Detect Road Traffic Events by Bayesian Robust Principal Component Analysis

1. Detect Road Traffic Events by Bayesian Robust Principal Component Analysis KonstantinosKalpakis Dept. of Computer Science and Electrical Engineering University of Maryland, Baltimore County 2013 • Thank IBM for their generous support of this research effort. • This work has been submitted to IEEE ITS2013

2. Outline • Motivation • Road Traffic • Real Dataset • Example Events • Events Detection with Our Method • Performance • Summary

3. Motivation • Use traffic sensor datastreams to monitor highway traffic condition • Automatic, early detecting of events to support drivers’ early decisions • Support the management of the transportation infrastructure

4. Road Traffic forecasting? …Bing Maps is now using our Nokia Maps trafficinformation… conversations.nokia.com We get our information from four types of sources: digital traffic sensors, GPS/probe devices, commercial and government partners, and our traffic operations center staff members. NAVTEQ Traffic.com We anonymously combine speed and location information of GPS-enabled devices currently traveling on the road. This, combined with historic traffic data, helps us determine the traffic time estimate. Support.google.com/maps

5. Real Dataset • Minnesota I-494 southbound/eastbound • Flow rate and occupancy measured by loop detectors • Minnesota Traffic Observatory • One reading every 30 sec • Aggregate to every 15 min • traffic incidents: 511MN.org • Roadwork, incident, and hazard • Location, start time, duration • Weather data: weathersource.com • Visibility

6. Example events • A recorded roadwork event lasting 47 min • One lane blocked • Reduced flow rate • Reduced occupancy • Reduced traffic due to limited visibility • Free flow with reduced flow rate and increased occupancy • An unusual traffic increase at a Saturday night • We hypothesize that it was caused by social activities • A traffic jam in early morning on a weekday • No reported incident • Heavy snow and low visibility • May be the severe weather caused traffic jam

7. Events detection with our method • Reported event; but had no impact on traffic; not detected • Reported car crash with lane blocking for 20 min (511MN.org); detected; with dropped occupancy and flow rate 1 2 3 4 reported incident 1 • Detected increase of flow and occupancy; no reported event • Detected reduced traffic speed due to low visibility (heavy snow according to weather record) 3 detected event 2 4

8. Detection with our method (cont’d) 5 6 7 8 • Reported event; but had unnoticeable impact on traffic; not detected 6 reported incident • detected lane blocking event; not reported from 511MN.org 5 ~ detected event 8

9. Performance Tab 1: Detection accuracy comparison of three methods, using data from the same sensor. Tab 2: Detection accuracy comparison of coupling different variables among two sensors using our Coupled BRPCA method.

10. Summary • Our method detects traffic events from continuously collected measurements from road traffic sensors. • Our method has accuracy improvement compared with traditional PCA, and robust PCA. • Our method has a probabilistic interpretation of the detected events, and has merits from non-parametric method. • Our method can be further used in finding denoised boundary conditions for some macroscopic traffic prediction models.

11. Thank you