ODOT Wildlife Hotspots Study

RESULTS OF STATEWIDE RESULTS OF STATEWIDE ANALYSISANALYSISJuly 21, 2008July 21, 2008

Melinda Trask Oregon Department of Transportation, Geo-Environmental Section, Salem ORFrancesca Cafferata-Coe, Jessica Burton, Ellen Voth, and John Lloyd, Mason, Bruce & Girard, Inc., Portland OR

hwye06y

This is where you give a very brief overview of the Wildlife Movement Strategy working group. This is our basic plan of attack (part our our groups original work plan). When ODFW is done with the 'Priority Wildlife Movement Areas, our group will have completed Tier 1.

Types of Wildlife Collision Data Focused Road Kill Observations

Possible to get accurate location and species info. Most expensive

Expert Opinion Good for first cut; precursor to focused studies Subjective; not empirical

Crash Records Used for national statistics Limited subjective reporting Data quality cannot be verified

Dispatch Carcass Reports Most comprehensive option Data quality cannot be verified

Oregon's Highway Animal-Vehicle Collisions

CRASH RECORDS: Avg. 400 wildlife collisions per year, past 14 years

About 5,500 records statewide Less than 3% of all crash reports in Oregon 15 fatalities & 117 serious injuries in 14 years

Crash records represent only a small portion of actual animal-vehicle collisions nationally (less than 10% of actual; literature)

Dispatch Carcass Records 6 times more data in similar period Represents avg. 2,600 wildlife collisions

per year, past 12 years About 32,000 records statewide in OR

(12 years)

ODOT Wildlife Collision Prevention Plan Addressing wildlife passage is supported by the Governor and

ODOT’s current mission and goals, and particularly within the values of safety, accountability, and environmental stewardship.

Current lack of information - we cannot adequately address the problem. Do we have a significant statewide road kill problem or just some

areas? Need to prioritize wildlife movement corridors and highway

barrier problem areas to make science-based decisions and cost-effective, versus ad-hoc.

Need better tools to adequately address wildlife passage. Non-regulated but supported by FHWA, ODFW, USFWS,

CETAS, nationwide attention.

Density:

low

medium

high

ODOT Wildlife Collision Hot Spot Analysis Uses existing carcass pick-up records Statewide, analytical approach Identify high frequency wildlife-vehicle collision zones Conducted pilot study in D10 to fine tune methods and

determine the feasibility of statewide analysis

USHwy

Data Preparation - Methods 3 different types of record keeping Wildlife Incident Reports, call = RDKILL Animal Type, Deer & Elk Consistent Dates, 12 yeas of data (1995-2006) Location, +/- 0.5 mile Link Location to GIS Coordinates

CAD_NUM CALL DATE LOCATION UNIT S

95309256 RDKILL 10191995 5925 WALLACE RD HWY2 1

95309392 RDKILL 10201995 HELMICK ROAD / 99 SR ;12600 HELMICK RD 21A P

95309598 RDKILL 10201995 21.5 228 SR 3A20 P

95312278 RDKILL 10231995 5.9 22 SR 3A26 1

95312329 RDKILL 10231995 SHERWOOD @ 99W SR MP 15.2-15.8/ ; 19025 SW PAC HWY 3A52 P

95312331 RDKILL 10231995 HWY 212 / FORMORE CT 4A30 P

Data Preparation - ResultsOriginal # Records 31,595 (100%)

Step 1 - Data Processing Narrowing Acceptable Parameters 25,216 (80%) (20% reduction)

Cut out records older than 1995, duplicate records, non deer/elk, low precision (> 0.5 mi)

Tabular Information Problems 21,335 (68%) (12% reduction) Poor location, highway nomenclature, or MP Not enough information in recorded data MP not referenced

Step 2 - Linkage to GIS GIS Mapping Problems 17,824 (56%)* (11% reduction)

Route ≠ ODOT Highway number

* Final number of "good" records used in data analysis.

GIS Challenges 2 highway numbering systems in use Signed Highways State Highway Routes

(ODOT internal system) Mileposts based on

Routes not signed hwys Mileposts not unique on

signed hwys Dispatch data generally

refer to Routes Final dataset reduced to

only records with equal hwys:route relationship

Final Data SetNumber of Records By Region By Year

Region Year R1 R2 R3 R4 R5 All 1995 16 73 1 0 0 90 1996 32 415 3 0 0 450 1997 33 400 3 0 5 441 1998 30 435 18 60 33 576 1999 33 444 411 336 145 1,369 2000 75 4 466 317 200 1,062 2001 81 5 591 389 317 1,383 2002 56 4 525 394 586 1,565 2003 86 370 675 675 872 2,678 2004 81 315 705 751 1,003 2,855 2005 54 379 866 918 1,094 3,311 2006 67 112 167 621 1,061 2,028 TOTAL 644 2,956 4,431 4,461 5,316 17,808

Nearest Neighbor Analysis 1st cut to see if clustering is non-random

Ripley’s K Distribution Gives indication of scale of clusters

Kernel Density Evaluation Shows location of clusters by density

Analytical Methods

Results: Nearest Neighbor Analysis Carcass reports

occur significantly closer together than would be expected by chance

Does not identify where the clusters occur

REGION R1 R2 R3 R4-5

Confidence Interval 99% 99% 99% 99%

n 100 100 100 100

t 2.63 2.63 2.63 2.63

Expected Mean NN Dist. (ft) 3951 2944 807.1 1201

Standard Deviation (ft) 199.1 77.72 13.75 10.87

Standard Error of the Mean 19.91 7.772 1.375 1.087

CI 1/2 width (ft) 52.28 20.41 3.61 2.86

Lower Confidence Limit (ft) 3899 2923 803.4 1198

Upper Confidence Limit (ft) 4003 2964 810.7 1204

Observed Mean NN Dist.(ft) 3484 2215 490.7 731.9

Nearest Neighbor Index* 0.88 0.75 0.61 0.61

* If <1.0, indicates significant clustering

Results: Ripley’s K Distribution

Looks at a range of scale distances Shows significant clustering of WVCs at all distances Does not identify cluster locations

Ripley's K-Function10 distance bands (d = 1 mile)

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Scale distance (d) in miles

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Low er CI

Upper CI

Region 3 shown (other Regionsessentially the same)

Results: Kernal Density Evaluation Analogous to a histogram

of reports per unit area with infinitely small bins

Produces an estimate of risk for each point. Highlights highway segments

with higher density probabilities than others

Results Depend on: Density of points Relative proximity of points Study area Method of categorizing Ranking or # “bins”

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Kernel Density Estimate*High Density Low Density

kj stip2008_11_pts_final

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D Approx milepoint

Discussion This study did not address why hotspots are found

in these areas. vehicle speed, traffic volume, movement barriers, adjacent

habitat structure, animal distribution, travel corridors, etc. Necessary to make sound management decisions

ODOT can pay for wildlife crossing improvements Justified under PD-04 FHWA Enhancement program (Category 11) Oregon Transportation Plan (Goal 4.1.1) SAFETEA-LU Section 148 (approved uses of safety funds)

Hazard Elimination Program (HEP) Highway Safety Improvement Program (HSIP)

Next Steps ??1. Convert data to vector and link to Hwy/MP

2. Clean-up data for Regions – spreadsheet

3. Uses in Planning

4. Uses in Project Development

5. Uses in Safety Projects??

RESEARCH NEEDS Detailed case-studies Design Options Characterize existing highway crossings and barriers

Wildlife Connections Conference

hwye06y

Design workshop limited in space. Mindy saved a space for one designer from Tech Services Traffic Safety Unit. Please send Simon an "application" form so we can get invitation/registration sent out.