1. Real-time Estimation of Accident Likelihood for Safety Enhancement Jun Oh, Ph.D., PE, PTOE Western Michigan University March 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 Accident Traffic Dynamics Environment Vehicle Characteristics Driver Characteristics

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

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 Threshold # accidents identified % accidents identified % time* 0.00025210099.8 0.00045098.296.2 0.00063465.452.9 0.00082955.827.9 0.00102446.217.4 0.00121936.510.6 0.00141121.24.5

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

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 techniques utilizing existing and future infrastructure

20. Vehicle Reidentification Matching Inductive Vehicle Signatures Volume (veh/interval) Travel Time (sec) Downstream OfframpLane 1 HOV Lane 2 HOV Lane 3Lane 4Lane 5Lane 6 Up-stream Up-stream Lane 1 HOV-81 veh 34.37 sec 18 veh 35.02 sec 6 veh 43.65 sec --- Lane 2--3 veh 41.86 sec 203 veh 32.62 sec 4 veh 31.43 sec 5 veh 28.93 sec - Lane 3- - -10 veh 29.50 sec 164 veh 34.47 sec 16 veh 29.31 sec - Lane 4----43 veh 32.01 sec 108 veh 35.63 sec 2 veh 31.32 sec Lane 527 veh 37.58 sec ---4 veh 29.34 sec 21 veh 36.01 sec 69 veh 38.00 sec Lane 610 veh 46.51 sec ----11 veh 51.49 sec 16 veh 44.12 sec Lane 7-----2 veh 42.29 sec 5 veh 37.34 sec  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