TxDOT Research Project 0-6912
Innovative Tools and Techniques in Identifying Highway Safety Improvement
Projects
Ioannis (Yianni) Tsapakis & Bahar Dadashova
October 11, 2017
1
Project TeamTxDOT
• Darrin Jensen (PM)
• Darren McDaniel - TRF
• Jacob Chau - Waco
• America Garza - Corpus Christi
• Brad Tiemann - Tyler
• Jeffery Vinklarek - Yoakum
• Rebecca Wells - Atlanta
• Kelli Williams - Odessa
Non-TxDOT
Ismael Soto
TTI
• Ioannis Tsapakis (PI)
• Karen Dixon (Co-PI)
• Bahar Dadashova
• Srinivas Geedipally
• William Holik
• Jerry Le
• Jing Li
• Swapnil Samant
• Sushant Sharma
20-6912 - Innovative Tools and Techniques in
Identifying Highway Safety Improvement Projects
Topics
• Research Questions
• General Framework
– Network Screening for Segments
– Network Screening for Intersections
– Crach Analysis and ViSualization (CAVS) Products
– Project Prioritization Tool
• Recommendations
30-6912 - Innovative Tools and Techniques in
Identifying Highway Safety Improvement Projects
Research Questions
How can TxDOT:
• Allocate HSIP funds in the most cost-effective manner?
• Create a level playing field for all districts participating in the HSIP?
• Improve and streamline existing HSIP processes?
40-6912 - Innovative Tools and Techniques in
Identifying Highway Safety Improvement Projects
General Framework
5
Network Screening
Diagnosis
Countermeasure Selection
Economic Appraisal
Project Prioritization
Evaluation
0-6912 - Innovative Tools and Techniques in Identifying Highway Safety Improvement Projects
6
Network Screening Diagnosis Select Countermeasures Economic Appraisal Prioritize Projects Safety Effectiveness Evaluation
Establish Focus
· Crashes occurred on on-system mainlanes
· Reduce number and severity of fatal and incapacitating injury crashes
Identify Network and Establish Reference Populations
· On-system main lane segments
· Group roadway segments by HPMS functional class
Screen and Evaluate Results
· Calculate performance measure(s) for each site
· Create table and map that show the results of network screening
· Rank sites based on performance measure(s)
Select Performance Measures
· Given the data that are currently available at TxDOT, consider the following
performance measures:
o Average crash frequency
o Crash rate
o Critical rate
o Excess predicted average crash frequency using method of moments
o Excess expected average crash frequency using SPFs
o Probability of specific crash types exceeding threshold proportion
o Excess proportions of specific crash types
Select Screening Method
· Sliding window method (preferred)
· Simple ranking method (simple, but not as reliable as sliding window
method)
Review CAVS Data
· Review crash locations using GIS tools
· Descriptive statistics of crash conditions (e.g., counts of crashes by type, severity, roadway or environmental conditions)
Assess Supporting Documentation
· Obtain and review documented information that provides addition
perspective to the CAVS data review. This information may include:
o Current traffic volumes for all travel modes
o As-built construction plans
o Relevant design criteria and pertinent guidelines
o Inventory of field conditions
o Relevant photo or video logs
o Maintenance logs
o Recent traffic operations or transportation studies
o Land use mapping and traffic access control characteristics
o Historic patterns of adverse weather
o Known land use plans for the area
o Records of public comments on transportation issues
o Roadway improvement plans in the site vicinity
o Anecdotal information about travel through the site
Assess Field Conditions
· Validate safety concerns identified from the review of crash data and
relevant documentation
· Travel through the site from all possible directions and modes
· Consider the following factors:
o Roadway and roadside characteristics
o Traffic conditions
o Traveler behavior
o Roadway consistency
o Land uses
o Weather conditions
o Evidence of problems (e.g., broken glass, skid marks, damaged signs)
Identify Safety Concerns
· Compile information to identify any specific crash patterns that could be
addressed by one or multiple countermeasures
Identify Contributing Factors
· Consider human, vehicle, and roadway contributing factors before, during, and after the crash. Possible contributing factors associated with different manners of collision and types of crashes on roadway segments include, but are not limited to:
o Vehicle rollover
- roadside design
- inadequate shoulder width
- excessive speed
- pavement design
o Fixed object
- obstruction in or near roadway
- inadequate lighting
- inadequate pavement markings
- inadequate signs, delineators, guardrail
o Nighttime
- poor visibility or lighting
- poor sign visibility
- inadequate channelization or delineation
- excessive speed
o Wet pavement
- pavement design
- inadequate pavement markings
- inadequate maintenance
o Opposite-direction sideswipe or head-on
- inadequate roadway geometry
- inadequate shoulders
- excessive speed
o Run-off-the-road
- inadequate lane width
- slippery pavement
- inadequate median width
o Bridges
- alignment
- narrow roadway
- visibility
Select Potential Countermeasures
· Review CAVS data and identify possible contributing factors
· Identify countermeasures that may address the contributing factors
· Conduct cost-benefit analysis, if possible, to select preferred treatments
Quantify Crash Reduction
· Calculate monetary benefits:
o Estimate change in crashes by severity
o Convert change in crash frequency to annual monetary value
o Convert monetary value to a present value
· Calculate costs:
o Calculate construction and other implementation costs
o Convert costs to present value
· Economic evaluation methods for individual sites:
o Net present value method
o Benefit-cost ratio (BCR) method
o Cost-effectiveness analysis (effectiveness is measured by the difference between predicted crash frequency and observed crash frequency)
Identify Economically Justified Countermeasures
· Identify one or more candidate countermeasures for possible implementation at each site (the countermeasures must be economically justified based on economic appraisal results)
· Return to the step of Select Countermeasures if considered countermeasures are not economically justified
Evaluation Types
· Safety effectiveness evaluation may include:
o Evaluating a single project at a specific site to determine the effectiveness of the project
o Evaluating a group of similar projects to determine the effectiveness of those projects
o Evaluating a group of similar projects to quantify a CMF for a countermeasure
o Assessing the overall effectiveness of specific types of projects or countermeasures in comparison to their costs
Evaluate Non-Monetary Factors
· Non-monetary considerations include:
o Public demand
o Public perception and acceptance of safety improvement projects
o Meeting established and community-endorsed policies to improve mobility or accessibility along a corridor
o Air quality, noise, and other environmental considerations
o Road user needs
o Providing a context sensitive solution that is consistent with a community’s vision and environment
Prioritize Projects
· Prioritization methods include:
o Incremental benefit-cost analysis ranking
o Ranking by economic effectiveness measures
o Optimization methods (including basic optimization methods and multi-objective resource allocation method)
Safety Evaluation Methods
· Evaluation methods include:
o Observational before/after evaluation studies
o Observational before/after evaluation studies using SPFs – the Empirical Bayes Method
o Observational before/after evaluation study using the comparison-group method
o Observational before/after evaluation studies to evaluate shifts in collision crash type proportions
o Observational cross-sectional studies
o Experimental before/after evaluation studies
Esta
blish F
ocu
s
Network Screening for On-System Main-Lane Segments
Iden
tify
Netw
ork
and
Esta
blish R
efe
ren
ce P
op
ula
tion
s
Import TxDOT Road-Highway
Inventory Network (RHiNo) data into
ArcGIS
Filter for on-system main lane segments and create a feature
class
The RHiNo attribute “REC” is used to differentiate various segment types.
Add a lane-width attribute to the feature
class
Calculate the lane width
Lane width is calculated as SUR_W (surface width) divided by NUM_LANES (number of lanes).
Dissolve main lane segments based on selected attributes
The attributes include district, county, highway name, functional class, AADT, number of lanes, lane width, shoulder width and use (both inside and outside), and median width.
Project dissolved feature class
Projection coordinate system: NAD_1983_2011_Texas_Centric_Mapping_System_Lambert
Import three years of CRIS crash data (Excel format) into
ArcGIS
Select target crashes:
· Fatal and incapacitating crashes
· On-system crashes
· Main/proper lane crashes
· Crashes with valid coordinates
· Crashes with valid highway name
· Crash_Severity = ‘FATAL’ or ‘INCAPACITATING INJURY’
· On_System_Flag = ‘Yes’
· Road_Part = ‘MAIN/PROPER LANE’
· Crash_Latitude <> 0 AND Crash_Longitude <> 0
· HWY <> Null
Delete fields not needed
Export displayed crashes as feature
class
Display selected crashes on ArcMap
Geographic coordinate system:GCS_WGS_1984
Project crash feature class
Projection coordinate system: NAD_1983_2011_Texas_Centric_Mapping_System_Lambert
Use the functional classification from the TxDOT Roadway Safety Design Workbook to reclassify RHiNo segments in order to apply SPFs
Merge adjacent segments with similar characteristics
for each group
Find adjacent segments for each segment
Identify ‘similar’ adjacent segments
Update attribute values for identified ‘similar’ adjacent segments
Merge ‘similar’ adjacent segments
Find two nearest segments for each
crash
Both segments’ highway names do not match with
the highway name of the crash
Select the segment that is closer to the
crash
Only one segment’shighway name matches with
the highway name of the crash
Both segments’highway namesmatch with thehighway name
of the crash
Project crash to corresponding
segment
Extract DFO for projected crash from
RHiNo
Use ArcGIS tool ‘Generate Near Table’
Use the ArcGIS tool ‘Locate Features Along Routes’
Sele
ct P
erfo
rman
ce M
easu
res
Review availability of other data and
functions at TxDOT
For example, SPFs calibrated for Texas roads
Use multiple performance measures to improve the level of confidence in the results. Performance measures that currently can be used at TxDOT are:
· Average crash frequency
· Crash rate
· Critical rate
· Excess predicted average crash frequency using method of moments
· Excess expected average crash frequency using SPFs
· Probability of specific crash types exceeding threshold proportion
· Excess proportions of specific crash types
Apply available data and functions
App
ly S
cre
enin
g M
eth
od
Generate a feature class of points along each segment at 0.1
mile interval
Assign number to each generated point
Numbering starts at 1 for each segment. Both start and end points are numbered.
Assign window group number(s) to each
generated point
· Window size is 0.3 miles
· Window moves at 0.1 mile increment
· For segments <= 0.3 miles, only end points are labeled as Window Group 1
· For segments > 0.3 but <= 0.6 miles, multiple points are labeled as Window Group 1, or Window Group 2 depending on the location of point
· For segments > 0.6 miles, multiple points are labeled as Window Group 1, or Window Group 2, or Window Group 3 depending on the location of point
Split the point feature class into three feature
classes by window group
Split segments at points from each
window group respectively
Apply the ArcGIS tool ‘Generate Points Along Lines’
Assign window ID to newly created windows (sub-
segments)
Window ID = Segment ID + “_” + Window Group Number + “_” + FID
Apply the ArcGIS tool ‘Split Line at Point’
Scre
en
an
d E
val
uate
Result
s
Apply sliding window method to roadway segments of a specific functional class
Map crashes on segments
Calculate performance measures for each
window
Rank windows based on one or multiple
performance measures
· Sites that repeatedly appear at the higher end of the list could become the focus of more detailed site investigations
· Sites that repeatedly appear at the low end of the list could be ruled out for needing further investigation
· Differences in the rankings due to various performance measures will become most evident at sites that are ranked in the middle of the list
Criteria for determining ‘similar’ segments:
· Functional classification: two adjacent segments belong to the same roadway functional class
· Highway name: two adjacent segments have the same highway name
· Number of lanes: two adjacent segments have the same number of lanes
· ADT: the difference in ADT values between two adjacent segments is less than or equal to a certain percent, which varies by the magnitude of the ADT
· Median width: the difference between two adjacent segments is less than or equal to 0.5 ft.
· Inside shoulder width: the difference between two adjacent segments is less than or equal to 0.5 ft.
· Outside shoulder width: the difference between two adjacent segments is less than or equal to 0.5 ft.
· Lane width: the difference between two adjacent segments is less than or equal to 0.5 ft.
· Inside/outside shoulder use: both adjacent segments allow curb parking (either diagonal or parallel parking) on inside/outside shoulder or both do not allow shoulder parking
R2
R3
R4
R5
√
√
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√
R6
R7
√
√
U2
U3
U4
U5
√
√
√
√
U6
U7
√
√
Functionalclass
Highway name
±40%
±40%
±50%
±50%
-
-
±20%
±30%
±40%
±50%
±50%
-
Number of lanes
ADT
-
±0.5 ft.
Lane widthMedian width
Inside shoulder
width
Outside shoulder
width
R1
U1
Inside shoulder
use
√
√
√
√
√
√
√
√
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√
√
√
√ ±20%√
√ ±30% -√
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±0.5 ft.
±0.5 ft.
±0.5 ft.
±0.5 ft.
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±0.5 ft.
±0.5 ft.
±0.5 ft.
±0.5 ft.
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±0.5 ft.
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√
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-
Outside shoulder
use
-
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√
√
-
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√
√
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-
· For the attributes ‘functional class’, ‘highway name’, and ‘number of lanes’ retain the original values
· For the attributes ‘ADT’, ‘median width’, ‘inside shoulder width’, ‘outside shoulder width’, and ‘lane width’ update attribute values for both segments with a length-weighted average value
· For the attributes ‘inside shoulder use’ and ‘outside shoulder use’ indicate whether diagonal or parallel parking is available
Use the ArcGIS tool ‘Dissolve’ to merge segments
Create segment groups based on HPMS functional
classification
Dissolve segments in each group
based on selected attributes
Combine all groups of segments into one feature class
Sort segments based on functional classification and
highway name
Assign new ID to each segment
Disaggregate the feature class of all segments into separate feature classes
based on functional classification
Select performance measures
B
A
C
D E
F
G
Enter project data in Excel
Calculate benefits B(i) of each
project i
Calculate the difference in
benefits between two projects
Filter for projects with SII>1
Sort projects in ascending order by construction
cost
Calculate the difference in costs
between two projects
Compute incremental
benefit cost ratio (IBCR) between
two projects
Start with the first two projects in the list
All projects have been ranked and removed
from the initial list
IBCR>1
For example:
IBCR = (B(2)-B(1)) / (C(2)-C(1))
B(i) = SII(i) x C(i)
i = 1, 2, …, n
SII(i) = Safety improvement index of project i
C(i)= Construction cost of project i
IBCR<1
Same project costs
Consider the project selected in the last pairing as the best economic
investment
Remove the project selected in
the last pairing from the initial list
More projects in the initial list to
be compared
End of the list (all projects have been compared)
Repeat calculations for the remaining unranked projects in the initial list
Consider project with higher cost and
compare it with next project in the list
Consider project with lower cost and
compare it with next project in the list
Consider project with higher benefits and compare it with next
project in the list
Rank the project based on the order in which it was included with the other projects that were previously determined to be the best economic investment.
Include it with other projects
determined to be the best economic
investment
Extract crash data from CRIS for the last three years
Extract preventable crash criteria for each work code (WC)
Determine applicable WCs for each crash
location
Filter for specific crash types
Apply data quality control criteria
Filter for:
· Fatal (K) and incapacitating (A) injury crashes
· On-system crashes
· Mainlane crashes
Select crashes with valid:
· Highway name
· Geographical coordinates (latitude and longitude)
Source: TxDOT HSIP Work Code Manual
Import data table in ArcGIS
Map crashes on RHiNo using geographical coordinates
Import RHiNo in ArcGIS
Geographic coordinate system:GCS_WGS_1984
Create shapefile containing
mapped crashes
Filter crash data by single WC or combination of
WCs
Crate feature class for each
selected WC or combination of
WCs
Create unique symbol for each
feature class
Convert each feature class into
a kml layer
More kml layers need to be created.
All kml layers have been created.
KML layers
CRIS Database
Network Screening Crash Analysis and ViSualization (CAVS)
Project Prioritization
Level 2 Diagram
0-6912 - Innovative Tools and Techniques in Identifying Highway Safety Improvement Projects
Network Screening for Segments
7
A
B
AB = 1.0 mile
0.2 miles
0.1 miles
0-6912 - Innovative Tools and Techniques in Identifying Highway Safety Improvement Projects
Performance Measures
8
HSM Performance Measure
1. Crash Frequency
2. Crash Rate
3. Equivalent Property Damage Only (EPDO) Average Crash Frequency
4. Relative Severity Index (RSI)
5. Critical Rate
6. Excess Predicted Average Crash Frequency Using Method of Moments
7. Level of Service of Safety (LOSS)
8. Excess Predicted Average Crash Frequency Using Safety Performance Functions (SPFs)
9. Probability of Specific Crash Types Exceeding Threshold Proportion
10. Excess Proportion of Specific Crash Types
11. Expected Average Crash Frequency with Empirical Bayes (EB) Adjustment
12. EPDO Average Crash Frequency with EB Adjustment
13. Excess Expected Average Crash Frequency with EB Adjustment
0-6912 - Innovative Tools and Techniques in Identifying Highway Safety Improvement Projects
Network Screening ArcMap Toolbox
90-6912 - Innovative Tools and Techniques in
Identifying Highway Safety Improvement Projects
Network Screening ArcMap Toolbox
100-6912 - Innovative Tools and Techniques in
Identifying Highway Safety Improvement Projects
Network Screening ArcMap Toolbox
110-6912 - Innovative Tools and Techniques in
Identifying Highway Safety Improvement Projects
Network Screening Results
120-6912 - Innovative Tools and Techniques in
Identifying Highway Safety Improvement Projects
Provide to all Districts in 2018 &
2019 HSIP
Intersection Data Collection
0-6912 - Innovative Tools and Techniques in Identifying Highway Safety Improvement Projects
13
Northern San Antonio
Intersection Data
140-6912 - Innovative Tools and Techniques in
Identifying Highway Safety Improvement Projects
Intersection Data
CRIS
2013-2015
Crash Numbers
Crash Severity
Crash Location
RHiNo
2013-2015
ADT
Lane Width
Shoulder Width
Manually Collected
Traffic Control & # Legs
# Through, Left and Right Turn Lanes
Right-turn Channelization
150-6912 - Innovative Tools and Techniques in
Identifying Highway Safety Improvement Projects
Evaluation: KABC Crashes
*Bold numbers are the intersection IDs
160-6912 - Innovative Tools and Techniques in
Identifying Highway Safety Improvement Projects
Summary: Performance MeasuresPerformance Measure Accounts
for RTM
Bias
Accounts for
Traffic
Volume
Accounts
for Data
Variance
Uses
Roadway
Design
Elements
Establishes
Threshold for
Similar Sites
Applicability Requires
Reference
Population based
Computation
Developed for Specific
Crash Type
Crash Frequency No No No No No Simple No No but can be applied
Crash Rate No Yes, but can
be biased
towards low
traffic
volume
No No No Simple No No but can be applied
Critical Rate No Yes Yes No Yes Moderate Yes No but can be applied
Excess PACF Using
Method of Moments
No Yes Yes No Uses average
crash frequency
per reference
population
Difficult Yes No but can be applied
Excess PACF Using
Safety Performance
Functions (SPFs)
No Yes No Yes Uses predicted
average crash
frequency
Difficult (if
the variables
used for
CMFs are
missing)
No but requires
CMFs which are
RP based
Yes
Probability of Specific
Crash Types Exceeding
Threshold Proportion
Not
Affected
No Yes No Yes Difficult Yes Yes, but could be biased
towards the sites with
unusually high crash
frequency
Network Screening Results
0-6912 - Innovative Tools and Techniques in Identifying Highway Safety Improvement Projects
17
Very high risk (Adjusted Weighted Ranking <= 10%)
Very low risk (Adjusted Weighted Ranking >= 90%)
¯
Loop 410
CAVS
180-6912 - Innovative Tools and Techniques in
Identifying Highway Safety Improvement Projects
CAVS
19Implementation Project 5-6912
CAVS
20Implementation Project 5-6912
Master Plans
21Implementation Project 5-6912
Project Prioritization – IBCR
220-6912 - Innovative Tools and Techniques in
Identifying Highway Safety Improvement Projects
Benefits
23
• # projects submitted to HSIP by 57%
• Time to identify projects by 15-50%
• Project prioritization method complements SII
0-6912 - Innovative Tools and Techniques in Identifying Highway Safety Improvement Projects
Recommendations
24
• Implement network screening & CAVS (5-6912)
• Evaluate projects and countermeasures (0-6961)
• Develop intersection inventory
• Develop new SPFs
0-6912 - Innovative Tools and Techniques in Identifying Highway Safety Improvement Projects
Thank You!!!
250-6912 - Innovative Tools and Techniques in
Identifying Highway Safety Improvement Projects
Ioannis Tsapakis
(210) 321-1217
Karen Dixon
(979) 845-9906