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Detect Road Traffic Events by Bayesian Robust Principal Component Analysis

This research presents a novel method for detecting road traffic events through Bayesian Robust Principal Component Analysis (BRPCA). By leveraging real-time sensor data from highways, the method allows for automatic and early identification of events, supporting driver decision-making and transportation management. The study uses a comprehensive dataset from Minnesota's I-494, analyzing traffic patterns during incidents such as roadwork and weather impacts. Results demonstrate improved detection accuracy compared to traditional PCA, offering a probabilistic framework beneficial for traffic prediction applications.

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Detect Road Traffic Events by Bayesian Robust Principal Component Analysis

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  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

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