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Crash Vehicle Person Roadway
MMUCCModel Minimum Uniform Crash Criteria
Impact of threshold adjustments
Sketch and narrative
http://www.nhtsa-tsis.net/crashforms/
Storage/retrieval
• <500 annually may be filed (paper) with summary tables
• Increasingly, all data are input into a database (and forms scanned)
• Feeds state and national databases
Old Location Process
Data Collection Technologies
• TraCS: Traffic and Criminal Software
TraCS data entry form
Incident Location Tool (and IMAT)
Easy Street Draw & Visio
Florida TraCS show
Data limitations Ogden)• Systematic reporting bias
– Database not truly reflective of crash situation
• Random bias– Under-reporting can result in distorted picture of road
crash situation• Numerically• Nature of the crashes• Not recording particular factor, means it was not present• Factor was present, but police officer did not think that it is not
important
Data limitations (continued)• Coding errors• Location errors• Discontinuities
– Data from one time period can not be compared to another time period
• Delays– Takes too long to have data available for analysis, so
countermeasures development is responding to historical crashes which may be out of date
Data limitations (continued)• Hidden problems
– It is assumed that database is good indicator of road safety problems
– There might some masked problems• Pedestrians avoid using an area because perceived safety problem
– This kind of problems need to be tackled through a road safety audit or identified through community consultation
Case study – access management
From …
Use and Abuse of Crash Data in Roadway Access Management
A Workshop at the National Access Management Conference
Baltimore, Maryland July 13, 2008
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
23
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
24
Problem 1: Fix This Mess South Ankeny Blvd., Ankeny, Iowa
25
What Do Crash Data Really Look Like?
26
What’s On Your Table …
27
Land Use
Crash data tables and charts
Crash data stack mapLaminated base map
Traffic over time
Corridor photos
27
An Example Plan …
28
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 …
29
You Need Good Quality Data
The Ingredients Matter: Quality Control30
The Characteristics of Data Quality (The “Six-Pack”)
31
FMCSA Data Quality
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!
33
Crash Data Quality: Accuracy
• Spatial Location• Attributes, e.g.,
severity, crash type, roadway info
34SO
UTH
AN
KEN
Y BO
ULE
VARD
SOU
TH A
NKE
NY
BOU
LEVA
RD
11STST Road Roadv
OriginalOriginal
Considering Considering functional functional areaarea
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
35
Collision TypeNum of Crashes Percentage
Non-collision 17854 32.6%
Head-on 1006 1.8%
Rear-end 12143 22.2%Angle, oncoming left turn 3528 6.4%
Broadside 10192 18.6%Sideswipe, same direction 5035 9.2%Sideswipe, opposite direction 1145 2.1%
Unknown 3538 6.5%
Not Reported 374 0.7%
Total 54815 100.0%
Crash Data Quality: Consistency/Uniformity
• Across jurisdictions
• Across time• Consistent
severities
36
Consistency
• Although the need for data is universally recognized, there is little consistency in collected data (Ogden)– Comparative study of eleven European countries found
that• Only two variables (date & hour) were collected in all eleven
countries• 7 percent of items were recorded in three countries• 70 percent recorded in only one country
– There is no nationwide crash data reporting system in US• Little consistency within states for recorded data elements
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)• Privacy is a tough issue• Another tough issue is multiple offices and even
agencies being in charge of various parts of safety data
38
Crash Data Quality: Accessibility
• How can you get crash data?• How easy is it to get?• What form do you want it in?• Liability and perception is an issue.• Continuum:
not available … special request w/delay … regular updates … service … instant web access
39
Typical Crash Data IssuesThese may not be apparent to the data user
Changes in Crash Forms• Content
– Addition/elimination of attributes collected– Change in definitions (values)
Non-collision
Head-on
Rear-end
Angle, oncoming left turn
Broadside
Sideswipe, same direction
Sideswipe, opposite direction
Head-on
Broadside/Left Turn
Rear End
Rear End/Right Turn
Rear End/Left Turn
Sideswipe/Opposite Direction
Sideswipe/Same Direction
Sideswipe/Right Turn
Sideswipe/Left Turn
Sideswipe/Dual Left Turn
Sideswipe/Dual Right Turn
Broadside/Right Angle
Broadside/Right Entering
Broadside/Left Entering
Head-on/Left Entering
Sideswipe/Both Left Turning
Single
Pedestrian
Bicycle
Parked Vehicle
Before After
Collision Type
41
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.
Year
Cras
h Ra
te
StatewideYear
Cras
h Ra
teSite #1
Change in crash form
42
Cartographic (Base Map) Changes
• Shift, update to reference road network
Impact: Challenging to systematically assign crash location.
43
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
44
Location Accuracy, cont.
• How do accuracies vary among location methods and reference networks?– Ex. GPS ±5m v. GIS-based road network ±10m
Impact: type I or type II errors – you’d not know
X
Actual crash location
Crash may be locatedanywhere within this area.
Roadway may be presentedanywhere within this area.
X
Geocoded crash location
GIS road network
45
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.
46
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.
47
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.
48
How Crash Data Are Abused
49
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.
50
Limited Perspective• Decisions made, almost exclusively, based on crash history.• Little consideration given to
changes during analysisperiod…– 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
problem51
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 the wrong sites (should use EB or at least more data) 52
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.
53
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?
54
Problem 2: Fix This Mess Lincoln Way, Ames, Iowa
55
Data On Your Tables …
1. Complete set of data2. 25 meter buffer vs. “Functional area”3. Crash frequency only vs. AADT and crash
type4. 1 year of data vs. 10 years of data5. Older data vs. recent data6. Current aerial photo only vs. past
development trend and detailed land use data
56
Locational Challenges for Next Generation of Crash Data
Systems
SAFETEA-LU Section 1401 (Highway Safety Improvement Program)
ID of top 5% of public hazardous locations on all roads
Local Road GIS Data
Where some states are now
Inventory data on all roads?
The “quadrennial needs” legacy
YesSome, quality issue, or working on itNoNo Response
State system as a percent of all public roads
Can 1401 be met without GIS?
Kansas, for example …• Has crashes on system only • Has ≈ 70% of crashes located to
road by route milepost• Does sliding spot (nongraphical)
& “named intersection” (program)
• Assuming the 30% missing does not affect the outcome
• No brainer to do top 5%
Mason City
Waterloo
Cedar Rapids
Quad CitiesDes Moines
Council Bluffs
IowaCity
Ames
SiouxCity
DubuqueFort Dodge
Ottumwa
Marshalltown
Spencer
Clinton
1 yrof data
Crash Density – 1 Year AverageAnnual Fatal and Major Injury Crashes Per Mile
Sample
- DRAFT
Sample
- DRAFT
Mason City
Waterloo
Cedar Rapids
Quad CitiesDes Moines
Council Bluffs
IowaCity
Ames
SiouxCity
DubuqueFort Dodge
Ottumwa
Marshalltown
Spencer
Clinton
3 yrsof data
Crash Density – 3 Year AverageAnnual Fatal and Major Injury Crashes Per Mile
Sample
- DRAFT
Sample
- DRAFT
Mason City
Waterloo
Cedar Rapids
Quad CitiesDes Moines
Council Bluffs
IowaCity
Ames
SiouxCity
DubuqueFort Dodge
Ottumwa
Marshalltown
Spencer
Clinton
5 yrsof data
Crash Density – 5 Year AverageAnnual Fatal and Major Injury Crashes Per Mile
Sample
- DRAFT
Sample
- DRAFT
Mason City
Waterloo
Cedar Rapids
Quad CitiesDes Moines
Council Bluffs
IowaCity
Ames
SiouxCity
DubuqueFort Dodge
Ottumwa
Marshalltown
Spencer
Clinton
10 yrsof data
Crash Density – 10 Year AverageAnnual Fatal and Major Injury Crashes Per Mile
Sample
- DRAFT
Sample
- DRAFT
Location methods• address• offset from known point
(intersection, bridge, crossing, milepost)
• Literal description• Smart map• Lat/long or other coordinates (GPS)• Aerial photo
Multiple methods required
Spatial analysis methods
• Spot/Intersection Analysis • Strip Analysis • Cluster Analysis • Sliding-Scale Analysis • Corridor Analysis
Spatial statistics is an emerging area
But …some technical issues
Some not-so “simple” questions
Feature not represented
Feature under
construction
Alignment OK
Alignment Off
Where are the roads? (Incorrect or incomplete cartography)
Where are the roads? (Improving cartography)
Alignment moves
Alignment stays put
Where are the crashes?• Crashes are not
necessarily point events• Some crashes may be
located using different methods and degree of accuracy – Temporal (e.g. link node
to lat long)– Spatial (e.g., state police
v. local)– Techno (GPS v. smart
map)
?
What’s “the” traffic volume on “the” road?
• Need traffic level for the year the crash happened
• Requires multiple files – in Iowa, working on going back past 1998 – difficult to do
• Was the road even there then? Is the road still there?
How to segment the road system?
• Requirements– Logical breaks (engineering and
public)– Relationship to inventory data– Long enough for manageability
and presentation– Short enough to reflect
important changes– Clear and understandable to use
• Facility location and type– What is rural/urban? Character is
important …Designated
“rural”
Can use attributes and/or proximity…Red: probable, Yellow: spatial @ 75’, Blue: possible + spatial
What is an intersection crash?
Where to go from here …