Validation of Highway Engineering Data Quality on Wisconsin Crash Reports. Andrea Bill

Transcription

1 Validation of Highway Engineering Data Quality on Wisconsin Crash Reports Andrea Bill

2 Outline Introduction Project Goal Good Data Quality Characteristics Common Issues Leading to Poor Data Quality Research Methodology Results Conclusions/Recommendations

3 Introduction Anecdotal evidence & literature point to crash report interpretive errors being common place Quality data necessary for analysts to draw any meaningful conclusions Police officers have a limited background in highway engineering Training Instruction manuals Common sense ehow.com

4 Project Goal Compare officer responses on MV4000 crash reports against researcher responses at the same location Determine where differences in data interpretation may require additional training or clarification Limited to highway engineering data fields

5 Good Data Quality Characteristics safety.fhwa.dot.gov FHWA s Crash Data Improvement Program Guide (CDIPG) published in April 2010 According to CDIPG, crash databases should have the following Six Pack Timeliness Accuracy Completeness Consistency/Uniformity Integration with other databases Accessibility of data This project focused on accuracy, completeness, and consistency of engineering data fields of the Wisconsin crash database

6 Good Data Quality Characteristics Accuracy Internal validation (electronic forms) External validation (this research) Completeness no blank entries Consistency uniformity among files All WI agencies use MV4000 National standard is Model Minimum Uniform Crash Criteria (MMUCC)

7 MMUCC Joint NHTSA & Governors Highway Safety Association publication Voluntary guideline, but compliance necessary for States to receive Traffic Safety Information System improvement grants (Section 408) 2010 NHTSA Traffic Records Assessment found WI MV4000 compliant in 90.6 percent of data fields and 53.1 percent in attributes Field: Traffic Control Attribute: Signal, Stop, Yield, etc.

8 Common Issues Leading to Poor Data Quality People & Training Processes Technology

9 People & Training According to the CDIPG, one refrain commonly heard from police is that crash forms are being completed just for insurance companies WisDOT MV4000 Instruction Manual Primary training resource for WI officers Last updated in 1998 Brief and vague concerning engineering fields No baseline definition of when to flag hills or curves Poor definition of traffic barrier No discussion of roundabouts

10 Processes Paper crash forms still used by approximately 45 percent of WI agencies in 2010 Paper forms require manual data entry Time consuming Constrain resources Lead to backlogs affecting timeliness of data Personnel under pressure to process backlogs are prone to errors Paper crash report backlogs in Texas. (GAO )

11 Technology CDPIG strongly recommends States adopt new and innovative technologies to improve crash data quality Two most commonly advocated technologies Electronic crash forms GPS based smart maps for location ID

12 Electronic Crash Forms Allow validity checks to catch inadvertent errors and blank fields Clean, clear crash diagramming Increases speed of submission, and therefore availability of data for analysis WI State Record planners predict 100 percent electronic reporting statewide by 2015 reportbeam.com

13 GPS/GIS Location Identification MMUCC recommends that highway engineering data studied in this research be generated by linking to roadway inventory data Several States, including Maine and Ohio, already have such systems in place Badger TraCS (most common electronic report system in WI) has interfaces for point-and-click location ID, but not yet used Maine DOT Map Viewer System (GAO )

14 Other Crash Data Quality Studies Several studies have investigated commercial vehicle crash data quality, concluding that officer training needed improvement Mickee (2008) audited ~1000 Massachusetts crash reports Used police, highway department & university auditors Reports were not verified by site visits Concluded that auditing created detailed statistical results, but was prohibitively time consuming Importantly, no other known studies incorporated investigation of verifiable conditions in the field

15 Research Methodology MV4000 data fields Highway classification (derived based on municipality population) Access control Traffic-way Horizontal curvature Vertical curvature Traffic control Easily observable permanent highway features Independent of other crash variables (e.g. date, time, weather, driver, etc.)

16 Research Methodology Analysis timeframe: 1/1/09 to 12/31/10 Geographic boundaries Dane & Rock Counties Representative of municipality types in WI 27 intersection locations Minimum of 5 crashes Apparent discrepancies in three or more fields 664 total crash reports Madison Janesville Beloit Site visit locations Bing.com

17 Site Visits USH 51 at W Delavan Drive (Janesville) Observed ~250 foot radius around intersection Marked appropriate field attribute for each intersection approach In accordance with WisDOT Training Manual when possible used engineering judgment when manual did not offer clear guidance Curve: Alignment change of 15 degrees or more Hill: Noticeable change in elevation Thinking from perspective of an officer they would not be measuring curve radii or grades

18 Data Coding Binary system 1 correct officer response 0 incorrect officer response Each crash report analyzed, using narratives and diagrams to determine correct approach where crash occurred Field Report Coding DOCTNMBR N/A HWYCLASS U CITY 1 ACSCNTL PART 0 ROADHOR C 1 ROADVERT 1 TRFCWAY ND 1 TRFCNTL1 TS OP 1 TRFCNTL2 NONE 0 Example of data coding for a report

19 Overall Results Highway classification In general corresponded well Exceptions often seen on municipal boundaries (e.g. Fish Hatch at Greenway Cross) Traffic control In general fairly accurate Most common error was reporting no control when in fact vehicles were subject to signals because of proximity to the intersection Access control, horizontal & vertical curvature, and trafficway were more problematic Correctly Reported Percent of Total TOTAL % HWYCLASS % ACSCNTL % ROADHOR % ROADVERT % TRFCWAY % TRFCNTL % TRFCNTL % Global Reporting Accuracy

20 Results: Access Control Locations with Partial Access Control Reporting Accuracy (number Location correct) Percentage USH-14 & Pontiac 8 of % STH-113 & CTH M 0 of 8 0% US-51 & CTH CV 2 of % W Main St. & O'Keeffe 2 of % USH-51 & Pflaum 45 of % USH-18 & CTH PD 22 of % Total 79 of % Locations with No Access Control Reporting Accuracy (amount 21 Remaining Locations correct) Percentage Total 401 of % USH 51 at Pflaum Rd (Madison) Overall accuracy only 72.3 percent Researchers noted a wider variability in officer responses at locations with at least one partially controlled facility (no private driveways, but atgrade intersections e.g. USH 51 through Madison) Results confirm a sharp decrease in accuracy at partially controlled locations

21 Results: Horizontal Curvature Eleven sites had a curve present on at least one approach that should have been flagged Accuracy at locations with a curve present was much lower than those without a curve Location Number of Observations ROADHOR Accuracy STH 26 & CTH N % USH-14 & N Pontiac % USH-51 & W Delavan % Maple & Fourth % US-51 & Anderson (CTH CV) % W Main & O'Keeffe % Century & Allen % University & Allen % Cumulative Total % Other 16 Locations %

22 Results: Vertical Curvature Location Number of Observations ROADVERT Accuracy STH 26 & CTH N % USH-51 & W Delavan % Johnson & Park % Fish Hatchery & Post % Fish Hatchery & Caddis % US-14 & Deming Way % University & Allen % Cumulative Total % Other 20 Locations % Seven sites had a hill present on at least one approach that should have been flagged Accuracy at locations with a hill present was much lower than those without a hill

23 Results: Traffic-way Divided without Barrier Totals Percentage Correctly Reported % Divided with Barrier % Not Divided % Blank 5 0.9% One Way 2 0.4% Total % Not Divided Totals Percentage Correctly Reported % Divided without Barrier % Blank 1 2.3% Divided with Barrier 0 0.0% One Way 0 0.0% Total % When facility was divided without a barrier, most common error was reporting a barrier Roadways with no division (i.e. two lane road with painted centerline) were reported as divided over half the time One Way Totals Percentage Correctly Reported % Not Divided 1 2.7% Divided without Barrier 0 0.0% Divided with Barrier 0 0.0% Total % True divided with barrier traffic-way

24 Results: Roundabouts Three roundabout intersections were studied The most inaccurate fields were trafficway, horizontal curvature, and traffic control S Towne & Industrial 8th St & Springdale Commercial Ave & N Thompson Roundabout Total HWYCLASS 100.0% 100.0% 100.0% 100.0% ACSCNTL 69.2% 100.0% 83.9% 82.0% ROADHOR 30.8% 66.7% 29.0% 34.0% ROADVERT 100.0% 100.0% 96.8% 98.0% TRFCWAY 53.8% 50.0% 54.8% 54.0% TRFCNTL1 92.3% 60.0% 86.2% 85.1% TRFCNTL2 61.5% 100.0% 72.4% 72.3%

25 Results: Roundabouts Horizontal curvature reporting errors were tied to the circulating lane Majority of crashes flagged with a curve involved at least one vehicle in the circulating lane

26 Results: Roundabouts Traffic-way errors were most commonly associated with incorrectly using not physically divided or divided with a barrier Low traffic control accuracy most often resulted from officers indicating no traffic control, when the correct response should have been a yield sign

27 Results: Completeness & Agency Trends On the 664 crash reports in the study, there were only 16 blank entries (out of a possible 4,648) No notable trends were found based agency type (e.g. police, sheriff, state patrol), however, only 6 locations had multiple responding agencies

28 Conclusions Low accuracy for access control at partially controlled facilities indicates a lack of understanding by officers for what qualifies as partial control When hills or curves are present on at least one approach, officers are flagging them even when they are not the site of the crash, which is incorrect Low traffic-way accuracy is a result of misunderstanding what constitute divided roadways and barriers Roundabout-specific inaccuracies were especially noteworthy in the horizontal curve and traffic-way fields

29 Recommendations Update WisDOT Instruction Manual and develop pocket-sized cheat sheet versions In-service officer training with case-studies of commonly misinterpreted data fields Generate data fields by linking to roadway inventory and hardware data

30 Recommendations Implement 100 percent electronic crash reporting in Wisconsin Utilize GIS/GPS location identification technologies Update data fields to comply 100 percent with MMUCC, including roundabout-specific attributes

31 Thank You We would be happy to answer any questions!