Transcript Slide 1

Use and Abuse of Crash Data in
Roadway Access Management
A Workshop at the National Access
Management Conference
Baltimore, Maryland July 13, 2008
Instructor Team
• Instructor team introductions
– David Plazak
– Zach Hans
– James Sun
– Eric Fitzsimmons
2
Workshop Overview
• The topic of this workshop was suggested by attendees
at the Park City, Utah National Access Management
Conference in 2006
• Persons who suggested the topic indicated that they
would like to be able to use available crash data to
evaluate access management plans and to market
access management as a way to improve safety
• This workshop has been designed to be very “handson”—participants should be able to immediately apply
many of the concepts they learn
3
Participant Introductions
• Brief participant introductions
– Your name
– Your involvement with access management
• We’d like to try to get a good mixture of level
of experience at the tables for the interactive
exercises
4
Parts of Today’s Workshop
• Workshop schedule:
– Part one:
• Time: 1:00 PM to 2:20 PM
• Workshop introduction and objectives
• Key access management safety concepts, including crash reduction
factors
• A hands-on problem: fix an “access management mess” where all
tables have complete data to work with
– Part two:
•
•
•
•
Time: 2:35 PM to 4:30 PM
Data quality considerations
Some data problems that might be encountered
Hands-on problem #2: Fix an “access management mess” where
tables have different levels of data completeness and quality to work
with
• Workshop wrap-up
5
Workshop Objectives
• Provide the participants with a good working
knowledge of:
– Crash types associated with lack of sound access
management
– Typical crash reduction factors associated with
access management treatments
– Crash data and potential weaknesses of crash data
6
Your Interest in Attending this Workshop?
• Let’s go around the room quickly and compile
a list of things you’d most like to learn during
this workshop
• Each table will provide one new idea, then
we’ll move on to the next table until we run
out of ideas
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Data-Driven Access Management
• Access management treatments and plans should be directly tied
to measurable objectives such as crash rate or crash cost reduction
• Access management treatments proposed should be appropriate
given the types of crashes and pattern of crashes being experienced
in a corridor
• Access management treatment costs need to be justifiable based
upon the expected benefits of crash reductions and other
objectives
• Stakeholders and decision-makers must be convinced that the “gain”
of access management is worth the “pain”
• Confidence in both past (“before treatment”) and expected future
crash rates (“after treatment”) should be high
• You want to be very sure that any treatments
will produce a noticeable and positive result
8
Access Management and Safety
• Most access-management related
crashes occur on urban and
suburban arterial roadways at
speeds of 35 to 55 miles per hour
• Up to half of all crashes in urban
areas are related to issues of
access (minor public road
intersections, traffic signal
spacing, driveways)
• Although most access-related
crashes occur in urban or
suburban areas, access-related
crashes in rural areas tend to be
severe crashes due to higher
travel speeds
• Access-related crashes occur at
conflict points
• The diagram represents one crash
data point
9
10
Conflict Points
Diverging
Merging
Crossing
11
Examples of Conflict Point
Reduction Treatments
12
Driveway Crash Pattern
• Left turn movements
generate ¾ of all
crashes at driveways
• Left turn entering
movements generate
almost ½ of all crashes
at driveways
Source: Federal Highway Administration
13
Common Types of Access
Management
• Traffic signal spacing
• Marginal access management
– Management of access features at (and beyond) the
roadway right of way line
• Examples: controlling driveway location and minimum spacing
between driveways
• Medial access management
– Management of access features in the center of the
roadway
• Examples: median types and median openings
• Separation of turning traffic from through
traffic streams
• Example: dedicated left-turn lanes or bays
14
Effectiveness of Traffic Signal
Spacing
• Reducing signals from 4 per mile (1/4 mile
spacing) to 2 per mile (1/2 mile spacing) will
reduce the total crash rate by up to 50%*
– (This will also have a significant impact on quality
of traffic flow during peak hours)
• Uniform traffic signal spacing is safer and
more efficient than non-uniform spacing
*The source for all crash reduction factors is the National
Highway Institute course “Access Management: Location
And Design”
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Effectiveness of Marginal Access Management
Treatments
• Applying total access control (e.g. allowing no
direct driveway accesses)
– Reduces crash rates by 40-45% on median divided
roadways
• Managing access density (driveways per mile)
– Reducing access density by 20-25% can reduce
total crash rates by 25-40%
16
Effectiveness of Medial Access
Control Treatments
• Adding a two-way left turn lane to an
undivided 4 lane roadway will reduce crash
rates by 20-25%
– Pedestrian crash rates will not change
• Adding a non-traversable (raised) median to
an undivided 4 lane roadway will reduce crash
rates by 40-45%
– Pedestrian crash rates will
decrease by 50%
17
Effectiveness of
Left Turn Lanes or Bays
• Adding a left-turn bay at a busy un-signalized
urban or suburban intersection may reduce
crash rates by up to 70%
• Adding a left turn bay at a busy signalized
urban or suburban intersection or at a busy
rural un-signalized intersection may reduce
crash rates by 40-50%
18
Problem 1: Fix This Mess
South Ankeny Blvd., Ankeny, Iowa
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What Do Crash Data Really Look Like?
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Crash Rate Calculation
CrashRate

C
Y M
C = Number of crashes over Y years
Y = Number of years being evaluated
Links
Nodes (Intersections)
M = Hundred million vehicle miles/year (MHVM) for links
SectionLen gth ( mi )  AADT 
 AADT  365 days

365 days
year
M 
M = Million entering vehicles/year (MEV)
M 
2
year
1, 000 , 000
100 , 000 , 000
AADT = Average annual daily traffic
21
What’s On Your Table …
Traffic over time
Crash data tables and charts
Corridor photos
Land Use
22
Laminated base map
Crash data stack map
22
An Example Plan …
23
Brief Table Reports … What Treatments
Do You Recommend?
24
Break
25
Part Two
• Data quality considerations
• Some data problems that might be
encountered
• Hands-on problem #2: Fix an “access
management mess” where tables have
different levels of data completeness and
quality to work with
• Workshop wrap-up
26
Think About It …
(to discuss later, if we have time)
1. How do you use crash data?
2. What is important to you about crash data?
3. What are some of the concerns/problems
you experience in using crash data?
27
Crash Data Allow Better …
• Problem Identification
• Understanding of the problem
before jumping into exploring and
designing solutions
• Focus on severe crashes rather than
all (minor) crashes
However …
28
You Need Good Quality Data
The Ingredients Matter: Quality Control
29
The Characteristics of Data Quality (The
“Six-Pack”)
Accessibility
Timeliness
Integration
Data
Consistency
Accuracy
Uniformity
Completeness
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Crash Data Quality: Timeliness
• Sometimes crash data are not available for
months or even years
• Varying timeliness of different jurisdictions
can cause issues for comparative analysis
• Time itself is important – did something
change during the analysis period?
• Also – the time period is important … one year
of data are probably not enough!
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• Spatial Location
• Attributes, e.g.,
severity, crash
type, roadway
info
Considering
functional
area
Original
SOUTH ANKENY BOULEVARD
Crash Data Quality: Accuracy
1ST Road
v
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Crash Data Quality: Completeness
• Missing data can lead to a misleading
picture and erroneous conclusions
• Some crash records have “unknown”
or “other” fields
• Some crash records are missing
altogether
• Variations between jurisdictions
(county level, state level) can lead to
inaccuracies in comparative analysis
Collision Type
Num of
Crashes Percentage
Non-collision
17854
32.6%
Head-on
1006
1.8%
Rear-end
Angle, oncoming left
turn
12143
22.2%
3528
6.4%
Broadside
Sideswipe, same
direction
Sideswipe, opposite
direction
10192
18.6%
5035
9.2%
1145
2.1%
Unknown
3538
6.5%
Not Reported
374
0.7%
54815
100.0%
Total
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Crash Data Quality:
Consistency/Uniformity
• Across
jurisdictions
• Across time
• Consistent
severities
34
Crash Data Quality: Integration
• Integration provides a ‘richer’, more
complete source of information (e.g.,
integration with roadway features)
• Double check on accuracy (including
severity)
35
Crash Data Quality: Accessibility
•
•
•
•
How can you get crash data?
How easy is it to get?
What form do you want it in?
Continuum:
not available … special request w/delay … regular updates … service … instant web access
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Typical Crash Data Issues
These may not be apparent to the data user
Changes in Crash Forms
• Content
– Addition/elimination of attributes collected
– Change in definitions (values)
Head-on
Sideswipe/Dual Right Turn
Broadside/Left Turn
Broadside/Right Angle
Rear End
Broadside/Right Entering
Rear-end
Rear End/Right Turn
Broadside/Left Entering
Angle, oncoming left turn
Rear End/Left Turn
Head-on/Left Entering
Broadside
Sideswipe/Opposite Direction
Sideswipe/Both Left Turning
Sideswipe, same direction
Sideswipe/Same Direction
Single
Sideswipe/Right Turn
Pedestrian
Sideswipe/Left Turn
Bicycle
Sideswipe/Dual Left Turn
Parked Vehicle
Before
Non-collision
Head-on
Sideswipe, opposite direction
Collision Type
After
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Changes in Crash Forms, cont.
Impacts:
• Difficult to perform direct comparisons over analysis
period.
• May result in systematic change in apparent crash
performance, e.g. crash reduction.
Crash Rate
Crash Rate
Change in crash form
Year
Statewide
Year
Site #1
40
Cartographic (Base Map) Changes
• Shift, update to reference road network
Impact: Challenging to systematically assign crash location.
41
Location Accuracy
• How are the crashes located?
– GPS (where?)
– Manually derived, based on literal description
– LRS, Link-node, other?
• What reference networks are used?
– GIS
– LRS
– Link-node
42
Location Accuracy, cont.
• How do accuracies vary among location
methods and reference networks?
– Ex. GPS ±5m v. GIS-based road network ±10m
Crash may be located
anywhere within this area.
X
X
Roadway may be presented
anywhere within this area.
GIS road network
Actual crash location
Geocoded crash location
Impact: type I or type II errors – you’d not know
43
Changes in Statute
• Reportable crash definition
– Property damage threshold, e.g. $500 v. $1000
– Injury crash
• Reporting requirements
– Driver report “…is not required when the accident
is investigated by a law enforcement agency.”
Impact: May result in systematic change in apparent
crash performance, e.g. crash reduction.
44
Reporting Extent & Completeness
•
•
•
•
All public roads
Private property
State-maintained roads only
Jurisdiction, agency dependent
Impacts:
• Incomplete crash history skews findings.
• Difficult to compare different locations.
45
Multiple Data Sources
• Local law enforcement
• State DOT
• Other agencies, e.g. taxi authority
Impact: Difficult to access and integrate all crash data,
i.e. difficult to create a comprehensive, useable data
set.
46
How Crash Data Are Abused
47
Limited Frame of Reference
• Limited, no comparison to similar locations.
• No comparison to “expected” conditions
(comparables).
Impact:
• What may appear to be a problem site, in isolation,
may be performing as well as, or better than, similar
locations.
– However, this does not imply that a location is performing
well and/or can not be improved.
48
Limited Perspective
• Decisions made, almost exclusively, based on crash history.
• Little consideration given to
changes during analysis
period…
–
–
–
–
Land use and development
Infrastructure
Traffic patterns
Other, e.g. construction
during an analysis year
Impact:
• Factors significantly impacting
crash history are ignored.
• Solution no longer fits the
problem
49
Regression to the Mean
• Crashes are random.
• Extreme conditions will generally return to
“normal” state.
Source: Safe Speed
Source: Safe Speed
Impact: Overestimates effectiveness of treatment; focus on
50
the wrong sites (should use EB or at least more data)
Analysis Period Shortcomings
• Limited (short) analysis period
• “Dated” crash data
Impacts:
• May not accurately represent the performance of a
site. Similar to regression to the mean.
• May not accurately reflect the existing conditions.
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General Crash Data Issues
• Change in crash form
• Cartographic (base
map) changes
• Location accuracy
• Change in statute
• Reporting extent &
completeness
• Multiple data
sources
Impact: Not being aware of these issues – is it your
responsibility?
52
Over-Emphasis on Single Crash Metric
• Frequency (total or by collision type)
• Rate
• Severity
Impacts: focus on “glass and paint” rather
than “human impact”; or “chasing fatals”
53
Problem 2: Fix This Mess
Lincoln Way, Ames, Iowa
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Data On Your Tables …
1. Complete set of data
2. 25 meter buffer vs. “Functional area”
3. Crash frequency only vs. AADT and crash
type
4. 1 year of data vs. 10 years of data
5. Older data vs. recent data
6. Current aerial photo only vs. past
development trend and detailed land use
data
55
Workshop Wrap-Up
• What are some of the new things you learned
today?
• Did the workshop meet your expectations?
• Do you think you will be able to apply what
you learned in your work once the conference
is over?
• David Plazak [email protected] (just a guess)
• Zach Hans [email protected]
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