Automatic Inference of Anomalous Events from (California) Traffic Patterns
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Transcript of Automatic Inference of Anomalous Events from (California) Traffic Patterns
Automatic Inference of Anomalous Events from (California) Traffic Patterns Sean Li, Electrical Engineering. Professor: Dr. Padhraic Smyth
[email protected] · www.research.calit2.net/students/surf-it2006 · www.calit2.net
S ummer U ndergraduate 2 R esearch 0 F ellowship in 0 I nformation 6 T echnology
Markov Modulated Poisson Process
ACKNOWLEDGEMENTSThis project was conducted under the guidance of Dr. Padhraic Smyth and Jon Hutchins. PEMS Data collecting is done by EECS department at UC Berkeley.
Baseline Model has Limitations
The Chicken and Egg problem
Baseline model
Ideal model
Baseline model-lower thresholdBaseline model
False Positives, Persistence and Duration
Previous Work
Time Series Count DataReal Time Traffic Event Detection
In this project, we developed a web-based system that automatically identifies “anomalous events” on a freeway by analyzing traffic pattern data from sensors. By implementing the time varying Poisson model, this system is capable of detecting any unexpected events in any given location, day, time etc. This system can display both the real-time traffic data and "toggle" to a display what the model considers to be unusual.
Surf-IT ProjectThis Surf-IT project built on work presented in “Adaptive event detection with time-varying Poisson processes” A. Ihler, J. Hutchins, and P. Smyth, Proceedings of the 12th ACM SIGKDD Conference (KDD-06), to appear, 2006
OBSERVEDCOUNT NORMAL
COUNT(UNOBSERVED)
EVENTCOUNT
(UNOBSERVED)
Graphical model for event process (z(t)) and observed counts (N(t))
Graphical model for “Normal Counts” (No(t)) and the Poisson rate parameter
Illustration Of The Real Time Traffic Event Detection System
(1)Traffic data stored in the PEMS (Freeway Performance Measurement System) FTP server. (2) Perl script receives /extracts/parts/stores data into Mysql data base.(3) C++ inference code implements the time-varying Poisson model and returns the calculated event probability in real time.(4) Ruby on Rails and JAVA software is used to create and update the web-based system to reflect the current freeway condition.
Website shows map and displays predictions for the last 2 hours
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