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Businesses Business owners want customers: – Loyal/repeat customers (familiar/old) Know the way, just need reminder – New customers (unfamiliar/new) Need to find the way – Both customer groups rely upon signs for navigating to the business Business owners also rely upon signs for advertising and marketing
Citation preview
Statistical Analysis of the Safety Impacts
of Digital On-Premise Signs
H. Gene Hawkins, Jr., Ph.D., P.E.Texas A&M University
Presented at theNational Signage Research and Education
ConferenceCincinnati, Ohio; October 11, 2012
• Information, information, + more information– Traffic signs, in-vehicle displays, GPS
navigation, billboards, business signs, etc.
• As more information is presented, there is more competition for attention
The Problem
Businesses• Business owners want customers:– Loyal/repeat customers (familiar/old)
• Know the way, just need reminder– New customers (unfamiliar/new)
• Need to find the way– Both customer groups rely upon signs for
navigating to the business• Business owners also rely upon signs
for advertising and marketing
Types of Signs• Traffic signs– In the roadway right-of-way– Regulate, warn, and guide traffic
• On-premise signs– On business building or property– Get traffic to business
• Off-premise signs– Away from business– Generate interest in business
Competition for Attention • Drivers deal with:– Vehicle control
• Steering + brakes– Cockpit controls
• Environment• Music• GPS
– Navigation• Short-term• Long-term
• Other challenges– Increased traffic, higher speeds, more
controls
– Distractions• Phones (talking)• Phones (texting)• Phones (email)• Reading (books)• Listening (books)• Eating• Drinking• Make-up• Passengers• Other vehicles
Recent Example• From Bryan-College Station Eagle
How to Attract Attention
• Make it:– Bigger– Brighter– Add motion/movement– Increase contrast
• Applies to all visual stimuli– Traffic signs, business signs, people,
objects, etc.
Signage Advances• Recent technologies allow:– Electronic signs or panels– Custom messages– Animation– Video
• LEDs and other advancements provide more features at lower costs
• Potential concern that electronic displays increase driver distraction
20122003
The Challenge• Many issues combine to create a challenge:
– Advanced sign technologies are relatively new– Have spread rapidly– While traffic sign research is sponsored by
public agencies, there is not comparable research program for business signs
• Concerns over traffic safety– Base issue: Do electronic message signs create
a distraction problem that increases crash risk?– Result: Has led some local agencies to establish
sign codes that are based on opinion more than scientific fact
Our Research Study• Goal:–Determine if the installation of digital
on-premise signs has an impact on traffic safety in the area around such signs
• Highlights:– Scientific procedure– Robust crash dataset– Large sample size of sign locations– Advanced statistical analysis methods
Digital Sign Definition• Target: on-premise digital sign– Located on business property– Sign uses electrical display– Provides changeable message
Related Research• Limited research conducted on business signs• On-premise signs:
– Mace (2001): synthesis of literature• Hypothesized that distraction potential of signs could
compromise safety• Hypothesized benefits as navigational aid• No data collection to support or refute claims
– Wachtel (2009): synthesis suggested on-premise signs affect safety more than off-premise signs • Because locations and elevations of on-premise signs
might be closer to the road users• Angles of on-premise signs may be out of vision core and
require extreme head movements• Conclusions of both are based on educated
judgment rather than scientific analysis
Off-Premise Signs• FHWA study by Molino et al.
(2009)–Meta analysis of 32 previous studies– Focus on billboards–Most, but not all, of previous research
shows negative safety impact• Although safety issues not
resolved, there is more analysis of off-premise signs than on-premise
Knowledge Gap• Little knowledge about safety impacts
of on-premise signs–What is known is not based on
detailed scientific analysis– Inconsistent findings–Weaknesses
• Inadequate sample sizes• Inappropriate statistical analysis methods• Short time frames for analysis (before & after)
• At present, cannot define safety impacts of digital signs
Research Approach• Highlights:– Collect sign data• Specific to on-premise digital signs• Installation date and location are critical
– Collect crash data at locations where signs are located• Crash information: date, location, type,
etc• Need several years of data
–Develop statistical analysis procedure– Perform safety analysis
Sign Data Acquisition• Required significant effort• Initial attempts not successful– Asking for too much
• Refined request based on crash data dates and states– Installed in 2006 or 2007
• Insures adequate before and after periods– Located in California, Illinois, Maine,
Minnesota, North Carolina, Ohio, or Washington• These are states with crash data
Sign Data Sets• Sign datasets acquired from two
companies– #1: 2,953 sites with 27 variables• Variables: date, address, cross-street,
road/traffic information, etc.– Road/traffic information not used
– #2: 63 sites with 10 variables• Sign locations had to be confirmed
through Google Earth/Map to be usable
Sign Data Processing• Raw sign data required significant
processing to be useable• Sites eliminated due to:– Installed before 2006 or after 2007
• These limits provided sufficient date in both before and after analysis periods
• Year of installation not included in analysis– Location could not be confirmed in
Google Earth or Google Maps (Street View)• Analysis area defined as
within 0.1 mile of target sign
Crash Data Background
• Comprehensive crash data is limited and hard to obtain– Belongs to agencies– Protected information for liability reasons– Expensive to maintain
• National databases– FARS: Fatal Accident Reporting System• NHTSA owned – no info on crash location
– HSIS: Highway Safety Information System• FHWA owned – significant related information
HSIS Crash Data• HSIS provided the best means of
performing a detailed national analysis of crash impacts due to digital signs
• Contains crash, roadway, and vehicle information
• Operated/maintained by FHWA• Widely used for safety research • Multi-state data– California, Illinois, Maine, Minnesota, North
Carolina, Ohio, and Washington– We chose to focus on CA, NC, OH, and WA due
to limited sign locations in IL, ME, and MN
HSIS File Types• Crash files:
– Location, date, time, light, weather conditions, severity, number of related vehicles, collision type
• Driver and vehicle files:– Driver gender, age, contributing factor (possible
casual factor), vehicle type• Road and traffic files :
– Traffic data – Average Daily Traffic (ADT), speed limit – Road data – number of lanes, lane and median width,
shoulder width and type, rural or urban designation, and functional classifications.
• All files are linked to provide for robust analysis– Example: crash frequency and traffic volume can be
combined to present a crash rate at a location
Data Analysis Steps• General steps– Confirm location of signs– Convert sign location to crash
location– Evaluate site qualification factors– Conduct statistical analysis
• Each is explained in upcoming slides
Confirm Sign Location• Use Google Map and Google Earth– Verify location of each target sign• Used street address provided in sign data
set– Confirm that it is a digital sign– Confirm that is it an on-premise sign– Confirm still in place (as of the date of
the Google Street View image)–Measure milepost from county
boundary• Used to link location to crash data
Confirm Sign Location• Labor intensive and time consuming
activity• Used student workers to process data
Combine Sign and Crash Data
• Sign and crash data use different location systems– Sign data based on street address– Crash data based on route and
milepost• Convert sign location to route and
milepost format and combine with crash location data set
Site Qualification Factors
• We only retained the sign sites satisfying the following conditions:– Located in CA, NC, OH, or WA– Installed between 2006-2007– Located on the major roads –With at least one crash record in the
before or after period
Sign Data Yield Rates• After processing sign and crash data,
the number of sites usable for analysis was a fraction of initial number– Initial sign data sets: 2,953 + 63 = 3,016
–Overall yield rate = 126/3016 = 4.2%
State CA NC OH WA AllInitial 86 249 372 413 1,120Install Date 2006-2007 27 94 178 159 458Digital+Major Rd+Crashes 5 28 60 33 126Yield Rate 5.8% 11.2% 17.2% 8.0% 11.3%
Statistical Analysis Options
• Options for analyzing safety impacts:– Before-after study
• Crashes in the period before improvement compared to crashes in the period after the improvement
• Provides more direct evaluation– Cross-sectional study
• Crashes on a facilities with the improvement compared to crashes on similar facilities without the improvement
• Different facilities rarely identical in all features
• We chose to use before-after study
Types of Before-After • Naïve before-after study– Simple comparison– Results may be influenced by
factors not accounted for– Not a preferred analysis method
• Before-after study with control group– Control group helps to account for external
influences that could affect results– Requires additional data for control locations– Difficult to identify appropriate control locations
with same characteristics• Before-after study using the empirical Bayes
(EB) method
Empirical Bayesian Method• Recommended as preferred method in Highway
Safety Manual• Combines short-term observed crash numbers
with crash prediction model data to obtain a more accurate estimation of the long-term crash mean– Example: a new driver has no crashes during first
year of driving– Typical new driver has 0.08 crashes per year– Not reasonable to expect 0 or 0.08 crashes in
second year– EB would provide an estimate that is a mixture of
these two values by considering safety of a specific segment and safety of a typically similar road
EB Analysis Formula• General form:
• Where:
CrashMeanˆCrashMean
CrashFrequencyˆWeight (CrashPrediction) (1 Weight) CrashFrequency
afterEB
after
before
beforeEB
1(1 )
Weight Weight factor of theEB
overdispersion parameter crash prediction number
Crash Prediction estimated crash number based on the HSM crash prediction models (control data)
Preliminary Study Results• Interpretation factors• All crashes– For entire sample– For each state
• Single and multiple vehicle crashes– For entire sample– For each state
• These results are preliminary– Research in final stages of completion
Interpreting Results• Using the EB method, there is no
statistically significant change in crashes if the confidence interval for EB contains 1
Preliminary Results for All Crash Types
28 60 33 121No. of SitesSample Size
NC OH WA ALL0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5higher boundѲlower bound
NC OH WA ALL0.0
0.5
1.0
1.5
2.0
2.5
higher bound Ѳ
lower bound
Preliminary Results for Single Vehicle Crashes
23 48 25 96
No. of SitesSample Size
Preliminary Results for Multiple Vehicle Crashes
27 56 33 116
No. of SitesSample Size
NC OH WA ALL0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
higher bound
Ѳ
lower bound
Preliminary Conclusions
• For the entire sample:– We did not find a statistically significant
impact between the installation of digital signs and an increase in crashes within 0.1 mile of the signs’ locations
• For multiple-vehicle crashes– We found the same result
• For single-vehicle crashes– We found the same result except for California,
which is likely due to the smaller sample size and the resulting decrease in statistical certainty (increased probability of error)