Systemic Safety Project Selection Tool

Supplemental Case Studies

December 2016
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Cover Photos

Left: North Dakota Department of Transportation—https://www.flickr.com/photos/nddot/29154311966

License terms: http://creativecommons.org/licenses/by-sa/2.0/legalcode

Top Right: CH2M. 

Bottom Right: Washington County, Minnesota.
Technical Report Documentation Page

1. Report No.

FHWA-SA-17-002

2. Government Accession No. 3. Recipient’s Catalog No.

4. Title and Subtitle

Systemic Safety Project Selection Tool Supplemental Case Studies

5. 

D

Report Date

ecember 2016

6. Performing Organization Code

7. Author(s) 8. Performing Organization Report No.

9. Performing Organization Name and 

Cambridge Systematics, Inc.

555 12th Street, Suite 1600

Oakland, CA 94607

Address

and 

CH2M Hill, Inc.

1295 Northland Drive, 

Mendota Heights, MN 

Suite 200

55120

10. Work Unit No. (TRAIS)

11. Contract or Grant No.

DTFH61-10-D-00020

12. Sponsoring Agency Name and Address

U.S. Department of Transportation

Federal Highway Administration (FHWA)

Office of Safety

1200 New Jersey Avenue, SE

Washington, D.C. 20590

13. Type of Report and Period Covered

14. 

SA

Sponsoring Agency Code

15. Supplementary Notes

FHWA COTM: Karen Scurry, Office of Safety

16. Abstract

FHWA published the Systemic Safety Project Selection Tool (Systemic Tool) in 2013 and, given the condition of the practice at that time, most discussion and 

examples were based on rural applications and were located along a system with strong supporting data for crashes and road system characteristics. Since publication 

of the original Systemic Safety Project Selection Tool (FHWA, 2013), additional systemic analyses have indicated the systemic process can be successfully applied in 

urban areas and along systems with little supporting data. This supplement to the Systemic Safety Project Selection Tool provides two additional case studies 

demonstrating the systemic analysis process. One case study demonstrates how State, county, and local government agencies in Minnesota evaluated pedestrian and 

bicycle safety issues in urban areas and developed a program to address these issues based on risk. The second case study illustrates how North Dakota conducted a 

systemic analysis with little supporting data initially available for analysis.

17. Key Words 

Federal Highway Administration (FHWA); systemic analysis; Strategic 

Highway Safety Plan (SHSP); systemic approach; risk-based approach

18. Distribution 

No restrictions

Statement

19. Security Classif. 

Unclassified

(of this report) 20. Security Classif. 

Unclassified

(of this page) 21. 

43

No. of Pages 22. Price

N/A

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized
Table of Contents

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

Overview of Systemic Approach ................................................................3

Case Study 1: Pedestrian and Bicycle-Focused Systemic Analysis .......5

Case Study 2: Systemic Safety Analysis with Limited Data ..................17

Conclusion .................................................................................................34

References ..................................................................................................35

Appendix A. Glossary ......................................................................... A-1
List of Tables

Table 1. Minnesota Focus Crash Types ..........................................6

Table 2. Minnesota Adopted Risk Factors for Pedestrian 

and Bicycle Crashes ........................................................11

Table 3. Adopted Short-List of Pedestrian and Bicycle 

Countermeasures ............................................................13

Table 4. Final List of Adopted Safety Countermeasures, 

Crash Reduction Factors, and Typical 

Installation Costs .............................................................27
List of Figures

Figure 1. Framework for the Systemic Safety Project 

Selection Tool ....................................................................3

Figure 2. Systemic Safety Planning Process ....................................4

Figure 3. Minneapolis/St. Paul Crash Tree .......................................7

Figure 4. St. Paul Crashes Compared to Functional 

Classification .....................................................................8

Figure 5. Crashes versus Intersection and Intersection 

Control and Crashes versus Speed Limit Pie Chart ..........9

Figure 6. Crashes Compared to Average Daily Traffic 

Volume—Minneapolis/St. Paul ........................................10

Figure 7. Sample Documentation of State System Urban 

Signalized Intersection Screening for Pedestrian 

and Bicycle Risk ..............................................................12

Figure 8. Pedestrian and Bicycle Project Development 

Decision Tree ..................................................................14

Figure 9. Example Pedestrian and Bicycle Safety Project 

Overview for Greater Minnesota ......................................15

Figure 10. Rural Crash Tree .............................................................19

Figure 11. Example of a Visual Trap ................................................20

Figure 12. Sample Edge Risk Assessment Ratings .........................21

Figure 13. Sample Rural Intersection Prioritization/

Bottineau County .............................................................22

Figure 14. Rural Segment Risk Factor Evaluation ............................23

Figure 15. Rural Segment Risk Factor Ranking ...............................24

Figure 16. Rural Curve Risk Factor Ranking ....................................25

Figure 17. Rural Intersection Risk Factor Ranking .......................... 26

Figure 18. Sample Rural Intersection Project Decision Tree ........... 29

Figure 19. Sample Summary of Rural Intersection Projects 

in Bottineau County ........................................................ 30

Figure 20. Sample Map of Suggested Safety Projects 

in Bottineau County ........................................................ 31

Figure 21. Local Road Safety Program Project Summary ............... 32
Systemic Safety Project Selection Tool Supplemental Case Studies 1 Introduction

Introduction

Transportation agencies across the country are focusing efforts on reducing 

the most severe crashes (i.e., those involving fatalities and serious injuries) 

on their system of roadways. With the shift from analysis that looks at all 

severities of crashes to the focus on only the most severe, many safety 

professionals found a new challenge—how to identify candidate locations for 

safety investment.

Safety professionals have found that the density of most types of severe 

crashes is very low and most occur at locations that did not meet definitions 

for designation as high-crash locations. As such traditional hot spot analysis 

methods may overlook many safety investment opportunities. 

To address this challenge, safety professionals developed and began using 

a systemic approach to safety analysis. The systemic approach to safety is

intended to supplement and complement the traditional site analysis. The 

systemic approach is data driven and considers risk as well as crash history. 

The approach involves analyzing and prioritizing roadway facilities based on 

the presence of roadway and traffic characteristics (i.e., risk factors) that are 

found to be common at the locations across a system where severe crashes 

occur. This risk-based approach provides safety program managers the 

information necessary to proactively deploy safety countermeasures at highrisk locations, instead of only reacting to the occurrence of severe crashes. 

FHWA published the Systemic Safety 

Project Selection Tool (Systemic Tool) in 

2013 and, given the state of the practice at 

that time, most discussion and examples 

focused on rural applications located along 

a system with strong supporting data for 

crashes and road system characteristics. 

Since publication of the Systemic Tool, 

safety professionals have asked:

• Can the systemic process be applied 

in applications for pedestrian and 

bicycle crashes? 

• Can the systemic process be 

successfully applied in situations where 

little supporting data are available? 

The answer to both questions is yes. The systemic safety planning process 

can be successfully applied in urban areas for pedestrian and bicycle 

crashes and along systems where little supporting data are available. 

SYSTEMIC SAFETY ANALYSIS

Systemic safety analysis is data 

driven and considers risk as well as 

crash history. 

This risk-based approach provides 

safety program managers the data 

necessary to proactively deploy 

safety countermeasures at high-risk 

locations, instead of only reacting to 

the occurrence of severe crashes. 
2 Systemic Safety Project Selection Tool Supplemental Case Studies 2 Introduction

The purpose of this supplement is to demonstrate the application of the 

systemic safety planning process for these situations through two case 

studies. One case study demonstrates how State, county, and local 

government agencies in Minnesota evaluated pedestrian and bicycle safety 

issues in urban areas and developed a program to address these issues 

based on the identification and assessment of risk factors. The second case 

study illustrates how North Dakota conducted systemic analysis with little 

supporting data available before the project was initiated. 

This supplement serves as a complement to the Systemic Tool, providing 

case study applications of the systemic safety planning process. This 

supplement does not take the place of the technical information and 

guidance provided in the Systemic Tool. Safety professionals should refer to 

the Systemic Tool for additional information and detail on the systemic

safety analysis process.
Systemic Safety Project Selection Tool Supplemental Case Studies 3 Overview of Systemic Approach

Overview of Systemic Approach

The Systemic Tool provides a step-by-step process for conducting systemic 

safety planning, considerations for determining a reasonable distribution 

between the implementation of spot safety improvements and systemic 

safety improvements, and a mechanism for quantifying the benefits of safety 

improvements implemented through a systemic approach. The framework

for the Systemic Tool process (Figure 1) includes three key elements:

• Element 1: The Systemic Safety Planning Process helps safety 

professionals identify priority crash types and associated risk 

factors; evaluate proven low-cost safety countermeasures; prioritize 

alternative candidate locations for safety investment; and develop 

and prioritize safety projects with specific strategies to be deployed 

at specific locations.

• Element 2: A Framework for Balancing Systemic and Traditional 

Safety Investment provides basic data that can aid in setting 

funding goals to support projects identified through the systemic 

and site analysis approaches.

• Element 3: The Evaluation of a Systemic Safety Program provides 

high-level direction for evaluating the effectiveness of systemic 

safety programs. 

Figure 1. Framework for the Systemic Safety Project 

Selection Tool

Source: FHWA, adapted from the Systemic Safety Project Selection Tool, 2013.
Systemic Safety Project Selection Tool Supplemental Case Studies 4 Overview of Systemic Approach

The case studies included in this supplement focus on Element 1: The 

Systemic Safety Planning Process. As shown in Figure 2, the process 

begins by looking at systemwide data to analyze and identify focus crash 

types (those representing the greatest number of severe crashes) and 

potential risk factors. As the downward arrows indicate, the approach then 

moves to a microlevel risk assessment of locations across the network, 

which then leads to selecting relevant mitigating countermeasures most 

appropriate for broad implementation across those locations, and prioritizing 

projects for implementation. The upward arrows indicate that the results of 

one step might suggest the need to return to a previous step and make 

adjustments before continuing the process.

Figure 2. Systemic Safety Planning Process 

Source: FHWA, adapted from the Systemic Safety Project Selection Tool, 2013.

Each step can be scaled based on the availability of technical resources and 

the quality or quantity of data available to support different analytical 

approaches. The case studies included in this supplement demonstrate 

each of these steps, the data used, and overall process outcomes.
Systemic Safety Project Selection Tool Supplemental Case Studies 5 Case Study 1 ‒ Pedestrian and Bicycle-Focused Systemic Analysis

Case Study 1: Pedestrian and Bicycle-Focused Systemic Analysis 

Minnesota’s 2007 Strategic Highway Safety Plan (SHSP) committed to 

increasing the level of engagement of local highway agencies in the 

statewide safety planning process because approximately 50 percent of 

severe crashes (those involving a fatality or incapacitating injury) occurred 

on local roads. Minnesota Department of Transportation (MnDOT) and its

Federal and local partners developed the County Roadway Safety Plans 

Program to provide technical assistance to counties for data-driven systemic 

risk assessment of the county road system. 

Follow up analyses associated with the MnDOT 2014 SHSP identified 

crashes involving pedestrians and bicycles as a statewide priority (MnDOT, 

2014). After successfully completing the county safety plans, MnDOT 

decided to update the safety plans for each of their districts using the 

systemic approach to evaluate and prioritize the State’s highway system. 

The initial analysis of the State’s system also identified crashes involving 

pedestrians and bicycles to be a priority. MnDOT determined that 

approximately 67 percent of all severe crashes involving pedestrians and 

bicyclists occurred in the Minneapolis-St. Paul metropolitan area and more 

than 80 percent of these occurred on local systems. As a result, MnDOT 

partnered with the city of St. Paul to conduct a systemic evaluation of their 

street system with a focus on pedestrian and bicycle crashes. Through these 

three projects, systemic pedestrian and bicycle safety has been investigated 

on the State, county, and municipal roadway systems in Minnesota. The 

following case study illustrates how MnDOT applied the systemic safety 

planning process to pedestrian and bicycle crashes in the urban areas 

across the State’s system in Greater Minnesota (i.e., the 80 Counties 

outside of the seven county Minneapolis St. Paul area), the county system 

across all 87 Counties in the State and Minneapolis/St. Paul. 

The primary sources of data used to support the systemic analysis included:

• MnDOT’s crash records system: Geolocated severe crashes along 

the State and local road systems; it also provided information about 

roadway geometry and intersection control characteristics at the 

crash site and insight about contributing factors. 

• Video-logs: Provided current images of the State and county road 

system (number of lanes, cross-section, and alignment) and road 

edges (in-place traffic signs, on-street parking, boulevards, sidewalks, 

bus stops, and adjacent land use). 

• Google Earth: Supplemented information obtained from the video logs 

and provided a history of roadway and traffic control changes over time. 

• MnDOT’s database of traffic volumes: Provided daily traffic volumes 

along the State highway system and on the State-aided portion of 

county and municipal systems. 
Systemic Safety Project Selection Tool Supplemental Case Studies 6 Case Study 1 ‒ Pedestrian and Bicycle-Focused Systemic Analysis

STEP 1: IDENTIFY FOCUS CRASH TYPES AND RISK FACTORS—TASK 1: SELECT FOCUS CRASH TYPES

Table 1. Minnesota Focus Crash Types

Focus Area Crashes Percent

Lane departure 3,199 45.5%

Intersection 2,945 41.9%

Unbelted occupants 2,463 35.0%

Impaired roadway users 1,850 26.3%

Younger drivers 1,367 19.4%

Inattentive drivers 1,319 18.7%

Speed 1,309 18.6%

Motorcyclists 1,244 17.7%

Older drivers 1,028 14.6%

Commercial vehicles 714 10.1%

Unlicensed drivers 702 10.0% 

Pedestrians 649 9.2%

Bicyclists 286 4.1%

Work zones 103 1.5%

Trains 21 0.3%

Source: Minnesota SHSP http://www.dot.state.mn.us/trafficeng/safety/shsp/. 

Purpose

Identify the high-priority emphasis area—categories of 

severe crashes that represent the greatest opportunity 

for reduction.

Description

• Demonstrates disaggregation of Minnesota’s

statewide crash data.

• The Minnesota State SHSP identifies

pedestrian and bicycle crashes on the list of

focus crash types (Table 1). 

• Studying the statewide pedestrian and bicycle 

crash data in further detail showed: 

» In almost 420 cities across Greater 

Minnesota, on average there were 8 severe 

pedestrian and bicycle crashes per year.

Although a low number, it is the second 

highest of any crash type (second to right

angle collisions).

» In Minnesota’s largest urban area 

(Minneapolis/St. Paul), there are 

approximately 61 severe pedestrian and 

bicycle crashes per year and that is the 

highest total of any crash type.

» Two-thirds of all severe pedestrian and 

bicycle crashes in the State occur in the 

Minnesota/St. Paul metropolitan area.
Systemic Safety Project Selection Tool Supplemental Case Studies 7 Case Study 1 ‒ Pedestrian and Bicycle-Focused Systemic Analysis

STEP 1: IDENTIFY FOCUS CRASH TYPES AND RISK FACTORS—TASK 2: SELECT FOCUS FACILITIES

Figure 3. Minneapolis/St. Paul Crash Tree

Source: Adapted from CH2M, 2014. 

Purpose

Identify where crash types most frequently occur.

Description

• This is a crash tree analysis focusing on the 

pedestrian and bicycle crashes in the 

Minneapolis/St. Paul metropolitan area. The 

disaggregation of the county road system crashes 

allowed for focus facilities to be identified for the 

County Road Safety Plans.

» Analysis shows approximately 67 percent of 

all severe crashes involving pedestrians and 

bicyclists occurred in the Minneapolis/St. 

Paul metropolitan area and approximately 80 

percent of these crashes occurred on the 

local system.

» Among all roadway types, approximately 65 

percent of severe pedestrian and bicycle 

crashes occur at intersections.
Systemic Safety Project Selection Tool Supplemental Case Studies 8 Case Study 1 ‒ Pedestrian and Bicycle-Focused Systemic Analysis

STEP 1: IDENTIFY FOCUS CRASH TYPES AND RISK FACTORS—TASK 2: SELECT FOCUS FACILITIES (CONTINUED) 

Figure 4. St. Paul Crashes Compared to Functional Classification

Source: Adapted from CH2M, 2016.

40.2%

48.7%

11.1%

41.1%

49.8%

9.2%

28.5%

14.6%

78.5%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

Arterial Collector Local

Total Crashes Severe Crashes Miles

Purpose

Identify where crash types most frequently occur.

Description

• This graph compares the proportion of roadway 

mileage to the proportion of crashes along 

St. Paul’s municipal roadway system.

• The initial analysis of pedestrian and bicycle 

crashes in St. Paul found that 91 percent of 

these crashes occurred on 43 percent of the 

system roads classified as arterials and 

collectors (Figure 4).

• These data support the decision to designate 

arterials and collectors as the focus facility 

types and to concentrate on the detailed 

analysis of these roads because the large 

number of severe crashes provided the greatest 

opportunity for reduction.
Systemic Safety Project Selection Tool Supplemental Case Studies 9 Case Study 1 ‒ Pedestrian and Bicycle-Focused Systemic Analysis

STEP 1: IDENTIFY FOCUS CRASH TYPES AND RISK FACTORS—TASK 3: IDENTIFY AND EVALUATE RISK FACTORS 

Figure 5. Crashes versus Intersection and Intersection Control and Crashes 

versus Speed Limit Pie Chart

Source: Adapted from MnDOT, 2015. 

Purpose

Identify roadway characteristics to use as initial set of potential 

risk factors to be further evaluated for use in systemic network 

screening. In these systemic analyses, the largest group of 

attainable urban data was used to increase the data sample size; 

in some instances this is Minneapolis/St. Paul metropolitan data, 

and in other cases statewide. 

Description

Examples of the results of these analyses supporting the 

adoption of a set of risk factors in the Minneapolis/St. Paul 

metropolitan area include:

• More than 60 percent of the severe pedestrian and bicycle 

crashes occurred at intersections and almost 60 percent 

of these intersections had traffic signal control.

• Almost 60 percent of the locations with severe pedestrian 

and bicycle crashes had a 30-mile-per-hour speed limit, 

which is the statutory speed limit in urban areas in 

Minnesota. 

• While not shown here, the analysis showed approximately 

75 percent of severe pedestrian and bicycle crashes 

occurred at intersections where a pedestrian generator 

(retail or institutional land use) was present. 

• Almost 60 percent of severe pedestrian and bicycle 

crashes occurred at intersections where the major road 

entering volume was greater than 17,500 vehicles per day 

(Figure 6).
Systemic Safety Project Selection Tool Supplemental Case Studies 10 Case Study 1 ‒ Pedestrian and Bicycle-Focused Systemic Analysis

STEP 1: IDENTIFY FOCUS CRASH TYPES AND RISK FACTORS—TASK 3: IDENTIFY AND EVALUATE RISK FACTORS (CONTINUED)

Figure 6. Crashes Compared to Average Daily Traffic Volume—Minneapolis/St. Paul

3%

11%

13% 13%

15%

10%

5% 5%

3%

21%

13%

12%

15% 16%

13%

10%

6%

3% 4%

9%

0%

5%

10%

15%

20%

25%

0<10000 10000<12500 12500<15000 15000<17500 17500<20000 20000<22500 22500<25000 25000<27500 27500<30000 30000+

Major Entering Average Daily Traffic

Source: CH2M, 2016. 

Severe Ped/Bike Crashes (61 total) Intersections (668 total)
Systemic Safety Project Selection Tool Supplemental Case Studies 11 Case Study 1 ‒ Pedestrian and Bicycle-Focused Systemic Analysis

STEP 1: IDENTIFY FOCUS CRASH TYPES AND RISK FACTORS—TASK 3: IDENTIFY AND EVALUATE RISK FACTORS (CONTINUED)

Table 2. Minnesota Adopted Risk Factors for Pedestrian and Bicycle Crashes

Characteristic Minnesota County 

Roadway Safety Plans

Minnesota District 

Safety Plans

Traffic Signal  

Speed Limit  

Four Legs 

Undivided 

Bus Stop 

Ped Generator  

Volume  

Skew 

Curve 

Source: CH2M, 2016. 

Purpose

Identify roadway characteristics and network elements 

to use as an initial set of potential risk factors to be 

further evaluated for use in systemic network screening.

Description

• The results supported the adoption of a set of

risk factors that were used in the systemic 

evaluation of the local system in the 

Minneapolis/St. Paul metropolitan area and for 

the State system in Greater Minnesota 

(Table 2).

• The systemic analyses of Minnesota’s county

and State systems found four common

characteristics among the intersections with

severe pedestrian and bicycle crashes:

» Traffic signal control.

» Speed limit (major approach).

» Presence of pedestrian generator.

» Traffic volume.

These same four characteristics applied to the 

St. Paul municipal system along its arterial and 

collector facilities.
Systemic Safety Project Selection Tool Supplemental Case Studies 12 Case Study 1 ‒ Pedestrian and Bicycle-Focused Systemic Analysis

STEP 2: SCREEN AND PRIORITIZE CANDIDATE LOCATIONS—TASKS 1-3: PRIORITIZE FOCUS FACILITY ELEMENTS

Figure 7. Sample Documentation of State System Urban Signalized Intersection 

Screening for Pedestrian and Bicycle Riska

Source: CH2M, 2016. 

a This step was not conducted for the Minneapolis-St. Paul metropolitan area. This figure shows how the 

process was conducted for urban areas in MNDOT District 3 in the north central part of the State. 

b Cross Product—Multiplication of the average minor approach volumes and average major approach volumes.

Purpose

Evaluate the risk factors of the systems and locations 

selected for analysis using roadway and traffic 

characteristics in order to rank/prioritize at-risk 

locations. While this step was not conducted for the 

Minneapolis/St. Paul area, Figure 7 shows the process 

for urban areas in MNDOT District 3 in the north central 

part of the State. 

Description

• Aerial photography and video-logs were used to

screen the focus facilities (State highways in

urban areas in Greater Minnesota and arterials

and collectors along local systems in the

Minneapolis/St. Paul Metropolitan area) to

document risk factors present at each of the

1,979 signalized intersections.

• The systemic evaluation produced a

prioritization of intersections as candidates for 

safety investment based on the number of risk

factors present.

• Where the number of risk factors present was

equal across a number of intersections, the

ranking was then based on the designated

crash cost.

• The tabular format documents both the overall

prioritization of intersections and documentation

of the risk factors present.

b
Systemic Safety Project Selection Tool Supplemental Case Studies 13 Case Study 1 ‒ Pedestrian and Bicycle-Focused Systemic Analysis

STEP 3: SELECT COUNTERMEASURES—TASK 3: SELECT COUNTERMEASURES FOR DEPLOYMENT

Table 3. Adopted Short-List of Pedestrian and Bicycle Countermeasures

Countermeasure Crash Reduction Factor Implementation Cost

Countdown Timers 25% $12,000 per intersection

Leading Pedestrian Interval Up to 60% $600 per intersection

Lighting 33%-44% $10,000-$25,000

per intersection

Curb Extensions 30%-46% $36,000 per corner

Median Refuge Island 39%-46% $24,000 per approach

High-intensity Activated crossWalK (HAWK) Signals 69% $50,000-$120,000

Rapid Flash Beacons Increase Yield to 

Pedestrians $15,000

Source: CH2M, 2016. 

Purpose

Assemble an initial comprehensive list of 

countermeasures. Evaluate and screen the initial list to 

identify feasible countermeasures for implementation. 

Identify and select countermeasures for each focus 

crash type based on the evaluation of the 

countermeasures and consideration of agency 

priorities, practices, and policies.

Description

• Analysts evaluated a short-list of pedestrian and

bicycle on two key issues: 1) effectiveness and

2) implementation cost.

• The adopted short list of countermeasures was

primarily focused on signalized intersections,

but also included a variety of strategies that

could be used at Thru/ STOP controlled

intersections and one strategy primarily

intended for urban segments. The adopted

countermeasures, documented crash reduction

factors, and estimated implementation costs are

documented in Table 3.

• Analysis of Minnesota’s crash data indicated

that the majority of bicycle involved crashes

occurred at intersections, the same as for 

pedestrian involved crashes. As a result

analysts concluded that countermeasures

focused on intersections were equally

applicable to mitigating both pedestrian and

bicycle involved crashes.
Systemic Safety Project Selection Tool Supplemental Case Studies 14 Case Study 1 ‒ Pedestrian and Bicycle-Focused Systemic Analysis

STEP 4: PRIORITIZE PROJECTS—TASK 1: CREATE A DECISION PROCESS FOR COUNTERMEASURE SELECTION

Figure 8. Pedestrian and Bicycle Project Development Decision Tree

Source: CH2M, 2016. 

Purpose

Develop a decision process to facilitate consistency in 

the selection of countermeasures.

Description

• After identifying at-risk, high priority candidates

for safety investment, safety professionals

developed safety projects at each location. The

project development process considered key

features of intersections, including traffic

control, street cross-section, presence of onstreet parking, presence of a pedestrian

generator, and major road volume to develop a

project decision tree (Figure 8).

• Examples of the documentation of the project

development along the State’s urban highways

in Greater Minnesota (Figure 9) include

intersection identification, type of intersection

control, presence of street lighting and on-street

parking, and the suggested safety project based

on the application of the decision tree.
Systemic Safety Project Selection Tool Supplemental Case Studies 15 Case Study 1 ‒ Pedestrian and Bicycle-Focused Systemic Analysis

STEP 4: PRIORITIZE PROJECTS—TASK 2-3: DEVELOP AND PRIORITIZE SAFETY PROJECTS

Figure 9. Example Pedestrian and Bicycle Safety Project Overview for Greater Minnesota

Source: CH2M, 2016. 

The information in Figure 9 (abridged from complete project list) indicates that 14 of the 16 high risk intersections were suggested for projects—

including countdown timers at signalized intersections, one HAWK, one Rectangular Rapid Flash Beacon, three intersections with curb extensions, 

and three more with median refuge islands. In total, the systemic safety process identified approximately $1.7 million worth of pedestrian and 

bicycle projects along the local system in the Minneapolis/St. Paul Metropolitan area and $11.5 million worth of projects along urban State 

highways in Greater Minnesota. 
Systemic Safety Project Selection Tool Supplemental Case Studies 16 Case Study 1 ‒ Pedestrian and Bicycle-Focused Systemic Analysis

SUMMARY

Data generated as part of the systemic risk evaluations conducted along the 

State’s urban highways in Greater Minnesota and along the local system in 

the Minneapolis/St. Paul Metropolitan area demonstrate:

• Systemic process assisted in the identification of focus crash and

facility types.

• Adoption of a set of risk factors.

• Screening and prioritizing of the systems.

• Development of a short-list of safety countermeasures.

• Identification of more than $13 million worth of pedestrian and bicycle

focused safety projects at designated high-risk candidate locations.
Systemic Safety Project Selection Tool Supplemental Case Studies 17 Case Study 2 ‒ Systemic Safety Analysis with Limited Data

Case Study 2: Systemic Safety 

Analysis with Limited Data

The following case study illustrates how the North Dakota Department of 

Transportation (NDDOT) applied the systemic safety planning process as a part 

of a statewide effort to provide technical support and funding to prepare safety 

plans for 53 counties and 12 major cities. The initial analysis focused on 

highway-related crashes and disaggregated the crashes by system (i.e., State, 

county and city; urban and rural; roadway segments and intersections) and by 

type of crash (i.e., road departure, right angle, pedestrian, and bicycle). In 

addition, the analysis focused on paved county roads plus a category called 

County Major Collectors because these roads accounted for 52 percent of 

severe crashes, even though they accounted for approximately 10 percent of 

the county system by mileage. 

NDDOT was able to provide comprehensive geolocated crash data for the 

analyses, but limited roadway data. This case study demonstrates how a systemic 

safety analysis can be conducted with limited available roadway data. In this case, it

is possible to supplement available data with commercial aerial photography, a 

photo inventory taken by project staff, and analytical judgment. The data was 

collected and analyses conducted for urban and rural environments in North 

Dakota. However, this case study focuses on the rural environment only. The 

following data sources were used to conduct the analysis:

• Crash Data—North Dakota’s database

provided 5 years of geolocated crashes along

the local system, an overview of key roadway

characteristics, the relationship to

intersections, and a set of contributing factors.

• Aerial/Street-Level Imagery—Roadway

street-level imagery—NDDOT could not

provide street-level imagery of the local

system, so safety professionals used aerial

photography (Google Earth) supplemented

with a photo inventory of rural county roads

taken by project staff to document roadway

characteristics. A photo inventory of the city

streets was not necessary because Google

Earth, although limited, provided street view

images used for this purpose.

• Traffic volume—NDDOT provided current

or forecast daily traffic volumes for

approximately 40 percent of the system

under investigation, primarily the more

important and higher-volume roads and city

streets. For the remainder of the system,

traffic volume data were estimated using

one of three approaches:

DATA FOR SYSTEMIC SAFETY

ANALYSIS

It is necessary to have some data to 

conduct systemic safety analysis. 

Data can be gathered through 

traditional engineering field work; 

and estimated using engineering 

judgement. For example, an analyst 

could estimate traffic volumes for 

specific facilities based on average 

or typical volumes on comparable 

facilities; roadway speeds could be 

categorized based on local 

knowledge of traffic flow; or roadway 

characteristics can be estimated 

from aerial photography and street 

view photos from Google Earth. 

If the resources are not available to 

acquire data for a systemic safety 

analysis, agencies may want to 

consider implementing low cost 

safety countermeasures systemwide 

as part of roadway maintenance or 

other improvement projects. 
Systemic Safety Project Selection Tool Supplemental Case Studies 18 Case Study 2 ‒ Systemic Safety Analysis with Limited Data

− Local agencies were asked to provide any volume data they might 

have that were not included in the State database.

− If volume data were not available for a particular segment but were 

available on segments upstream or down, project staff interpolated 

the data. 

− If no volume data were available, a default of 29 vehicles per day in 

rural areas was derived from a sample of automatic traffic recording 

stations around the State. Note the number was not rounded to 30 

to serve as a “flag” for safety professionals to know this was an 

assumed value. 
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Systemic Safety Project Selection Tool Supplemental Case Studies 19 Case Study 2 ‒ Systemic Safety Analysis with Limited Data

STEP 1: IDENTIFY FOCUS CRASH TYPES AND RISK FACTORS—TASKS 1-2: SELECT FOCUS CRASH TYPES AND FACILITIES

Figure 10. Rural Crash Tree

Source: North Dakota Local Road Safety Program, CH2M, 2015. 

Purpose

Identify where crash types most frequently occur.

Description

• This is a crash tree analysis focusing on rural 

crashes in North Dakota (Figure 10).

• The focus crash types are: 

» Lane departure crashes.

» Right-angle crashes.

» Segments, horizontal curves.

» Thru/STOP controlled intersections.
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Systemic Safety Project Selection Tool Supplemental Case Studies 20 Case Study 2 ‒ Systemic Safety Analysis with Limited Data

STEP 1: IDENTIFY FOCUS CRASH TYPES AND RISK FACTORS—TASK 3: IDENTIFY AND EVALUATE RISK FACTORS

Figure 11. Example of a Visual Trap

Source: North Dakota Local Road Safety Program, CH2M, 2015. 

Purpose

Identify selected risk factors.

Description

The adopted rural risk factors and data sources include:

• Rural Horizontal Curves: 

» Occurrence of a severe crash—from NDDOT crash records. 

» Range of curve radii—estimated from Google Earth. 

» Range of daily traffic volume—from database, interpolation or default. 

» Presence of an intersection—from Google Earth. 

» Presence of a visual trap—from Google Earth and photo inventory 

(Figure 11). 

• Rural Segments: 

» Range of average daily traffic volume ‒ from database, estimate 

or default. 

» Density of lane departure crashes—from NDDOT crash records. 

» Access density—from Google Earth. 

» Curve density—from Google Earth. 

» Edge risk assessment: from photo inventory (Figure 12). 

• Rural Intersections: 

» Occurrence of an intersection related crash—from NDDOT 

crash records. 

» Skewed minor approaches—from Google Earth. 

» In/near curve—from Google Earth. 

» Cross-product of major and minor street traffic volume—from database, 

estimate or default. 

» Presence of commercial development—from Google Earth and 

photo inventory. 

» Distance (along the minor approaches) to the previous STOP sign—

from Google Earth. 

» Proximity to a rail grade crossing—from Google Earth.
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Systemic Safety Project Selection Tool Supplemental Case Studies 21 Case Study 2 ‒ Systemic Safety Analysis with Limited Data

STEP 1: IDENTIFY FOCUS CRASH TYPES AND RISK FACTORS— 

TASK 3: IDENTIFY AND EVALUATE RISK FACTORS (CONTINUED) 

Figure 12. Sample Edge Risk Assessment Ratings 

. 

Source: North Dakota Local Road Safety Program, CH2M, 2015. 

Purpose

Identify roadway characteristics to use as an initial set of potential risk factors to 

be further evaluated for use in systemic network screening.

Description

• North Dakota project safety professionals reviewed the following 

State and national research reports to identify relationships between 

crashes and roadway characteristics:

» National Cooperative Highway Research Program (NCHRP)

Report 500 Series, (NCHRP, 2003-2009).

» American Association of State Highway and Transportation 

Officials (AASHTO) Highway Safety Manual (AASHTO, 2010). 

» FHWA Crash Modification Factors Clearinghouse.

• For example, the AASHTO Highway Safety Manual (HSM) has 

identified the following variables to be dependent factors in the 

computation of each category: 

» Edge risk in the computation of the estimated number of 

crashes along rural segments (Figure 12). 

» Skew in the computation of crashes at rural intersections. 

» Speed limit in the computation of crashes along urban segments. 

• Edge risk was one of the risk factors adopted for the evaluation of 

rural road segments. Safety professionals were trained using this set 

of photos to assign a rating of Good (Photo 1) where they observed a 

usable shoulder and a reasonable clear zone, a rating of Poor 

(Photo 3) where they observed no usable shoulder, and a roadside 

with obstacles and a rating of Fair (Photo 2) if they found a variety 

of features.
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Systemic Safety Project Selection Tool Supplemental Case Studies 22 Case Study 2 ‒ Systemic Safety Analysis with Limited Data

STEP 2: SCREEN AND PRIORITIZE CANDIDATE LOCATIONS—TASK 2: CONDUCT RISK ASSESSMENT

Figure 13. Sample Rural Intersection Prioritization/Bottineau County

Abridged version of complete table. Partial data shown. 

Source: North Dakota Local Road Safety Program, CH2M, 2015. 

Purpose

Identify network elements from the focus facility types 

which represent the locations where the focus crash 

types tend to occur. The elements are for use in 

network screening.

Description

• Using Google Earth and the photo inventory, 

analysts conducted a census of the system and 

screened segments (12,000 miles), horizontal 

curves (1,800), and intersections (3,400), noting 

the presence of risk factors observed at each 

location in a spread sheet. 

• The prioritization of the segments, curves, and 

intersections was based on the number of risk 

factors—the higher the number of risk factors, 

the higher the priority. In most cases, the 

presence of three or more risk factors resulted 

in designation of high-priority and each of those 

locations were subject to the project 

development process (Figure 13). 
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Systemic Safety Project Selection Tool Supplemental Case Studies 23 Case Study 2 ‒ Systemic Safety Analysis with Limited Data

Step 2: Screen and Prioritize Candidate Locations—Tasks 1-3: Prioritize Focus Facility Elements

Figure 14. Rural Segment Risk Factor Evaluation

0.013

0.017

0.013

0.009

0.009

0.006

0.004

0.007

0.008 0.009 0.40 0.36

0.29

0.36

0.44

60%

64%

71%

64%

56%

0%

10%

20%

30%

40%

50%

60%

70%

80%

0.000

0.002

0.004

0.006

0.008

0.010

0.012

0.014

0.016

0.018

ADT Range Lane Departure

Density

Access Density Critical Curve Radius

Density

Edge Risk

Risk Factor

Severe Crash Density With Severe Crash Density Without % Miles With % Miles Without

Crash Density

Source: North Dakota Local Road Safety Program, CH2M, 2015. 

Purpose

Assess selected risk factors to determine their feasibility 

for differentiating between elements (curves, segments, 

and intersections) on the focus facility type.

Description

• Rural County Road Segment—11,200 miles of 

rural paved and gravel major county collectors 

were evaluated

» Average crash density equaled 0.004 severe 

lane departure crashes per mile per year. 

For example, high levels of access density 

was adopted as one of the risk factors for 

Lane Departure crashes along rural 

segments. High levels of access density 

were found along a minority of the system 

(30%) and the crash density at these 

locations was almost double (0.013 versus 

0.007) that at locations with low levels of 

access density. 

• The results confirm the use of average daily 

traffic (ADT), access density, curve density, and 

edge risk assessment as risk factors; the 

density of severe crashes was consistently 

higher in segments with each factor present, 

compared to segments without the factor 

(Figure 14).
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Systemic Safety Project Selection Tool Supplemental Case Studies 24 Case Study 2 ‒ Systemic Safety Analysis with Limited Data

STEP 2: SCREEN AND PRIORITIZE CANDIDATE LOCATIONS—TASK 3: PRIORITIZE FOCUS FACILITY ELEMENTS—THRESHOLD FOR 

SELECTING CANDIDATE LOCATIONS

Figure 15. Rural Segment Risk Factor Ranking

0.004 0.004

0.009

0.013

0.022

0.022

0.002

0.003

0.006

0.010

0.019

0.022

12%

30%

28%

21%

7%

1%

0%

5%

10%

15%

20%

25%

30%

35%

0.000

0.005

0.010

0.015

0.020

0.025

« «« ««« «««« «««««

Severe Crash Densities Severe Lane Departure Crash Densities Statewide

Source: North Dakota Local Road Safety Program, CH2M, 2015. 

Purpose

Identify network elements from the focus facility types 

which represent the locations where the focus crash 

types tend to occur. The elements are for use in 

network screening.

Description

• Rural roadway segments with three or more risk 

factors were considered high priority and had 

crash densities above the systemwide average 

(0.05) (Figure 15).

• This data supports the use of the adopted risk 

factors and the validity of the results of the 

evaluation. Crashes are not uniformly distributed 

across the system—the majority of severe lane 

departure crashes occurred along a minority of 

the rural county system (27% of miles).
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Systemic Safety Project Selection Tool Supplemental Case Studies 25 Case Study 2 ‒ Systemic Safety Analysis with Limited Data

STEP 2: SCREEN AND PRIORITIZE CANDIDATE LOCATIONS—TASK 3: PRIORITIZE FOCUS FACILITY ELEMENTS—THRESHOLD FOR 

SELECTING CANDIDATE LOCATIONS (CONTINUED) 

Figure 16. Rural Curve Risk Factor Ranking

0.038

0.065

0.086

0.106

0.166

0.455

0.002 0.004

0.008 0.015 0.031

0.200

0.004

0.001 0.006 0.012 0.022

0.236

28%

32%

24%

12%

4%

1% 0%

5%

10%

15%

20%

25%

30%

35%

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

0.400

0.450

0.500

« «« ««« «««« ««««« Total Crash Density Severe Crash Density

Severe Lane Departure Crash Density Percent of Curves Receiving Stars

Source: North Dakota Local Road Safety Program, CH2M, 2015.

• Rural County Horizontal Curves—1,811 

horizontal curves were evaluated

» 77 percent of these curves had no 

crashes during the study period and 14 

percent had one crash; no curves along 

the local rural system averaged one 

severe crash per year.

» The results also confirm the presence of 

intersections and visual traps as risk 

factors. Curves with these features had 

crash densities almost 50 percent 

higher than curves without.

» The highest priority curves (those with 

all five risk factors present) totaled 

approximately 1 percent of the system 

and had crash densities more than five 

times greater than the system average 

(Figure 16).
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Systemic Safety Project Selection Tool Supplemental Case Studies 26 Case Study 2 ‒ Systemic Safety Analysis with Limited Data

STEP 2: SCREEN AND PRIORITIZE CANDIDATE LOCATIONS—TASK 3: PRIORITIZE FOCUS FACILITY ELEMENTS—THRESHOLD FOR 

SELECTING CANDIDATE LOCATIONS (CONTINUED) 

Figure 17. Rural Intersection Risk Factor Ranking

0.001 0.001

0.008

0.029

0.018

0.049

0.057

0.000 0.000

0.001

0.005

0.016

0.012

0.027

0.057

0.000

16%

29%

26%

19%

7% 3%

0%

0% 0%

5%

10%

15%

20%

25%

30%

35%

0

0.01

0.02

0.03

0.04

0.05

0.06

« «« ««« «««« ««««« «««««« «««««««

Severe Crash Density Severe Right Angle Crash Density Percent of Intersections

Source: North Dakota Local Road Safety Program, CH2M, 2015.

• Rural Intersections—2,202 intersections 

were evaluated: 

» 95 percent of rural, Thru/STOP intersections 

had no severe crashes and only 4 percent 

had one severe crash.

» The results also confirm the use of skew, the 

presence of commercial development, and 

distances greater than 5 miles (along the 

minor approaches) to the last STOP sign, as 

risk factors. The density of severe crashes at 

intersections with these features was 

consistently higher in all cases.

» Intersections with three or more risk factors 

were considered a priority and these 

intersections had crash densities 5 to 10 

times higher than the statewide average 

(Figure 17). 
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Systemic Safety Project Selection Tool Supplemental Case Studies 27 Case Study 2 ‒ Systemic Safety Analysis with Limited Data

STEP 3: SELECT COUNTERMEASURES—TASK 3: SELECT COUNTERMEASURES FOR DEPLOYMENT 

Table 4. Final List of Adopted Safety Countermeasures, Crash Reduction Factors, 

and Typical Installation Costs

Strategy

Crash Reduction Factor 

(Based on review of CMF 

Clearinghouse and other published 

research. ND DOT requested that the 

factors be stated as Crash Reduction 

Factors (CRFs))

Typical 

Installation 

Costs

Rural Segments

4-inch latex edge line Values not available at the time 

the project was conducted

$1,320 per mile

4-inch latex centerline Values not available at the time 

the project was conducted

$660 per mile

6-inch latex edge line 10% to 45% all rural 

serious crashes

$1,980 per mile

Shoulder or edge line rumble strips 20% run off-road crashes $5,850 per mile

Ground in wet-reflective markings N/A $36,000 per mile

Centerline rumble strips 40% head-on/sideswipecrashes$3,600 per mile

6-inch centerline N/A $1,020 per mile

Rural Curves 

Chevrons 20% to 30% $3,960 per curve

Large arrow sign N/A $1,200 per curve

Advance warning sign and advisory speed plaque N/A $1,440 per curve

2-foot paved shoulder and shoulder rumble strips 20% to 30% run-off-theroad crashes

$54,000 per mile +

$5,850 per mile

Purpose

Identify and select a few countermeasures for each 

focus crash type based on the evaluation of the 

countermeasures and consideration of agency 

priorities, practices and policies.

Description

• A list of potential safety countermeasures was 

compiled from the North Dakota SHSP and 

published safety research, primarily the NCHRP 

Report 500 Series (NCHRP, 2003-2009), FHWA 

Crash Modification Factors Clearinghouse, and 

the AASHTO Highway Safety Manual

(AASHTO, 2010). 

• The first level of screening of the 

countermeasures focused on identifying those 

that were associated with the identified focus 

crash types. The next level of screening 

focused on the general effectiveness (crash 

reduction) and typical implementation costs of 

the remaining countermeasures. 

• The final list of countermeasures was consistent 

with the North Dakota SHSP (NDDOT, 2013)

and with the priorities identified by NDDOT 

safety program managers (Table 4).
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Systemic Safety Project Selection Tool Supplemental Case Studies 28 Case Study 2 ‒ Systemic Safety Analysis with Limited Data

Table 4. Final List of Adopted Safety Countermeasures, Crash Reduction Factors, 

and Typical Installation Costs (continuation)

Strategy

Crash Reduction Factor 

(Based on review of CMF 

Clearinghouse and other published 

research. ND DOT requested that the 

factors be stated as Crash Reduction 

Factors (CRFs))

Typical 

Installation 

Costs

Rural Intersections

Roundabout 20% to 50% all 

crashes/60% to 90% rightangle crashes

$4,200,000 per 

intersection

Directional median (RCI or J-Turn) 17% all crashes/

100% angle crashes

$1,080,000 per 

intersection

Mainline dynamic warning sign 50% all crashes/

75% serious right-angle 

crashes

$60,000 per intersection

Close median N/A $30,000 per intersection

Intersection lighting 25% to 40% nighttime crashes $10,200 per streetlight

Upgrade signs and pavement markings 40% upgrade of all signs 

and pavement markings/

15% for STOP AHEAD 

pavement marking

$2,640 per approach 

(Includes $540 per STOP sign, $540 

per junction sign assembly, $600 per 

STOP AHEAD sign, $600 per STOP 

AHEAD pavement marking 

message, and $360 per stop bar)

Clear sight triangle 37% serious injury crashes

(Reduction based on increasing 

sight distance triangle)

$2,940 per intersection

(Inclusive of sign upgrades 

identified and materials and labor 

for clearing of sight triangle.)

Source: North Dakota Local Road Safety Program, CH2M, 2015.

Note: N/A = not applicable.
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Systemic Safety Project Selection Tool Supplemental Case Studies 29 Case Study 2 ‒ Systemic Safety Analysis with Limited Data

STEP 4: PRIORITIZE PROJECTS—TASK 1: CREATE A DECISION PROCESS FOR COUNTERMEASURE SELECTION

Figure 18. Sample Rural Intersection Project Decision Tree

Source: North Dakota Local Road Safety Program, CH2M, 2015. 

Purpose

Develop a decision process to facilitate 

consistency in the selection of countermeasures.

Description

• Decision tress (Figure 18) were developed to 

identify basic roadway and traffic 

characteristics and, depending on the exact 

features present at specific locations, pointed 

safety professionals toward a specific 

countermeasure determined to be suitable for 

a specific combination of characteristics.

• For example, the approach to safety project 

development at rural intersections 

(Figure 18) focused on providing enhanced 

intersection recognition or a reduction in the 

number of intersection conflicts. At-risk, 

high-priority, low-volume intersections would 

receive less costly improvements (generally 

upgraded signs and pavement markings), 

higher volume intersections would receive 

more costly improvements (upgraded signs 

and markings, plus streetlights), and the 

highest volume intersections would receive 

the most costly improvements (dynamic 

warning signs or directional median 

treatments at intersections with multi-lane 

divided State highways).
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Systemic Safety Project Selection Tool Supplemental Case Studies 30 Case Study 2 ‒ Systemic Safety Analysis with Limited Data

STEP 4: PRIORITIZE PROJECTS—TASK 2: DEVELOP SAFETY PROJECTS

Figure 19. Sample Summary of Rural Intersection Projects in Bottineau County

Source: North Dakota Local Road Safety Program, CH2M, 2015. 

Purpose

Apply decision process to develop specific safety projects 

for each candidate site selected for safety investment.

Description

• Results of the project development effort were 

reported to local agencies in a series of spread 

sheets (Figure 19) and maps (Figure 20).
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Systemic Safety Project Selection Tool Supplemental Case Studies 31 Case Study 2 ‒ Systemic Safety Analysis with Limited Data

Figure 20. Sample Map of Suggested Safety Projects in Bottineau County

Source: North Dakota Local Road Safety Program, CH2M, 2015. 
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Systemic Safety Project Selection Tool Supplemental Case Studies 32 Case Study 2 ‒ Systemic Safety Analysis with Limited Data

STEP 4: PRIORITIZE PROJECTS—TASK 3: PRIORITIZE SAFETY PROJECT IMPLEMENTATION

Figure 21. Local Road Safety Program Project Summary

Source: North Dakota Local Road Safety Program, CH2M, 2015. 

Purpose

Identify the order in which projects will be implemented.

Description

The end result of the Local Road Safety Program 

(LRSP) effort was the production of a safety plan 

for each of North Dakota’s 53 counties, 12 major 

cities, 4 Tribes, and Theodore Roosevelt National 

Park. These plans documented the results of the 

systemic risk analysis and identified approximately 

3,000 safety projects with total estimated 

implementation costs approaching $55 million. A 

breakdown and summary of the suggested 

projects are provided for one county in Figure 21. 
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Systemic Safety Project Selection Tool Supplemental Case Studies 33 Case Study 2 ‒ Systemic Safety Analysis with Limited Data

SUMMARY

The lack of a complete and comprehensive data set did not prevent the 

completion of the systemic safety process for each of North Dakota’s 53 

counties, 12 major cities, 4 Tribes, and Theodore Roosevelt National Park and 

engaging the local agencies in the statewide safety planning effort. By 

combining the data that was available, along with the generated, surrogate, and 

default values discussed herein, the systemic risk assessment was successfully 

conducted resulting in the prioritization of the facilities and the development of 

approximately 3,000 safety projects. Even in a low-data environment where a 

variety of assumptions and field data collection techniques were devised, the 

systemic process still identified almost $55 million worth of safety projects with 

an average implementation cost in the range of $18,000. 

These data support a conclusion that the LRSP achieved the initial objectives:

• A systemic risk evaluation of both rural and urban local systems was 

completed. The rural portion of the evaluation was shown in this case 

study for brevity.

• Segments, curves, and intersections along local systems were prioritized.

• Safety plans were completed for each of the counties, major cities, 

and Tribes.

The final measure of success for the LRSP will depend on implementation 

(which is just starting) and the results of a before versus after study that 

documents whether the safety investments along the local system bent the 

trend line and supported North Dakota’s vision of working towards zero 

traffic-related fatalities.
Systemic Safety Project Selection Tool Supplemental Case Studies 34 Conclusion

Conclusion

The systemic approach to safety involves widely implemented improvements 

identified to address high-risk roadway features correlated with severe injury 

crashes. This approach to safety is a complementary analytical technique 

intended to supplement the traditional site analysis approach and results in a 

more comprehensive safety management program.

The Systemic Safety Project Selection Tool (Systemic Tool) provides a stepby-step process for conducting systemic safety planning, considerations for 

determining a reasonable distribution between the implementation of spot 

safety improvements and systemic safety improvements, and a mechanism 

for quantifying the benefits of safety improvements implemented through a 

systemic approach. The two case studies examined in this supplement 

demonstrate how the systemic analysis process can be successfully applied 

in urban areas and along systems with little supporting data. 

Based on data produced as part of the risk evaluations performed for 

Greater Minnesota’s urban road system and local systems in the 

Minneapolis/St. Paul Metropolitan area, MnDOT was able to leverage the 

systemic analysis process to overcome challenges and obtain useful results. 

The systemic analysis process assisted in identifying focus crash and facility 

types, adopting a set of risk factors, creating a short-list of safety 

countermeasures, and identifying more than $13 million worth of focused 

safety projects at designated high-risk locations. 

NDDOT also implemented the systemic analysis process successfully, 

despite little supporting data, to produce a safety plan that benefits North 

Dakota’s counties, cities, Tribes, and the Theodore Roosevelt National Park. 

By leveraging the systemic analysis process, NDDOT was able to overcome 

data gaps and identify almost $55 million worth of safety projects focused on 

low-cost countermeasures. 
Systemic Safety Project Selection Tool Supplemental Case Studies 35 References

References

American Association of State Highway and Transportation (AASHTO). 

Highway Safety Manual. 2010. 

http://www.highwaysafetymanual.org/Pages/default.aspx. 

CH2M. City of Saint Paul Roadway Safety Plan. January 2016. Unpublished.

CH2M. Minnesota County Road Safety Plans. January 2015. Unpublished. 

CH2M. North Dakota Local Road Safety Plans Program Summary. April 

2015. Unpublished.

Federal Highway Administration (FHWA). 2014. Crash Modification Factor 

(CMF) Clearinghouse. Access date: October 2014. 

http://safety.fhwa.dot.gov/tools/crf/resources/. 

Federal Highway Administration (FHWA). 2013. Systemic Safety Project 

Selection Tool. http://safety.fhwa.dot.gov/systemic/fhwasa13019/. 

Federal Highway Administration (FHWA). 2009 Version with Revisions 

Numbers 1 and 2 incorporated, dated 2012. Manual on Uniform Traffic 

Control Devices.: http://mutcd.fhwa.dot.gov/. 

Minnesota Department of Transportation (MnDOT). 2007. Strategic Highway 

Safety Plan. http://www.dot.state.mn.us/trafficeng/safety/shsp/MinnesotaSHSP-2007.pdf. 

Minnesota Department of Transportation (MnDOT). 2014. Strategic Highway 

Safety Plan. 

http://www.dot.state.mn.us/trafficeng/safety/shsp/Minnesota_SHSP_2014.pdf. 

Minnesota Department of Transportation (MnDOT). Minnesota Traffic Safety 

Fundamentals Handbook. 2015. Unpublished.

National Cooperative Highway Research Program (NCHRP). NCHRP 

Report 500: Guidance for Implementation of the AASHTO Strategic Highway 

Safety Plan. The National Academies of Sciences, Engineering, Medicine.

2003-2009. http://www.trb.org/Main/Blurbs/152868.aspx. 

North Dakota Department of Transportation (NDDOT). Strategic Highway 

Safety Plan. September 2013.

https://www.dot.nd.gov/divisions/safety/docs/ND_SHSP_final_2013-09-

09.pdf. 
Systemic Safety Project Selection Tool Supplemental Case Studies A-1 Appendix A. Glossary

Appendix A.Glossary

AASHTO American Association of State Highway and Transportation Officials

ADT average daily traffic

CMF crash modification factor

FHWA Federal Highway Administration

HAWK High-intensity Activated crossWalk

HSIP Highway Safety Improvement Program

LRSP Local Road Safety Program

MnDOT Minnesota Department of Transportation 

NCHRP National Cooperative Highway Research Program

NDDOT North Dakota Department of Transportation 

SHSP Strategic Highway Safety Plan 
U.S. Department of Transportation

Federal Highway Administration (FHWA)

Office of Safety 

1200 New Jersey Avenue, SE

Washington, DC 20590

http://safety.fhwa.dot.gov

Publication No. FHWA-SA-17-002

December 2016

This material is based upon work supported by the FHWA under contract number DTFH61-10-D-00020. 

Any opinions, findings and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the FHWA.