Utilizing Crash Data Performance Metrics to Drive Improvement

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1 Utilizing Crash Data Performance Metrics to Drive Improvement Richie Frederick, Program Manager Florida Department of Highway Safety and Motor Vehicles Thomas Austin, Management Analyst Florida Department of Highway Safety and Motor Vehicles

2 Establishing Crash Data Performance Metrics Establishing Crash Data Performance Metrics 1

3 Objectives Identify the need for crash data performance metrics Identify the approach for establishing measures for accuracy and completeness Establishing Crash Data Performance Metrics Using the measures to make improvements Lessons learned and next steps for expanding the program 2

4 Motoring Environment The Department of Highway Safety and Motor Vehicles is the official custodian for driver license, motor vehicle, and crash record data. In 2016, Florida had the following: 20,148,654 Estimated Population 16,568,874 Licensed Drivers 20,472,415 Vehicles Registered 3,133,609 Citations Issued 50,597 Sworn Law Enforcement Officers 3

5 Motoring Environment In calendar year 2016, Florida had the following: 711,666 Crashes Reported 254,245 Injuries Reported 3,176 Total Fatalities 10 Approved e Crash Vendors 92% of Crash Reports Received Electronically 4

6 Background In 2007, Electronic crash reporting started as a pilot project with the Florida Highway Patrol In 2008, the program was rolled out statewide Florida used TRCC grants to make improvements on specific data elements, which proved beneficial 5

7 Background Florida lacked formal performance measures, which was identified during records assessments Identified the need to create more comprehensive measures to: Track accuracy and completeness over time Identify areas of ambiguity on the report and in the crash manual Determine where to focus program resources Setting goal of 5% improvement per year 6

8 Background 7

9 Baselines REPORTING PERIOD TYPE ITEM DESCRIPTION SCORECARD 10/01/ /30/2016 Accuracy Event 93.40% 10/01/ /30/2016 Accuracy Person 97.83% 10/01/ /30/2016 Accuracy Vehicle 97.86% 10/01/ /30/2016 Completeness Event 95.92% 10/01/ /30/2016 Completeness Person 96.44% 10/01/ /30/2016 Completeness Vehicle 97.15% 8

10 Baseline Goals TYPE Final 5% Goal 5% Goal 5% Goal Increase A E 93.73% 94.05% 94.35% 0.94% A P 97.93% 98.04% 98.14% 0.31% A V 97.97% 98.07% 98.17% 0.30% C E 96.12% 96.32% 96.50% 0.58% C P 96.62% 96.79% 96.95% 0.51% C V 97.29% 97.42% 97.55% 0.41% 9

11 Approaching Accuracy Measures 10

12 Accuracy Approach Evaluated 226 fields on crash form to quantify accuracy Identified relational fields to judge accuracy based on the consistency of the data elements 32 checks for accuracy 11 checks for event page 10 checks for person page 12 checks for vehicle page 11

13 Accuracy Example If First Harmful Event is 14 Motor Vehicle in Transport, then 12

14 Accuracy Example the crash report should have at least two vehicle sections

15 Approaching Completeness Measures 14

16 Completeness Approach Evaluated 226 fields on crash form to quantify completeness Identified principal and relational fields, which would require values in a corresponding field to judge completeness 53 checks for completeness 19 checks for event page 18 checks for person page 16 checks for vehicle page 15

17 Completeness Example If Injury Severity is 2 (possible), 3 (nonincapacitating) 4 (incapacitating), 5 (fatal), or 6 (non traffic fatality), then 16

18 Completeness Example Source of Transport to Medical Facility cannot be null 17

19 Making Improvements Conducted 11 train the trainer workshops around the state on improving crash reporting, with specific emphasis on data elements identified by the measures Attended by 618 law enforcement officers representing 144 agencies 18

20 Intersection to Junction If Type of Intersection is 2 (four way intersection), 3 (T intersection), 4 (Yintersection), 5 (traffic circle), 6 (roundabout), or 7 (five point or more) then 19

21 Intersection to Junction First Harmful Event Relation to Junction cannot be 1 (non junction), 4 (driveway/alley access related), or 5 (railway grade crossing) 58.34% accuracy rate statewide 20

22 Motorcycle Endorsements If Person is 1 Driver and Vehicle Body Type is 11 Motorcycle then 21

23 Motorcycle Endorsements Required Endorsements cannot be 3 No Required Endorsement 73.13% accuracy rate statewide 22

24 Data Visualizations 23

25 Data Visualizations 24

26 Data Visualizations 25

27 Data Visualizations 26

28 Data Visualizations 27

29 Data Visualizations 28

30 Data Visualizations 29

31 Next Steps Continue the review of state approved data collection software applications to recommend improved data validation capabilities to improve the accuracy, completeness, uniformity, and timeliness of crash data Conducting train the trainer workshops with content tailored to issues identified by performance metrics 30

32 Next Steps Create a crash uniformity measure comparing the current Florida crash data elements and attributes to the new MMUCC 5 th Edition standard Develop a crash dataset completeness measure to perform trend forecast analysis for the total crashes expected from counties and agencies Develop accuracy and completeness metrics for Uniform Traffic Citations 31

33 Next Steps 32

Conclusion Performance metrics allow tracking over time and establishing goals to continually improve Compared fields on crash form to quantify accuracy

34 Conclusion Performance metrics allow tracking over time and establishing goals to continually improve Compared fields on crash form to quantify accuracy and completeness scores Used measures to create improvements; clearer guidance to reporting officers and tailored training content Areas of focus improved 33

35 Thank you for participating in this session. Richie C. Frederick, Program Manager Bureau of Records, Crash Data & Record Systems Support Thomas Austin, Management Analyst Bureau of Records, Crash Data & Record Systems Support 34

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