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Real-time Estimation of Accident Likelihood for Safety Enhancement

Real-time Estimation of Accident Likelihood for Safety Enhancement. Jun Oh, Ph.D., PE, PTOE Western Michigan University March 14, 2007. Background / Motivation. Is it possible to predict occurrence of accidents? Maybe NOT. / Almost impossible

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Real-time Estimation of Accident Likelihood for Safety Enhancement

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  1. Real-time Estimation of Accident Likelihood for Safety Enhancement Jun Oh, Ph.D., PE, PTOEWestern Michigan UniversityMarch 14, 2007

  2. Background / Motivation • Is it possible to predict occurrence of accidents? • Maybe NOT. / Almost impossible • Are there certain traffic conditions that lead to more accidents? • Maybe YES. • Then, is it possible to identify such traffic conditions? • What will be possible indicators?

  3. Contents • Previous Studies • Traffic Dynamics and Accident • Empirical Example • Accident Likelihood Estimation • Issues on Accident Study • Advanced Surveillance System

  4. So far, previous studies... • Analyzed long term historical data • To identify relationships between traffic variables or geometric elements and accidents • off-line studies • Incident detection and incident traffic management • after-incident

  5. Objectives • To enhance traffic safety under ITS • To identify traffic conditions leading to more accidents • Real time • Before accident • To estimate accident likelihood

  6. Occurrence of Traffic Accidents Traffic Dynamics Environment Driver Characteristics Accident Vehicle Characteristics

  7. Accident Indicator Accident occurs Implication starts Traffic Dynamics (Indicator) Normal traffic condition Disruptive traffic condition T-x T TIME

  8. Empirical Example • Freeway traffic data • I-880, California • Volume, Occupancy, and Speed (double-loop) • 10-second periods from upstream detector stations • Accident profiles (52 accidents) • Traffic Variables • Occupancy, Flow, and Speed • 5 minute Mean and STD

  9. Pattern Classification • Two traffic conditions • Normal traffic condition: a 5-minute period apart from traffic accident (more than 30 minutes apart) • Disruptive traffic condition: a 5-minute period right before an accident • Non-parametric density estimation • kernel smoothing technique • Best indicator: STD of speed

  10. Estimation PDF

  11. Bayesian Model for Accident Likelihood • P(A/X) = Posterior probability that given traffic measurement belongs to traffic conditions leading to an accident occurrence • P(A) = Prior probability that given traffic measurement belongs to disruptive traffic condition • P(N) = Prior probability that given traffic measurement belongs to normal traffic conditions

  12. Estimation of Accident Likelihood

  13. Real-time Application

  14. Identification of Accidents • The percentage of time when P(A/X) was above the given threshold

  15. GIS Database for Enhanced System • Traffic Accident Data Mapping • Linear Referencing & Dynamic Segmentation • Reconstruction of highway segments • Detector location and accident location • Other Characteristics • Weather • Highway Geometry • Real-time Traffic Data

  16. Database Example Real-time Traffic Data Accident location and type

  17. Drive safely Caution! Traffic Unstable Possible Application Framework Real-time traffic measurement with highway geometry and weather Real-time estimation of accident likelihood Is traffic condition stable? Yes No Provide safety information at upstream via VMS

  18. Issues on Accident Study • Accident data availability and accuracy • Need more data • Accurate accident occurrence time • Accident duration • Other measures? • Wide-area detection • Individual vehicle tracking • Need better surveillance systems

  19. An Advanced Surveillance System • Present traffic surveillance systems • mostly use inductive loop detectors (ILDs) • have significant limitations (e.g. point estimates) and errors • reduce the ability to effectively manage and control freeway and arterial traffic systems, and to implement ATMIS • Advanced sensor systems • Integration of weather and surface sensors • Individual vehicle detection for details • Vehicle reidentification techniquesutilizing existing and future infrastructure

  20. Matching Inductive Vehicle Signatures Vehicle Reidentification • Volume • Occupancy • Speed • Vehicle Types • Section Density • Section Delay • Travel Time • Level of service • Lane-by-lane travel time • Lane changing pattern

  21. Concluding Comments • Speed variance can be a good surrogate • Traffic dynamics reflects hazardous factors • Temporal spatial speed variation • Advanced surveillance systems may provide better exposure • Lane-by-lane travel time • Lane-changing pattern • Possible to identify traffic conditions leading to more accidents (Accident Likelihood) • Integration of traffic, weather, and geometry information

  22. Thank you Q & A Jun Oh jun.oh@wmich.edu

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