Realtime Estimation of Accident Likelihood for Safety Enhancement

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

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Contents

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

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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

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Objectives

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

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Occurrence of Traffic Accidents
Traffic Dynamics
Environment
Driver Characteristics
Accident
Vehicle Characteristics
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Accident Indicator
Accident occurs
Implication starts
Traffic Dynamics (Indicator)
Normal traffic condition
Disruptive traffic condition
T-x
T
TIME
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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

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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

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Estimation PDF
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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

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Estimation of Accident Likelihood
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Real-time Application
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Identification of Accidents

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

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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

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Database Example
Real-time Traffic Data
Accident location and type
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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
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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

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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

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Vehicle Reidentification

• Volume
• Occupancy
• Speed
• Vehicle Types
• Section Density
• Section Delay
• Travel Time
• Level of service
• Lane-by-lane travel time
• Lane changing pattern

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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

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Thank you Q A Jun Oh jun.oh_at_wmich.edu