Kentucky Transportation Cabinet

Kentucky’s Network 

Screening Process 

SAFETY DATA CASE STUDY 

FHWA-SA-21-015 

Federal Highway Administration Office of Safety 

Roadway Safety Data Program 

http://safety.fhwa.dot.gov/rsdp
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SAFETY DATA CASE STUDY FHWA-SA-21-015

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SAFETY DATA CASE STUDY FHWA-SA-21-015

1. Report No.

FHWA-SA-21-015

2. Government Accession 

No.

3. Recipient's Catalog No.

 

4. Title and Subtitle

Kentucky’s Network Screening Process

5. Report Date

February 2021 

6. Performing Organization Code

7.Author(s) 

Ian Hamilton and Dylan Coley

8. Performing Organization Report No.

9. Performing Organization Name and Address

Vanasse Hangen Brustlin, Inc (VHB)

940 Main Campus Drive 

Raleigh, NC 27606 

10. Work Unit No.

11. Contract or Grant No.

DTFH61-16-D-00052

12. Sponsoring Agency Name and Address

Federal Highway Administration Office of Safety 

1200 New Jersey Ave., SE

Washington, DC 20590

13. Type of Report and Period

Case Study, January 2020-January 

2022

14. Sponsoring Agency Code

FHWA

15. Supplementary Notes

The contract manager for this report was Jerry Roche. Funding for this effort provided in part by the Highway Safety 

Manual Implementation Pooled Fund, TPF-5(255).

16. Abstract

This purpose of this case study is to describe Kentucky’s network screening methodology for all State-owned roads, as well as 

local roads classified as a collector street or above. The Kentucky Transportation Cabinet’s (KYTC) Highway Safety 

Improvement Program requires a data-driven process to identify sites with a potential safety need and prioritize projects. The 

KYTC partnered with the University of Kentucky’s Kentucky Transportation Center (KTC) to develop a network screening 

approach to prioritize locations statewide to be targeted for future safety improvement projects. This network screening approach 

addresses five focus areas: 1) Roadway Departure Corridors, 2) Cable Barrier, 3) High Friction Surface Treatment (HFST) 

Segments, 4) HFST Ramps, and 5) Intersections. The KTC analyzed statewide enterprise road, traffic, and crash data to develop

safety performance functions (SPFs) that predict crashes on all facilities encompassed by each focus area. The KTC used 

cumulative residual (CURE) plots to assess SPF model performance and identify outliers or issues inherent in the dataset that

lead to worse model fit. The CURE plot approach also underscores the importance of thoughtful and homogenous site 

segmentation for improved performance and meaningful network screening results. This network screening methodology applies 

a State-specific approach to rank locations based on higher-than-expected crashes and associated crash costs.

17. Key Words: 

Highway Safety Manual, HSM, Network 

Screening

18. Distribution Statement

No restrictions.

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this 

page) Unclassified

21. No. of Pages

 15 

22. Price

Technical Documentation Page
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SAFETY DATA CASE STUDY FHWA-SA-21-015

Acronyms

Acronym Description

AADT annual average daily traffic

AASHTO American Association of State Highway and Transportation Officials

CURE cumulative residuals 

CMF crash modification factor

EB Empirical-Bayes 

EEC Excess Expected Crashes

FHWA Federal Highway Administration 

HFST High Friction Surface Treatment

HIS Highway Information System

HSIP Highway Safety Improvement Program 

HSM Highway Safety Manual

KTC Kentucky Transportation Center

KYTC Kentucky Transportation Cabinet

LOSS level of service of safety

RwD roadway departure

SPF Safety Performance Function

SPF-R Safety Performance Function – R 

TED Transportation Enterprise Database
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SAFETY DATA CASE STUDY FHWA-SA-21-015

Table of Contents

Introduction............................................................................................................................................... 1

Safety Performance Analysis ................................................................................................................... 1

Challenges.................................................................................................................................................. 6

Conclusions and Lessons Learned ........................................................................................................... 8

References .................................................................................................................................................. 9

List of Figures

Figure 1. Graphic. CURE plot – more heterogenous segmentation. ................................................... 4

Figure 2. Graphic. CURE plot – more homogenous segmentation...................................................... 4

Figure 3. Graphic. Diagram illustrating EEC relative to the SPF prediction. ..................................... 5

Figure 4. Graphic. CURE plot with a significant outlier around 2,000 AADT..................................... 7

Figure 5. Graphic. CURE plot with the significant outlier removed. ................................................... 7

List of Tables 

Table 1. KYTC network screening focus areas. ..................................................................................... 2
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SAFETY DATA CASE STUDY FHWA-SA-21-015

Executive Summary

This purpose of this case study is to describe Kentucky’s network screening methodology for all State-owned 

roads, as well as local roads classified as a collector street or above. The Kentucky Transportation Cabinet’s 

(KYTC) Highway Safety Improvement Program requires a data-driven process to identify sites with a potential 

safety need and prioritize projects. The KYTC partnered with the University of Kentucky’s Kentucky 

Transportation Center (KTC) to develop a network screening approach to prioritize locations statewide to be 

targeted for future safety improvement projects. This network screening approach addresses five focus areas: 

1) Roadway Departure Corridors, 2) Cable Barrier, 3) High Friction Surface Treatment (HFST) Segments, 

4) HFST Ramps, and 5) Intersections. The KTC analyzed statewide enterprise road, traffic, and crash data to 

develop safety performance functions (SPFs) that predict crashes on all facilities encompassed by each focus 

area. The KTC used cumulative residual (CURE) plots to assess SPF model performance and identify outliers or 

issues inherent in the dataset that lead to worse model fit. The CURE plot approach also underscores the 

importance of thoughtful and homogenous site segmentation for improved performance and meaningful network 

screening results. This network screening methodology applies a State-specific approach to rank locations based 

on higher-than-expected crashes and associated crash costs.
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SAFETY DATA CASE STUDY FHWA-SA-21-015

Introduction 

The Transportation Research Board’s Safety Performance Analysis (ACS20) User Liaison Subcommittee 

has an on-going initiative focused on practical application of the American Association of State Highway 

and Transportation Officials (AASHTO) Highway Safety Manual (HSM) (i.e., “using the HSM in the real 

world”). FHWA also administers the HSM Implementation Pooled Fund, which includes 22 States 

focused on projects to help further HSM implementation. Development of HSM case studies will assist 

practitioners in performing data-driven safety analysis using the advanced methods described in the 

HSM. The primary purpose of the HSM case studies is to highlight noteworthy applications of HSM 

methods, focus on common challenges, and feature agencies that overcame those challenges. These case 

studies serve as a source of lessons learned and noteworthy practices to help guide practitioners 

applying the HSM.

Background, Purpose, and Need

This purpose of this case study is to describe Kentucky’s network screening methodology for all Stateowned roads, as well as local roads classified as a collector street or above. The Kentucky 

Transportation Cabinet’s (KYTC’s) Highway Safety Improvement Program (HSIP) requires a data-driven 

process to identify sites with a potential safety need and prioritize projects. According to the First 

Edition of the HSM (2010): 

“Network screening is a process for reviewing a transportation network to identify and rank 

sites from most likely to least likely to realize a reduction in crash frequency with 

implementation of a countermeasure” (p. 4-1). 

The KYTC partnered with the University of Kentucky’s Kentucky Transportation Center (KTC) to 

develop a network screening approach to prioritize locations statewide to be targeted for future safety 

improvement projects. This methodology applies a State-specific approach to rank locations based on 

higher-than-expected crashes and associated crash costs.

Project Description

• Sponsoring agency(ies): Kentucky Transportation Cabinet’s Highway Safety Improvement 

Program Team. 

• Project location: All State-owned roads and any locally-owned roads functionally classified as 

a collector street or above.

• Area and Facility type(s): Rural two-lane, two-way roads; urban and rural freeways; ramps;

and intersections.

• Project status: Completed annually.

Safety Performance Analysis

This section provides an overview of Kentucky’s analysis methods, assumptions, and results. Chapter 4 

of the First Edition of the HSM contains general guidance on network screening techniques and 

considerations. 
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SAFETY DATA CASE STUDY FHWA-SA-21-015

Analysis Overview

Critical first steps to the network screening process include establishing a focus for the analysis, 

delineating the network to be screened, and identifying reference populations. Kentucky established five 

focus areas, each with their own specific networks and reference populations, for its network screening 

approach.

Table 1. KYTC network screening focus areas.

Focus Area Key Characteristics

Roadway Departure 

(RwD) Corridors

 Rural

 2-lane

 Posted speed limit of 50 miles per hour and above

 RwD crashes only

Cable Barrier

 Urban and rural

 Interstates and parkways with applicable space for 

installing cable barrier (i.e. no existing barrier and 

median suitable for cable)

 Median crossover crashes only

High Friction Surface 

Treatment (HFST) 

Segments 

 Urban and rural

 Wet pavement RwD crashes only

 8 roadway classes.

HFST Ramps

 All ramps

 Wet pavement RwD crashes only

Intersections 

 Urban and rural

 All crashes within intersection buffer area

 This is based on a pre-specified proximity to the 

crossing route, and it will vary if another adjacent 

intersection falls within the specified buffer distance

 36 intersection classes

The KYTC houses the State’s traffic (e.g., annual average daily traffic – AADT) and roadway data in the 

Highway Information System (HIS). The KYTC obtains crash data, maintained by the Kentucky State 

Police, and integrates it in the agency’s Transportation Enterprise Database (TED) which stores traffic 

and other performance monitoring data. The KTC receives annual extracts of both databases and stores 

these data in its own internal database, “BILL.” KTC analysts summarize the crash data according to the 

KABCO crash-severity scale. 
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SAFETY DATA CASE STUDY FHWA-SA-21-015

The KABCO scale refers to the most severe injury of a person involved with a crash:

 K: Fatal crash

 A: Suspected serious injury

 B: Suspected minor injury

 C: Possible injury

 O: No injury 

To create the appropriate network for each analysis, the KTC divides segments and intersections into 

their relevant groups or “populations” of similar locations. The KTC developed State-specific safety 

performance functions (SPFs) for each of the KYTC’s five focus areas (e.g., RwD corridors) to predict K

and A; B; C; and O severity crash frequency at relevant sites. Since these are State-specific SPFs, they do 

not require calibration to effectively apply to the Kentucky context.

Analysis Details

First, analysts developed SPFs based on the most recent available crash and AADT data. If AADT data 

were not available for a specific site, the KTC applied traffic estimates to facilitate the use of SPFs. These

locations were typically locally-owned roads, and the KTC used county-level estimates for these facilities

(i.e., local roads in a specific county all share the same value). To validate each SPF model, analysts use 

cumulative residual (CURE) plots and other metrics to evaluate and improve the SPF development 

process each year. The KTC created populations of homogenous roadway segments and intersections 

based on the appropriate focus area, and the analysis emphasized the importance of homogenous 

segmentation of the roadway inventory. As an example, figures 1 and 2 show CURE plots of an SPF 

based on two different levels of segmentation. CURE plots are a common method of assessing model fit 

and the appropriateness of variables in SPF development. A satisfactory CURE plot should have two 

overall characteristics (Srinivasan and Bauer, 2013):

1. Residuals should gently oscillate around zero on the y-axis of the plot

 Drifting up indicates the SPF is underpredicting crashes

 Drifting down indicates the SPF is overpredicting crashes

2. Plotted values should stay within the two standard deviation bounds

Figure 1 plots a model with homogeneity defined only as rural two-lane roads. Figure 2 illustrates a 

model with much stricter segmentation criteria, and it only includes rural two-lane road segments with 

no median, a shoulder width of 2 feet, a lane width of 9 feet, no vertical or horizontal curvature, and an 

AADT of less than 500 vehicles per day. 
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SAFETY DATA CASE STUDY FHWA-SA-21-015

Figure 2. Graphic. CURE plot – more homogenous segmentation. 

Figure 1. Graphic. CURE plot – more heterogenous segmentation. 

© KTC

© KTC
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SAFETY DATA CASE STUDY FHWA-SA-21-015

Analysts developed SPFs using an open source statistical package, Safety Performance Function – R (SPFR), for K and A; B; C; and O crash severity types using five years of crash data from the Kentucky State 

Police. R is an open source statistical package with its own programming language and available for 

download. The SPF-R can be downloaded for use by anyone from FHWA’s Roadway Safety Data 

Program Toolbox. 

Kentucky uses the Empirical-Bayes (EB) method to account for regression-to-the-mean bias by 

combining predictions from the SPF with observed crash data. KTC computed new metrics, the excess 

expected crashes (EEC) and the level of service of safety (LOSS), by comparing the expected crash 

frequency obtained from the EB method with observed crash frequency. Figure 3 Illustrates an example 

of how the EB method is used to compare observed crashes to predicted crashes and calculate the EEC.

Once the KTC finalizes the SPFs, KYTC then incorporates average crash costs, per crash severity level,

to arrive at a figure referred to as the “Cost of EECs.” This value represents the theoretical 

comprehensive crash costs the public bears because of the excessive crashes. The KYTC prioritizes a 

list of sites in each focus area for potential improvement according to the Cost of EECs metric.

Results

KTC updates the SPFs annually, and the results of the analysis, primarily the Cost of EECs metric, help 

“sort and prioritize locations where countermeasures are most identify and rank sites from most likely 

to least likely to realize a reduction in crash frequency with implementation of a countermeasure” 

(AASHTO, 2010, p. 4-1). In practice, these rankings help determine how $30-40 million of annual HSIP 

funding should be spent across Kentucky’s network of roadway segments and intersections. Leadership 

at KYTC has noted that these results are quite valuable in making better investment decisions with their 

safety improvement projects.

Figure 3. Graphic. Diagram illustrating EEC relative to the SPF prediction.

© KTC
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SAFETY DATA CASE STUDY FHWA-SA-21-015

Additional Resources

In addition to the HSM, several FHWA resources contributed to this research and development:

 SPF Development Guide: Developing Jurisdiction-Specific SPFs (Srinivasan and Bauer, 2013)

 The Calibrator: An SPF Calibration and Assessment Tool User Guide (Lyon et al., 2016)

 SPF Decision Guide: SPF Calibration vs SPF Development (Srinivasan et al., 2013)

The FHWA’s Crash Costs for Highway Safety Analysis document also provides national guidance and a 

supporting tool for crash cost development (Harmon et al., 2018). The following documents outline

components of the State’s data development and analysis approach: 

 A Methodology to Prioritize the Locations of Cable Barrier Installations in Kentucky (Green and Fields, 

2015)

 Spatial Database for Intersections (Green et al., 2016)

 Automating Safety Performance Function Development to Improve Regression Models (Green et al., 

2018)

Challenges

The Kentucky analysis comes with many of the same caveats that apply to many States. The availability 

and quality of some data presented challenges to the analysis. Many local roads in Kentucky do not have 

reliable (or any) AADT records available. This required analysts to rely on county-level traffic estimates. 

Additionally, there is some degree of uncertainty regarding the accuracy of crash-location data. The 

Kentucky Traffic Records Advisory Committee is aware of this concern and is in the process of 

investigating it further.

Limitations of the HSM Method and Solutions

A typical challenge associated with network screening involves outlier observations and locations that 

are substantially different from average conditions. The KTC noted that some intersections and 

segments did not match the baseline conditions that were used to develop the SPFs for each facility 

type. The KYTC and KTC applied adjustments factors to the extent possible. However, KTC could not 

effectively apply adjustment factors to every outlier segment or intersection. One common issue 

involves highways which experienced a change in their route designation (i.e., a highway route became a 

bypass or business route). In these instances, crash data might indicate that crashes occurred on the

original highway route, but the milepoint location is incompatible with the new route designation. This 

resulted in incomplete or inaccurate crash data for certain routes. Locations based on latitude and 

longitude coordinates could somewhat overcome this limitation, but it adds necessary processing time 

and is not a guarantee of accuracy. The KTC identified some of these systemic issues and outliers during

its CURE-plot review. Outliers created large changes in the CURE plots (figure 4), and analysts could 

remove these observations from the model to generate better model fit and SPF performance (figure 5). 
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SAFETY DATA CASE STUDY FHWA-SA-21-015

Figure 4. Graphic. CURE plot with a significant outlier around 2,000 AADT. 

Figure 5. Graphic. CURE plot with the significant outlier removed.

© KTC

© KTC
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SAFETY DATA CASE STUDY FHWA-SA-21-015

Conclusions and Lessons Learned

The KYTC needed a data-driven method for prioritizing locations for safety-improvement projects to 

meet HSIP requirements. Through a university partnership, KYTC was able to develop SPFs for five 

different focus areas that span urban and rural roadway segments and intersections. These advanced, 

predictive methods account for regression-to-the-mean bias, compare expected crashes with observed 

crashes to determine excess crashes, and create a metric for finding locations that presented the

greatest cost to the public associated with excess crashes.

In addition to the emphasis of specific focus areas and reference populations in Kentucky, the KTC and 

KYTC project team noted the importance of consistent, accurate data, as well as the appropriateness of 

careful site delineation and homogeneity. Thoughtful consideration of network segmentation was an 

important part of Kentucky’s success. Future iterations of this method can be improved by having more 

complete or better-quality data (e.g., traffic volumes on local roads) for the purposes of SPF 

development.
9 

Contact

Mike Vaughn, Kentucky Transportation Cabinet, Transportation Engineering Specialist

Mike.Vaughn@ky.gov

Eric Green, Kentucky Transportation Center, Research Engineer

eric.green@uky.edu

References

 American Association of State Highway and Transportation Officials. (2010). Highway Safety 

Manual: 1st Edition, Washington, D.C.

 Green, E.R., M.A. Fields. (2015). A Methodology to Prioritize the Locations of Cable Barrier 

Installations in Kentucky, Roadway Safety and Simulation Conference, Orlando, FL; University of 

Central Florida & the University of Tennessee. 

 Green, E.R., C. Blackden, M.A. Fields. (2016). Spatial Database for Intersections, 95th Annual 

TRB, Washington, D.C.; Transportation Research Board. 

 Green, E.R., C. Blackden, R.R. Souleyrette, and W. Staats. (2018). Automating Safety 

Performance Function Development to Improve Regression Models, 97th Annual TRB, Washington, 

D.C.; Transportation Research Board.

 Harmon, T., Bahar, G., and Gross, F. (2018). Crash Costs for Highway Safety Analysis, Federal 

Highway Administration, Report No. FHWA-SA-17-071, Washington, D.C. Available online: 

https://safety.fhwa.dot.gov/hsip/docs/fhwasa17071.pdf

 Lyon, C., Persaud, B., and Gross, F. (2016). The Calibrator—An SPF Calibration and Assessment 

Tool User Guide, Federal Highway Administration, Report No. FHWA-SA-17-016, Washington, 

D.C. Available online: https://safety.fhwa.dot.gov/rsdp/downloads/fhwasa17016.pdf.

 Srinivasan, R., Carter, D., and Bauer, K. (2013). Safety Performance Function Decision Guide: SPF 

Calibration vs SPF Development, Federal Highway Administration, Report No. FHWA-SA-14-

004, Washington, D.C. Available online: 

https://safety.fhwa.dot.gov/rsdp/downloads/spf_decision_guide_final.pdf.

 Srinivasan, R. and Bauer, K. (2013). Safety Performance Function Development Guide: Developing 

Jurisdiction-Specific SPFs, Federal Highway Administration, Report No. FHWA-SA-14-005, 

Washington, D.C. Available online: 

https://safety.fhwa.dot.gov/rsdp/downloads/spf_development_guide_final.pdf.

 The R Foundation. (n.d.). "The R Project for Statistical Computing". (website) The R 

Foundation. Available online: https://www.r-project.org.