SPATIAL AND TEMPORAL PATTERNS OF FATIGUE RELATED CRASHES IN HAWAII

Transcription

1 SPATIAL AND TEMPORAL PATTERNS OF FATIGUE RELATED CRASHES IN HAWAII By Karl E. Kim Eric Y. Yamashita Hawaii CODES Project Traffic Records Forum July 29 - August 2, 2001 New Orleans, Louisiana

2 Overview Background Data & Methods Findings Countermeasures Conclusion

3 Background Fatigue-related crashes difficult to identify; Underreporting & misclassification; Occur most often in the early morning hours with a smaller peak in the mid-afternoon; Nationwide, younger drivers (<30 of age) accounted for 2/3 of drowsy driving crashes; Majority of fatigued drivers are males.

4 Typically occur on long straight stretches of roadway; 1 percent of all the crashes nationwide are cited as being fatigued-related In Hawaii, 3 percent of all the crashes that occurred from 1986 to 1995 were fatiguerelated crashes According to NHTSA, alcohol was consumed in nearly 20% of all fatiguerelated crashes.

5 Fatigue and Alcohol On 4 hours of sleep, 1 can of beer can have the same impact as a six-pack; 18 hours of sustained wakefulness produces performance impairment equivalent to.05 BAC; 24 hours without sleep =.10% BAC Source: National Sleep Foundation, 2000

6 Data & Methods Crash Outcome Data Evaluation System (CODES) Project Funded by the U.S. D.O.T., NHTSA. Data includes linking Crash, EMS, Hospital, and Claims Data Linked with Automatch Hawaii s Traffic Safety GIS Statistical Analysis + Spatial Analysis = Problem Identification

7 Findings Driver Characteristics Gender Age Fatigue Related Non-Fatigue Related Z-test Frequency Percent Frequency Percent Male Female Total & over Total Mean Age Std. Deviation t-test Prob.= The are 3.2 times more males than females involved in fatigue related crashes v Sixty-two percent of the fatigued drivers were under 30 years old.

8 Findings Vehicle Characteristics Fatigue Related Non-Fatigue Related Z-test Frequency Percent Frequency Percent Crash Type Single Vehicle Pedestrian Bike/Moped Vehicle-to-Vehicle Total Vehicle-to-Vehicle Crashes Head On Rear End Sideswipe Same Dir Sideswipe Opp. Dir Angle Same Dir Angle Opp. Dir Broadside Other Total Vehicle Manuever Prior to Crash Straight Ahead Changing Lanes Merging Overtaking Slow/Stopping Right Turn Left Turn Other Total percent of the fatigue related crashes are single vehicle crashes. 89 percent of the vehicles involved in crashes were going straight at the time of the crash.

9 Findings Spatial Factors Fatigue Related Non-Fatigue Related Z-test Frequency Percent Frequency Percent Freeway/Highway Yes No Total Urban/Rural Urban Rural Total Intersection Yes No Total Vertical Road Alignment Level Unlevel Total Horizontal Road Alignment Straight Curved Total percent of fatigue related crashes occur on urban roads. 78 percent occur on straight roads 64 percent occur on level roads

10 Temporal Factors Freq. (Fatigue) Late Night or Early Morning Late Afternoon Freq. (No Fatigue) Fatigue Related Non- Fatigue Related 0 0 0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Hour of the Day Two peak periods exist - Late night/early morning and Evening. 58 percent of the fatigue-related crashes occur during the hours of 12 AM to 6 AM.

11 Temporal Factors Freq. (Fatigue) Freq. (No Fatigue) Fatigue Related Non- Fatigue Related Monday Tuesday Wednesday Thursday Friday Saturday Sunday Year 42 percent of fatigue-related crashes occur on the weekend. The number of non-fatigue crashes decline on the weekends. The weekend fatigue related crash involves more alcohol, and risk-taking behavior.

12 Findings Logistic Regression Model of Fatigued Drivers Involved in All Vehicle Crashes Variable Parameter s.e. Prob. X- Square Odds Ratio Intercept < Early Morning (12:00 AM to 6 AM) < Driver Age (15 to 29) < Rural Area < Highway < Male < Serious or Fatal Injury < Weekends < Level Grade < Straight Alignment < Speeding < Likelihood Ratio X (with 10 Df, p=0.0001) 8.8 times more likely to occur in the hours 12AM to 6AM. 1.4 times more likely to occur on weekends. 2.1 times more likely to occur on rural roads. 1.2 times more likely to occur on highways.

13 Injury Outcomes 14 Time of Day Effects for Fatigued-Related Crashes Required EMS Transport Vs. Non-EMS Transport Frequency Require EMS No EMS -2 1AM 3AM 5AM 7AM 9AM 11AM 1PM 3PM Time of Day 5PM 7PM 9PM 11PM Again two peak periods exist for fatigue-related crashes

14 Fatigue Related Non-Fatigue Related Z-test Frequency Percent Frequency Percent Driver Injury (EMS Score) DOA Extremely Critical Critical Severe Minor None Total Required EMS Transport Yes No Total percent of the fatigue-related crashes required EMS Transport, as oppose to 8.5 percent for nonfatigue related crashes.

15 Single Vehicle Fatigue-Related Crashes by Crash Type Required EMS Transport EMS as a Crash Type Frequency % Frequency % of Crashes Guardrail Culvert Bridge / Overpass Underpass/Bridge Support Building Island/Median/Curb Embankment/Retaining Wall Fence Utility Pole Traffic Signal/Stop Sign Tree Hydrant Animal Total Percent of the single vehicle fatigue-related crashes involved collisions with utility poles, and 30 percent of those required EMS Transport.

16 Hospital and Claims Analysis of At Fault Drivers and Fatigue-Related Crashes At Fault Driver Hospital Stay Days Hospital Billing ($) Total Claim ($) Mean SD Mean SD Mean SD Fatigue-Related Non-Fatigue-Related The mean hospital stay days for at fault fatigue-related drivers was 2.5 days as opposed to non-fatigue which was 1.4 hospital stay days. The mean dollar in hospital billing for at fault fatigue-related drivers was 1.85 times greater than for non-fatigue-related drivers. The mean dollar in insurance claims for at fault fatigue-related drivers was 1.69 times greater than for non-fatigue-related drivers.

17 Fatigue Related CBD Fatigue-related crashes have greater spatial dispersion than non-fatigue crashes.

18 Single Vehicle Fatigue-Related Crashes 3D mapping provides another means of visualizing & locating areas to implement corrective measures Location of Greatest Problem

19 Single Vehicle Fatigue Related Crashes on Highways (12:00 AM to 6:00 AM) Use statistical model to identify significant areas of concern. Use GIS to map locations of concern. Focus measures at these locations to improve conditions that cause fatigue related crashes.

20 Education: Countermeasures Raise public awareness about the risks of driving while fatigued. Focus education on the young male population. Educate shift workers on the problems associated with fatigue related crashes. Educate those with active lifestyles that restricts sleep. Engineering: Installation of rumble strips in problem areas. Increase lighting in areas of high fatigue related crashes. In the vehicle alerting devices.

21 Conclusion Understanding both the spatial and temporal factors associated with fatigue-related crashes can aid in the prevention of these events. Using Crash data and Statistical / Spatial Analysis Techniques provides a better understanding of the fatigue-related crash.