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 ACKNOWLEDGEMENTS This 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 threshold Baseline model False Positives, Persistence and Duration Previous Work Time Series Count Data Real 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 Project This 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 OBSERVED COUNT NORMAL COUNT (UNOBSERVED) EVENT COUNT (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 4 3 2 1

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Baseline model. OBSERVED COUNT. NORMAL COUNT (UNOBSERVED). EVENT COUNT (UNOBSERVED). Ideal model. Previous Work. Surf-IT Project. - PowerPoint PPT Presentation

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Page 1: 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