In-vehicle monitoring system (IVMS) data: An examination of patterns of risky driving behavior Kyla Retzer, NIOSH Gregory Kushnir, Cartasite

Transcription

1 In-vehicle monitoring system (IVMS) data: An examination of patterns of risky driving behavior Kyla Retzer, NIOSH Gregory Kushnir, Cartasite

2 Disclaimer The findings and conclusions in this report are those of the author(s) and do not necessarily represent the official position of NIOSH, CDC. Mention of any company or product does not constitute endorsement by the National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention.

3 The proportion of deaths due to transportation incidents in oil and gas extraction (OGE) is increasing 100% 80% 60% % 20% % Transportation On-site contact injuries, falls, fires/explosions, exposure Note: Fatality counts from BLS Census of Fatal Occupational Injuries N=1,485

4 In-Vehicle Monitoring Systems (IVMS) Records Data Date and Time Speed Acceleration/ ` Deceleration Seat Belt Use Measures Performance Measures driver performance against a predetermined set of ` parameters Changes Behavior Has shown to be effective in realizing immediate ` and positive effect on driver behavior. Motor Vehicle Crash Rates % Speeding 2 60% Miles Driven % 1 Gale et. al, 2012, Mora et. al, 2010, Velasquez, et. al, 2010, Matusalen, et. al, 2006, Ballard et. al, 2004, Jutten et. al, 2002, Cocianni & Taviansky, 1998); 2 Twilhaar, 2000; 3 Lopez, 2006, Twiilar, 2000.

5 NIOSH Evaluation of IVMS What we found: Risky driving behaviors 48% Goal Decline is greatest when supervisory coaching is included Bell et. al, 2017

6 Background of study Mutual interest in how IVMS data may be used to identify driving patterns across the OGE industry. NIOSH and Cartasite signed a data sharing agreement. Initial analysis focused on driving patterns throughout the day, depending on hours spent away from home base (vs. time of day).

7 Research Questions 1. Can IVMS data be used to identify trends in driver behaviors across companies in OGE? 2. Are there variations in driving behaviors between company types, states, and throughout the work day? 3. Can these data be used to guide journey management programs and other road safety management systems?

8 Methods Selected a subset of 8 oil and gas companies using vendor IVMS (Cartasite ROVR). Four large operators (one midstream). Four OGE service companies. o 1 large, 3 small (<100 employees). o Includes 1 water hauler. Primarily light duty vehicles (except water hauler).

9 Dataset 2,147 drivers. Driving that occurred Monday-Friday between October 11, 2015-October 10, Includes days with time away from home base longer than 3 hours. Excluded drive time beyond 21 hours away from home base. Includes over 39 million minutes of driving (over 651K hours).

10 Number of drivers by company type Number of drivers Company Range Operators Field Services

11 States where driving occurred State Proportion of driving Colorado 38.7% New Mexico 30.9% Wyoming 13.6% North Dakota 10.4% Utah 3.5% Other states 2.9%

12 Analysis Number of Speeding (SP), Harsh Braking (HB), and Harsh Acceleration units per hour while away from home base (driver s location between midnight and 3 AM). Calculated rate of SP and HB units per hourly interval based on minutes driven during each hourly interval. Examined rates between company types, hours away from home base throughout the day, and throughout the week.

13 Speeding and Harsh Braking Units Speeding (mph over posted speed limit) Harsh Braking (mph per sec) Units Minor to Moderate to Severe >20 >-15 3

14 Rate of SP units example Doug drove 30 minutes during hour 1 of his workday. Doug s IVMS captured 1 minor speeding event and 1 severe speeding event during this hour. Doug s total speeding units: 1+3= 4 units Doug s speeding unit rate per hour: 4 30 minutes X 60 minutes= 8 units per hour

15 Time spent away from home base (by company type, size and state)

16 Average daily time away from home base Avg. daily time away from home (hours) Company Range Operators Field Services

17 Average % of time per day driving (while away from home base) Avg. % of time per day driving Company Range Operators 28.3% 24.5% -33.3% Field Services 23.1% 20.8% %

18 Total minutes driven by hourly interval 8,000,000 7,000,000 6,000,000 5,000,000 4,000,000 3,000,000 2,000,000 1,000,

19 Speeding

20 Rate of speeding units (per hourly interval) greatest in New Mexico Colorado New Mexico North Dakota Utah Wyoming

21 Rate of speeding units (per hourly interval) higher for field services Rate of speeding units Company Range Operators Field Services * Field Services rate is 38% higher than operator rate

22 Rate of SEVERE speeding units (per hourly interval) also greater for Field Services Rate of severe speeding units Company Range Operators Field Services * Field Services rate is 43% higher than operator rate

23 Rate of speeding units by hourly interval

24 Rate of SEVERE speeding units by hourly interval

25 12.0 Rate of speeding units much greater for water haulers Rate of speeding units (all companies) Rate of speeding units (water hauler)

26 Rate of speeding units (per hourly interval) Increases slightly through the week Monday Tuesday Wednesday Thursday Friday

27 Rate of severe speeding units (per hourly interval) also increases slightly through the week Monday Tuesday Wednesday Thursday Friday

28 Harsh Braking

29 Rate of harsh braking units (per hourly interval) higher for Field Services Rate of harsh braking units Company Range Operators Field Services * Field Services rate is 19% higher than operator rate

30 Rate of harsh braking units (per hourly interval) also highest in New Mexico Colorado New Mexico North Dakota Utah Wyoming

31 Rate of harsh braking units by hourly interval

32 Rate of harsh braking units (per hourly interval) increase only slightly through the week Monday Tuesday Wednesday Thursday Friday

33 Limitations Harsh acceleration threshold too high; data not useful. Could not distinguish between on-duty and off-duty. Water-hauler company small, need larger number of drivers to draw conclusions. This is preliminary analysis.

34 Next steps Complete analysis. Conduct comparisons with time of day. Analyze data from TX. Publish scientific paper and trade journal article.

35 Conclusions: speeding Vary by state, highest in New Mexico. Highest in the first hour and the 10 th hour on. Speeding rates for water haulers may be a concern. Examine workplace policies that may contribute.

36 Conclusions: harsh braking Highest in New Mexico. Highest in the first hour and with additional peaks later in the day. Increase slightly through the week.

37 Recommendations Examine your own data for trends. Adjust journey management programs. Reach out to industry colleagues using these data in a predictive manner (i.e. using hot spots on maps).

38 Visit the NIOSH Exhibit Booth #418 New video Printed products NIOSH

39

40 Questions? Kyla Retzer, NIOSH Gregory Kushnir, Cartasite