Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
7. Drivers’ safety behavior research using in-vehicle technologies
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
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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
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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
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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
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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
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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)
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
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?
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
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