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7. Drivers’ safety behavior research using in-vehicle technologies

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  • Neale,V.L, Klauer, S.G., Knipling,R.R., Dingus,T.A., Holbrook, G.T., Petersen, A., The 100 Car Naturalistic Driving Study: Phase 1- Experimental Design (2002) DOT HS 809 536
  • McLaughlin,S.B., Hankey,J.M., Klauer, S.G., and Dingus, T.A.(2009), Contributing Factors to Run-Off-Road Crashes and Near-Crashes: Final Report DOT HS 811 079
  • McLaughlin,S.B., Hankey,J.M., Klauer, S.G., and Dingus, T.A.(2009), Contributing Factors to Run-Off-Road Crashes and Near-Crashes: Final Report DOT HS 811 079
  • Undesired driving events are events where the driver exceeds a certain threshold of speed or acceleration. for example sudden braking and lane changing, Excessive acceleration, sharp turning, and so on.
  • Undesired driving events are events where the driver exceeds a certain threshold of speed or acceleration. for example sudden braking and lane changing, Excessive acceleration, sharp turning, and so on.
  • Ti identify the accordance of driving events we used in-vehicle data recorder named the Green-Box. The green-box manufactured by GreenRoad Technologies is able to identify driving events. It consists with Sensors for acceleration and speed and a processing unit. The information is transmitted in real-time to a server that analyzes it and generates a driver profile. A report generator provides the driver with feedback via text messaging, e-mail or web-based reporting. Real time In-vehicle feedback is also available.
  • So, lets me briefly walk you through the type of information that this technology can generate. In this web report we can see the days of the month along the X axis and the number of trips taken that day along the Y axis, with each square representing a specific trip. The trips are color coded by the trip safety level. A green trip has only a few events, and a red trip consists with many events. The cumulated data is then used to generate risk indices for the specific driver.
  • Tomer Toledo,Oren Musicant and Tsippy Lotan. In-vehicle data recorders for monitoring and feedback on drivers’ behavior. Transportation Research Part C: Emerging Technologies Volume 16, Issue 3 , June 2008, Pages 320-331
  • Musicant, O., Lotan, T., Toledo, T., 2007. Safety correlation and implications of an in-vehicle data recorder on driver behavior. In: Preprints of the 86th Transportation Research Board Annual Meeting, Washington, DC Evaluating the Safety Implications and Benefits of an In-Vehicle Data Recorder to Young Drivers Tsippy Lotan (OR YAROK - Israel ), Tomer Toledo ( Israel Institute of Technology - Israel )
  • Daniel V. McGehee , Mireille Raby , Cher Carney , John D. Lee and Michelle L. Reyes . Extending parental mentoring using an event-triggered video intervention in rural teen drivers. Journal of Safety Research Volume 38, Issue 2 , 2007, Pages 215-227
  • first we evaluate on whether the events frequency is approximately the same for every trip duration. We expected the answer to be yes. but in reality for short trips the events frequency is higher and as the duration becomes longer the trend is leveled. now the question is – why? Why are we seeing this behavior in events frequency?
  • The answer is in this figure. The figure shows the events frequency at each minute from the beginning of the trip for trips, with duration of 10 minutes. This graph shows the same information for trips with duration of 15 minutes. We can see that in the beginning and ending the frequency is higher. This phenomenon was repeated for other trips durations as well. So we have a constant event count in the trip edges and when we divide this constant by the duration we receive this shape.
  • So lets see how good is the model fit in the middle of trip. The upper left graph shows the probability in percentage for 0 events for each trip duration. the blue line shows the observed probability and the black lines indicate the model acceptance region. The figure on the upper right shows the probability of one event. And bellow you can see the probability of 2 and 3 events. So you can judge for your self were the negative binomial-model has a good fit and when the fit is not so good.
  • Now, for the trip edges we used the Chi square test to evaluate the model fit. The figure shows for each cell the observed and expected frequency . There is a remarkable resemblance. yet the formal test rejected the assumption that the observed distribution is actually Negative-Binomial. Yet, with such a large sample the assumption can be very easily rejected.
  • Next we locked at the events frequency at each time of day. As you can see night time has more events then day time. The partition of the day for 2 segments seems suitable here. And we chose the partition you see because it maximized the likelihood function.
  • Differences in events frequency between the days of week seemed not meaningful in comparison to the differences in the previous figure so we didn’t consider it as an important variable to be included in the model.
  • In this figure you can see the result of a negative-binomial regression. Driver Gender and Time of day are the explanatory variables for events frequency. In both trip segments, the interaction between gender and time of the day is significant. when moving from day to night Males' Events frequency increased more prominently than females‘.
  • McCartt A.T.; Shabanova V.I.; Leaf W.A. (2003). “Driving experience, crashes and traffic citations of teenage beginning drivers,” Accident Analysis and Prevention, 35, (3), pp. 311-320
  • Driving patterns of young drivers within a graduated driver licensing system
  • Carlo Giacomo PRATO, Tsippy LOTAN, Tomer TOLEDO (2009). Intra-familial transmission of driving behavior: evidence from in-vehicle data recorders. Transportation Research Board Annual Meeting 2009 Paper #09-1205
  • Transcript

    • 1. Drivers’ safety behaviour research using in-vehicle technologies Oren Musicant, Hillel Bar-Gera, and Edna Schechtman Ben-Gurion University of the Negev May 20, 2009
    • 2. In vehicle sensors
      • Location (GPS)
      • Speed
      • Acceleration
      • Lane position (Video)
      • Headway (Video, Radar)
      • Seat belt usage and forces
      • Airbag activation
      • Etc.
    • 3. Safety uses of in vehicle technologies
      • Decision advisory/support
        • Intelligent speed adaptation
        • Lane position and headway
      • Retrospective   
        • Record continuously
        • Record undesirable events (Green Box)
        • Record accidents (Black box)
    • 4. Outline
      • The 100-car naturalistic study
      • Undesirable events and GreenBoxes
      • Undesirable events and accidents
      • Undesirable events reduction
      • Statistical analysis of undesirable events
      • Driving learners
    • 5. The 100-car naturalistic study
      • Project sponsors : NHTSA & Virginia Department of Transportation
      • Subjects: 100 Vehicles, 241 primary and secondary drivers
      • Study duration: 12-13 months,
      • Information collected:
        • Multiple Sensor's data: ~2,000,000 miles, ~43,000 hours of data
        • Crashes, Near crashes, Incidents
        • Self reported data – Questionnaires
    • 6. Sensors network (Neale et al, 2005)
      • accelerometer (longitudinal & lateral kinematic)
      • headway detection system
      • side obstacle detection
      • An incident box to allow drivers to flag incidents
      • A video-based lane tracking system
      • five video cameras
      • GPS
      • Vehicle computer info (optional) : ABS on/off, cruise speed control….
      video cameras
    • 7. The role of inattentiveness (Neale et al, 2005)
      • Inattentiveness is defined as:
      • Secondary task
      • Fatigue
      • inattention to the forward roadway
      • “ non-specific eye glance”
      Percentage of events for attention by severity level
    • 8. The role of inattentiveness (Neale et al, 2005) Frequency of occurrence of secondary tasks for crashes, near crashes and incidents
    • 9. The role of inattentiveness (Neale et al, 2005) Frequency of occurrences in which the contributing factor was wireless device use by level of severity
    • 10. Contributing Factors to Run-Off-Road Crashes and Near-Crashes (McLaughlin et al, 2009) based on the 100 study
    • 11. Run-Off-Road Crashes and Near-Crashes by age group (McLaughlin et al, 2009) Error bars indicate standard error
    • 12. Examples for additional analysis done with the 100 info
      • Whether, Lighting factors
      • Road type, Traffic density factors
      • Driver properties (questionnaires)
      • Seat belt use
      • Demographic properties (age, gender, experience..)
      • Drivers’ Behaviors That Contributed to crashes Involvement
      • Characteristics of driving patterns (acceleration, speed, steering) in crash events
      • Additional information at : http://www.nhtsa.gov
    • 13. Undesirable events and Green Boxes From extremely large records of raw data of speed, acceleration, lane position… To information about meaningful behavioral patterns (Undesirable driving events) In order to provide feedback to drivers and monitoring tools for parents and fleet safety managers By product – more information for researchers.
    • 14. What Are Undesirable Driving Events ? Sudden braking Lane changing Acceleration Sharp turning
    • 15. Why Undesirable Driving Events ?
      • Turning is more common for at fault crashes ( McGwin & Brown 1999 )
      • Older drivers have relatively more crashes related to left-turn, gap acceptance, and lane changing maneuvers ( Chandraratna, 2003 )
      • “ poor turn maneuver” is involved in 15% of the crashes (DFT 2006 )
      • 10% of “typical lane change” crashes involved large trucks changing lanes and light vehicles going straight ( NHTSA 2003 )
      • Lane maneuvering are more common for at fault crashes ( McGwin & Brown 1999 )
      • Older drivers have relatively more crashes related to lane changing maneuvers ( Chandraratna, 2003 )
      • fatigue is associated unintentional drifting-out-of-lane events ( Fell & Black, 1997 )
      • Braking is more common in non-fatal crashes ( Zhang et al, 2000 ) meaning driver succeeds to prevent the hard consequences.
      • Inattentive drivers compensate on slower reaction time by braking harder ( Liu & Lee, 2005 , Hancock et la, 2003 )
      • Sudden braking and following too close involved in 8% of car crashes (DFT, 2006 )
    • 16. Real time Driver profile Reports generator Real-time feedback Sensors The “ Green Box ” Green Box
    • 17. Each square is one trip Color indicates safety level Cumulated information used to create drivers indices Web - Based Reports
    • 18. Undesirable events and accidents
      • What is the correlation between crash involvement and the occurrence of driving events
    • 19. Study #1: information from the 100 study (NHTSA, 2009) Drivers where categorized to Safe/Moderate/Unsafe by crashes/near crashes frequency
    • 20. Longitudinal Deceleration (NHTSA, 2009) Unsafe drivers perform unsafe braking (>0.3 g) more then Safe & moderate drivers in all acceleration thresholds
    • 21. Lateral Acceleration (NHTSA, 2009) Unsafe drivers perform unsafe turning (>0.3 g) more then Safe & moderate drivers in all acceleration thresholds
    • 22. Swerving (NHTSA, 2009)
      • Swerving (Yaw rate) is measured in degrees per second and is an indication of the rate of the vehicle’s rotation around the vertical axis
      • Unsafe drivers perform unsafe Swerving (degrees per second >3 ) more then Safe & moderate drivers in all acceleration thresholds
    • 23. Study #2: The linkage between driving events and crash involvement (Toledo et al, 2008)
      • Crashes information :
      • ~32 months prior to in vehicle installation
      • Classified to “at fault” y/n by the insurance company
      • IVDR information :
      • 8 weeks measurement
      • No feedback was provided
      • Assumption
      • With the lack of feedback measured driver behavior represent the typical past 32 month behavior.
      • Risk Index:
      • Assuming the count of events per driving time is a random variable from a negative binomial distribution with Lambda and Alpha, The risk index for driver i is F(Events (i) ,Driving time (i), Lambda, Alpha)
      • Poisson regression Model:
      • Yi is the number of crashes (all and fault only) for driver i. Ri is the IVDR risk index. b0 and b1 are parameters. Mi and Ti are the number of months for which the driver’s crash records were available
      • (i.e. length of the available crash history), and the monthly number of hours driven during the blind-profile stage, respectively
    • 24. Study #3 & 4: The linkage driver class (safe, moderate, unsafe) determined by the greenbox and crash involvement Study #3 : Safety correlation and implications of an in-vehicle data recorder on driver behavior. n (Musicant, ,Lotan, Toledo, 2007) Study #4 : Evaluating the Safety Implications and Benefits of an In-Vehicle Data Recorder to Young Drivers ( Lotan & Toledo 2005)
    • 25. Undesirable events reduction To what extend the use of the green box contribute to safety behaviors?
    • 26. Extending parental mentoring using an event-triggered video intervention in rural teen drivers (McGehee et all, 2007) Intervention : In vehicle display weekly e-mails for parents and teens
    • 27. When technology tells novice drivers how to drive Blind Profile Vs. Feedback (Not published paper)
      • 32 teens
      • Variable of interest: Events Frequency (Events per minute)
      • Blind Profile: 0.11 (s.d.= 0.094)
      • Feedback 0.045 (s.d.= 0.035)
      • Mean EF decrease in 59% (Paired t = 4.3, p<0.01)
    • 28. Statistical analysis of undesirable events
      • Toledo et al 2008
      • Musicnt et al, 2007
      • Lotan & Toledo 2005, 2006
      How often ? When ? Who ? Detailed & Objective Event frequency is safety surrogate We already know We don’t know
    • 29. Events frequency Vs trip duration … Based on 117,195 trips of 109 drivers
    • 30. Timing of Undesirable Events
    • 31. Model fit: Middle trip
    • 32.  ^2 = 94.56 df = 11 p = 1.86301E-14 * Numbers above the bars are cell chi-square values Model Fit: the Trip Edges
    • 33. Event Frequency By Hour Night Day
    • 34. Event Frequency By Weekday
    • 35. Frequency, Gender & Time
    • 36. Driving learners
      • Minimum driving requirement
      • Nighttime restrictions
      • Length of accompanied period
      Context of Accompanied driving
    • 37. Amount of drivng time (Lotan & Toledo 2007) 31 young drivers (20 males, 11 females) 2842 driving hours ,8246 trips The intensity of accompanied driving time is about as half of the intensity in the solo solo driving accompanied driving 4.45 2.02 hours per week 13.22 4.82 trips per week 21.3 25.2 trip length
    • 38. Temporal distribution of driving time (Lotan & Toldo 2007) Time at day Days at week Where the parent can or when he should?
    • 39. Intra-familial correlations (Prato et al, 2009)
    • 40. Summary
      • Green Boxes measurements - Based on known factors of safety
        • Speed
        • Acceleration
        • Lane discipline
        • Safe distance
      • Reinforce known knowledge
        • Boy & Girls
        • Night time & Day time
      • Tools to achieve new knowledge
        • Trips edged vs. middle trip
        • NB distribution
        • Correlation between parents and teens
        • The importance of feedback
      • Support decision making
        • GDL schema
    • 41. Future projects
      • Correlation between drivers perceptions and behavior
        • Organizational settings: Management commitment, Work pressure
        • Attitudes toward safety
        • Aggressive behavior
      • Correlation between safety and Fuel economy
      • Patterns in driver’s behavior over time
      • GIS projects
        • Intersections
        • Speed cameras

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