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GIS Analysis Group

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Bike Week Crash Analysis

David Salzer Patrick Santoso

University of New Hampshire Technology Transfer Center

7/15/2014

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GIS Analysis Group

What is Bike Week? l Official name is “Laconia Motorcycle Week”

l First or second week of June

l 2013 attendance: 330,000

l Peak attendance in 2004: 430,000

l Laconia population: 20,000

l Brings $100 million to NH

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Started in 1916, Added to Gypsy Tour 1917

Photo Courtesy of Charlie St. Clair's and Jennifer Anderson's book, Laconia Motorcycle Week, Arcadia publishing, 2008

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Changed to a Weeklong Event in 1935

Photo Courtesy of http://www.weirsbeach.com/Largejpgs/bikeweekguidecovers.html

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Riot of 1965

Photo Courtesy of http://sillyverse.com/2013/01/11/summer-vacations-on-winnisquam/

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Today: Family Friendly Event

Photo Courtesy of http://sillyverse.com/2013/01/11/summer-vacations-on-winnisquam/

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Orientation to New Hampshire l Population (2013): 1,323,459

l Population is concentrated in the lower portion of the state which is considered “Boston Metro”

l State is predominantly rural

l Laconia is located in the “Lakes Region” on the banks of Lake Winnipesaukee (69 mi2)

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Concord

Manchester

Nashua

Portsmouth

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Orientation to Laconia

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Issue to be Solved l State and local police want to

reduce the number of motorcycle crashes through enforcement

l Where and when should police be stationed to see maximum reduction in crashes?

l Part of Driving Towards Zero

l Note: NH does not have a helmet law for riders over 18 years old

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Proposed Solution l Analyze historical motorcycle crash data

(2002-2011) sourced from local and state police to determine enforcement locations l Temporal analysis l Correct non-georeferenced data points

using NH Mosaic Parcel Map l Identify focus areas of high accident

density (kernel density analysis) l Hub and spoke analysis

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Step 1-Temporal Analysis of MC Crashes

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Average Crashes by Month

All Crashes by Year and Time of Day

Bike Week Crashes by Day and Time (3 Year Avg-’10,’11,’12)

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Average MC Crashes By Month

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0.0

20.0

40.0

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0 2 4 6 8 10 12

10 YRAVG

3 YRAVG

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All MC Crashes by Time and Year

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PEAK

High

Per

iod

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Bike Week MC Crashes by Day & Time

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PEAK

High Period

High

Per

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Friday Saturday Sunday Monday Tuesday Wednesday Thursday Friday Saturday Sunday

Bike Week MC Accidents By Day

Total MCAccidents

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Friday Saturday Sunday Monday Tuesday Wednesday Thursday Friday Saturday Sunday

Bike Week MC Accidents By Day

Total MCAccidents

Poly. (Total MCAccidents)

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3 Year “Top 15 Towns” For MC Crashes by Month

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Step 2-Analyze Crashes Spatially

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Geolocate “Centroid Crashes”

Kernel Density Analysis

Hub and Spoke Analysis

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Crash Point Data

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l Data is captured by local and state Police

l Standardized and compiled by NHDOT

l Available data: 2002-2011

l Data Captured l Location, data and time l Vehicles involved, property damaged l Influencing factors (speed, alcohol, etc.) l Injuries, fatalities l Make of vehicle

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Geolocating Applicable Crashes

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Kernel Density Analysis In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable. Kernel density estimation is a fundamental data smoothing problem where inferences about the population are made, based on a finite data sample. 1,2

1.Rosenblatt, M. (1956). "Remarks on Some Nonparametric Estimates of a Density Function". The Annals of Mathematical Statistics 27 (3): 832. doi:10.1214/aoms/1177728190.

2.Parzen, E. (1962). "On Estimation of a Probability Density Function and Mode". The Annals of Mathematical Statistics 33 (3): 1065. doi:10.1214/aoms/1177704472. JSTOR 2237880.

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What Does it Mean? l Density of crashes represented in a heat map

l Indicate areas where crashes most often happen

l Can be inferred that crashes will most likely happen in these areas again

l ArcMap function exists in: Spatial Analyst Tools>Density

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All MC Crashes (2002-2011)

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T2 Bike Week (2009, 2010, 2011) Bike Week (2002-2011)

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T2 Bike Week (2002-2011)

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Hub and Spoke Analysis

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Route 11

Route 25

Route 106

Winona Rd

Route 3

Route 3

Route 104

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What Was the Impact in 2013?

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Substantial Reduction in June 2013 Crashes

Reduction in Bike Week 2013 Crashes

Reduction in Bike Week 2013 High Period Crashes

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Average MC Crashes By Month

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0 2 4 6 8 10 12

10 Yr Average

3 Yr Average

2013

January February March April May June July August September October November December10 YR AVG 1.6 1.5 13.0 63.8 95.0 193.4 143.4 124.5 98.8 50.8 17.3 1.83 YR ABG 1.3 1.3 18.0 73.7 101.0 162.3 136.7 124.3 94.0 45.3 16.7 2.7

2013 0.0 0.0 15.0 59.0 91.0 149.0 96.0 126.0 68.0 30.0 9.0 1.0

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Bike Week 2013 MC Crashes by Day & Time

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Saturday Sunday Monday Tuesday Wednesday Thursday Friday Saturday Totals12:00 AM 0 0 0 0 0 0 1 1 21:00 AM 0 0 0 0 0 0 0 0 02:00 AM 0 0 0 0 0 0 0 0 03:00 AM 0 0 0 0 0 0 0 0 04:00 AM 0 0 0 0 0 0 0 0 05:00 AM 0 0 0 0 0 0 0 0 06:00 AM 0 0 0 0 0 0 0 0 07:00 AM 0 0 1 0 0 0 0 0 18:00 AM 0 0 0 0 0 0 0 0 09:00 AM 0 0 0 0 0 0 0 3 310:00 AM 1 0 0 1 0 0 0 1 311:00 AM 0 2 0 0 0 1 1 1 512:00 PM 1 1 1 0 0 1 1 2 71:00 PM 0 1 0 0 1 0 1 2 52:00 PM 0 2 0 0 0 0 1 1 43:00 PM 1 0 0 0 0 1 0 0 24:00 PM 0 0 1 0 0 0 3 2 65:00 PM 0 0 0 0 0 0 2 1 36:00 PM 0 0 0 0 0 1 1 2 47:00 PM 0 1 0 0 1 0 1 0 38:00 PM 0 0 0 0 0 0 0 0 09:00 PM 0 0 0 0 0 0 0 1 1

10:00 PM 0 0 0 0 0 1 0 111:00 PM 0 0 0 0 1 0 0 0 1

Daily Total 3 7 3 1 3 5 12 173 year avg 10 3 7 8 11 9 14 18

Bike Week 2013 Time/Day

l Total Crashes l ’09,’10,’11 AVG: 77 l 2013: 51 crashes l 33% reduction l All days except Sunday

lower in 2013

l High Period (Wed-Sat) l ‘09,’10,’11 AVG: 52 l 2013: 37 l 29% reduction l All days lower in 2013

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Questions?

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