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Reducing Roadway Crashes: GIS-Based Evaluation of the Effectiveness of

Selective Law Enforcement

Dr. Andrew Graettinger, Jenna SimandlDr. Randy Smith, Tim Barnett

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Introduction

• According to the National Highway Traffic Safety Administration, in 2012:– Speeding: 30% of fatal crashes• Cost to society = 40.4$ billion

– Driving under the influence: 31% of fatalities

while– Seat belt usage saved an estimated

12,174 lives

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Alabama Crash Facts

• In 2011: 814 fatal crashes, killing 899 people– Speeding:

27% of fatal crashes

– DUI: 27% of fatal crashes

– Lack of seat belt usage in 42% of the crashes

DUI

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ight

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ting

0%

5%

10%

15%

20%

25%

30%

Percentage of Fatal Crashes per Specific Driver Contributing Cir-

cumstance

Per

cent

age

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Introduction

• Selective Law Enforcement Campaigns: intensify law enforcement at high crash locations– Attempt to change negative driver

behaviors that contribute to most severe crashes

Alabama Selective Enforcement Campaign

• Collaborative effort: Department of Public Safety and DOT

• Troopers worked extra-duty shifts• Selective Enforcement data (SE data)

is documented in invoices and timesheets– One year analysis from available data– August 1, 2010 through July 31, 2011

Date Worked Trooper Hours Salary Per Diem Miles Initiative Troop Post5

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Data Sources• The Center for Advanced Public Safety

(CAPS) at The University of Alabama• Research center Developed software

for:– Data collection– Data storage– Research platforms and dashboards for

statistical analysis

• Used by law enforcement, traffic engineers, and other disciplines of business

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Mobile Officers’ Virtual Environment (MOVE)

• Designed to enable officers to use laptops in their cars for automated data entry

• 80,000 Electronic Citations (eCitations) per month–No structured location information

required

• 10,000 Electronic Crash (eCrash) reports per month

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Mobile Officers’ Virtual Environment (MOVE)

• State trooper vehicle location is polled every 30 seconds (GPS trace data)

• eCite and GPS trace data stored in relational databases– Structured Query Language (SQL)

• eCrash data stored in the Critical Analysis Reporting Environment (CARE)– Research Dashboard

Project Overview

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Verify Selective Enforcement Locations

Integrate eCrashes

Geolocate eCitations

Integrate officer location GPS trace data

Evaluate citation and crash trends before and during selective enforcement

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Geolocating eCitations• eCitations do not require GPS data

or structured location information• eCitations do have a timestamp– Temporal Join: Join eCitations to officer

trace GPS point based on closest timestamp

1 2 3

*Fictitious data

Map of officer activities

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Processing eCitation and GPS trace data

• Query citations and GPS trace data for the participating officers and Aug. ’10 – July ‘11– 475,214 eCitations– 37.6 million GPS points

• Big Data! Leverage SQL

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Geolocating eCitations SQL QueryJoin on UserID = UserID and select smallest DATEDIFF() within 600 secs

141646

> 600

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Geolocating eCitations• Temporal join was accepted if time

difference was within 600 seconds (10 minutes)– 68.6% of eCitations were successfully

geolocated

• 49 of the officers in the year dataset did not have any GPS trace data– Early years of GPS trace data implementation– Excluding those officers, 72.6% of eCitations

were successfully geolocated within 600 seconds• 95% of those within 30 seconds

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Selective Enforcement Work

UserID, TimestampLatitude & Longitude

Trooper NameUserID

Date WorkedHours WorkedDifference between

Successive GPS Points

Date, Shift Start & Shift EndShift Length (Hours Worked)

Identify when the difference was greater than 6 hours: indicates end of shift.

Calculate time between GPS Points

GPS Data: Selective Enforcement

Data:

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SELECT [UserID] ,[Latitude] ,[Longitude] ,[GPSTimeUTC] ,LEAD([GPSTimeUTC], 1, NULL) OVER (PARTITION BY [UserID] ORDER BY [GPSTimeUTC] ASC) AS [NextTime] ,DATEDIFF(second, [GPSTimeUTC], LEAD([GPSTimeUTC], 1, NULL) OVER (PARTITION BY [UserID] ORDER BY [GPSTimeUTC] ASC)) AS DifferenceFROM [AURA].[dbo].[JSGpsCoordsToGpsGroupToUsers]

Calculating Diff. Between GPS Points

SQL Query: LEAD Function

UserID Latitude Longitude GPSTimeUTCA x y 7/1/09 2:33:58A x y 7/1/09 2:34:28A x y 7/1/09 2:34:58A x y 7/6/09 6:20:03A x Y 7/6/09 6:20:33B x y 7/3/09 9:32:47

NextTime7/1/09 2:34:287/1/09 2:34:587/6/09 6:20:037/6/09 6:20:33NULL

Diff303044550530NULL

GPS Trace Data in GIS

• GPS points on selective enforcement shift days:

2.98 million points• “Selective

Enforcement Overtime” points:

1.65 million points16

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Hotspot Analysis

• Traditional Hotspot Analysis (HSA): Euclidean distance– Potentially useful tools in GIS

• Hotspot Analysis (HSA) along the roadway network

• Desirable to utilize SQL– Summation and Grouping Technique• Effectively HSA

– First: join GPS data to Route-Milepost information in GIS

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SQL Hotspot Analysis

• Centered the analysis on a particular MP: LEAD and LAG functions

• 3 different HS indicators: 1/10 mile, ½ mile, 1 mile

• Completed in 2.5 hours

Route MP Number of Events

A 3.0 0A 3.1 49A 3.2 1A 3.3 6A 3.4 0A 3.5 430A 3.6 645A 3.7 61A 3.8 5A 3.9 1A 4.0 0A 4.1 0A 4.2 0A 4.3 8

Sum

486

1131

1192

1197

1198

1198

1198

1149

1156

1150

1150

720

75

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Verifying Selective Enforcement Locations

• Import SQL HSA results into GIS• Classify using Natural Breaks

Symbology– Lowest value from the largest class as the

limit

• One Mile Bucket was chosen at the indicator

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Half-Mile Bucket (Sum ≥ 1887) One-Mile Bucket (Sum ≥ 2885)

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21 Located Selective Enforcement Locations

• 26 locations investigated in GIS with aerial imagery and Google Maps Street View– Refined to 21

locations

• 10 urban locations• 11 rural locations

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Crash Frequency and Citation Counts at Selective Enforcement

Locations• eCrash data exported to GIS from

CARE– One year before and one year during

selective enforcement

• Crash frequency recorded for each location

• eCitation count recorded for each location– Select by attribute: only Speed, DUI, or

seat belt related citations

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Including Crash Severity

• Equivalent C injury level crashes recorded (severity indicator) as well

KABCO Letter Injury Severity Level

Equivalent C Level Crashes

K Fatal Injury 5 C injuriesA Incapacitating Injury (visible/carried from the scene) 3 C injuriesB Non-incapacitating Injury (bruises/abrasion/swelling) 2 C injuriesC Possible Injury (minor/pain/fainting) 1 C injuriesO Property Damage Only 0 C injuries

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Crashes Citations Equivalent C Level Crashes

Location One Year Before

One Year During

Percent Change

One Year Before

One Year During

Percent Change

One Year Before

One Year During

7 2 0 -100.00% 93 153 64.52% 0 08 2 0 -100.00% 31 23 -25.81% 0 0

12 33 19 -42.42% 1512 963 -36.31% 27 521 22 14 -36.36% 91 257 182.42% 10 1411 10 7 -30.00% 180 180 0.00% 6 419 17 14 -17.65% 75 505 573.33% 5 1110 24 20 -16.67% 268 1133 322.76% 0 95 15 13 -13.33% 4 41 925.00% 8 1

13 16 14 -12.50% 916 511 -44.21% 8 99 16 15 -6.25% 33 698 2015.15% 4 4

15 16 15 -6.25% 214 826 285.98% 10 103 22 22 0.00% 886 1243 40.29% 11 8

16 2 2 0.00% 33 46 39.39% 6 220 9 9 0.00% 144 343 138.19% 2 21 13 15 15.38% 311 223 -28.30% 7 74 43 50 16.28% 35 170 385.71% 21 156 7 9 28.57% 371 365 -1.62% 1 1

18 13 19 46.15% 229 568 148.03% 7 92 19 30 57.89% 140 161 15.00% 7 11

17 3 5 66.67% 27 91 237.04% 0 014 13 34 161.54% 868 1599 84.22% 8 10

Paired Difference t-test Results:21 SE Locations

Difference between Before and During SE P-value Conclusion

Number of Crashes 0.390

85% confident that selective enforcement did decrease crashes when urban and rural were separated.

Number of Citations 0.01898% confident that the number of issued citations increased (α = 0.02)

Number of Equivalent C Crashes 0.287

Beginning to see trends in effectiveness of selective enforcement

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Selective Enforcement Recommendations

• Define the scope and intent of the SE campaign

• Identify high-crash locations eligible for SE

• Plan timing and logistics• Organize community outreach and media

publicity• Train participating officers for

implementation• Document SE activities

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Identify high-crash locations for selective enforcement

• Use three years of crash data

• 2012-2014 crash dataset in CARE

• Hotspot indicators: Minimum # of crashes in 5 miles

• Additional considerations: severity, shoulder width, number of lanes

75 in 5m 50 in 5m

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Plan timing and logistics

• Crash frequency analysis for all locations for months– 3 waves in a year campaign is suggested

• March, July, and December

– Can subsidized overtime for the year• Increase especially during the 3 identified months

• Crash frequency analysis at each location for time of day– Example: Location A should have increased

selective enforcement patrol during 7-8 AM, 3-4 PM, and 10-11 PM

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Community Outreach and Media Publicity

• Initial Press Conference

• Public Service Announcements

• Billboards• Paper

Advertisements (brochures, posters, etc.)

Linesof

Communication

Representative from DOT (i.e. traffic safety engineer)

Representative from Troopers

Representative from local Mayor’s office

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Train Officers on Implementation

• New and existing equipment– From simple radar to new mapping tools

• Where: Understand locations to patrol–Nearby posts patrol surrounding areas for

increased productivity

• When: Organize schedules – for overtime during peak times of day,

and – over three intense waves in respective

months of the year

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Documentation of Selective Enforcement Activities

• Selective Enforcement Data: – document specific location of increased

patrol; – start and end times of the shift

• eCitations: – populate GPS coordinates; – potentially program as a validation step

• GPS data: – Functioning GPS in each vehicle– On when working, otherwise turned off

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Conclusion• Geolocated 72.6% of eCitations; 95% of which

were within approximately 30 feet• Dealing with big data– Making use of GPS data– Leveraging relational databases with SQL

• Located 21 selective enforcement locations• 85% confident that crashes were reduced in both

urban and rural locations, analyzed separately• Severity of crashes is trending down, locations

analyzed collectively• Up to 99% confident that the number of issued

citations increased• Techniques exist to improve the effectiveness of

selective enforcement

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Selective Enforcement Works

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References• US Speeding Fact: http://www-nrd.nhtsa.dot.gov/Pubs/812021.pdf• US DUI Fact: http://www-nrd.nhtsa.dot.gov/Pubs/811870.pdf• US Seatbelt Fact: http://www-nrd.nhtsa.dot.gov/Pubs/811851.pdf• AL Crash Facts: Center for Advanced Public Safety. (2011). 2011 Alabama traffic

crash facts. Alabama Department of Transportation. Retrieved February 22, 2015• Introduction Pictures:

– http://www.dailymail.co.uk/news/article-2157763/Drivers-fined-flashing-headlights-warn-oncoming-cars-police-speed-trap.html

– http://www.segabg.com/article.php?id=737637– http://www.thejournal.ie/what-county-is-the-worst-offender-for-not-wearing-

seatbelts-1361152-Mar2014/• KABCO Injury scale: Federal Highway Administration. (2011). Highway Safety

Improvement Program Manual; Chapter 4.0: Planning. Washington, DC: U.S. Department of Transportation. Retrieved May 28, 2015

• Literature used for recommendation campaign:– Federal Motor Carrier Safety Administration. (2007). Guidelines for

Developing a High-Visibility Enforcement Campaign to Reduce Unsafe Driving Behaviors among Drivers of Passenger and Commercial Motor Vehicles. Washington, DC: US Department of Transportation.

– National Highway Traffic Safety Administration. (2008). Speed Enforcement Program Requirements. Washington, DC: US Department of Transportation.