In this paper we show a practical system for predicting the cost and impact of highway incidents using classification models trained on sensor data and police reports. Our models are built on an understanding of the spatial and temporal patterns of the expected state of traffic at different times of day and locations and past incidents. With high accuracy, our model can predict false reports of incidents that are made to the highway patrol and classify the duration of the incident-induced delays and the magnitude of the incident impact, measured as a function of vehicles delayed, the spatial and temporal extent of the incident. Equipped with our predictions of traffic incident costs and relative impacts, highway operators and first responders will be able to more effectively respond to reports of highway incidents, ultimately improving drivers' welfare and reducing urban congestion.