http://safety.fhwa.dot.gov

FHWA Safety Program

Benefit-Cost Analysis of Investing in Data Systems 

and Processes for Data-Driven Safety Programs:

Decision-Making Guidebook
FINAL REPORT SAFETY DATA BCA GUIDEBOOK

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Notice

This document is disseminated under the sponsorship of the U.S. Department of 

Transportation in the interest of information exchange. The U.S. Government assumes no 

liability for the use of the information contained in this document.

The U.S. Government does not endorse products or manufacturers. Trademarks or 

manufacturers’ names appear in this report only because they are considered essential to the 

objective of the document.

Quality Assurance Statement

The Federal Highway Administration (FHWA) provides high-quality information to serve 

Government, industry, and the public in a manner that promotes public understanding. 

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FINAL REPORT SAFETY DATA BCA GUIDEBOOK

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TECHNICAL DOCUMENTATION PAGE

1. Report No.

FHWA-SA-12-030

2. Government Accession 

No.

3. Recipient's Catalog No.

4. Title and Subtitle

Benefit-Cost Analysis of Investing in Data Systems and Processes for DataDriven Safety Programs: Decision-Making Guidebook

5. Report Date

August 2012 

6. Performing Organization Code

7.Author(s)

Michael Lawrence, Devon Cartwright-Smith, Janine Mans, Paul Nguyen, Nancy 

Lefler

8. Performing Organization Report No.

9. Performing Organization Name and Address

Jack Faucett Associates

4550 Montgomery Avenue 

Suite 300N

Bethesda, MD 20814

Vanasse Hangen Brustlin, Inc.

8300 Boone Boulevard

Suite 700

Vienna, VA 22182

10. Work Unit No.

11. Contract or Grant No.

DTFH61-10-D-00022 (VHB)

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

Final Report, June 2011 - August 2012

14. Sponsoring Agency Code

FHWA

15. Supplementary Notes

The contract manager for this report was Dr. Heather Rothenberg. 

16. Abstract

The Federal Highway Administration (FHWA) Office of Safety commissioned this study to develop guidance on 

methodologies that State and local Departments of Transportation (DOTs) could implement to make the case for investing 

in data collection, data systems, and processes. This Guidebook is intended to assist States in justifying the decision to 

invest in additional data collection efforts related to safety. If a State is uncertain of the value of data collection, or if a 

State is having difficulty justifying the allocation of resources to data collection projects, this Guidebook provides 

instructions in how States may assess the potential impact of investment in safety data improvement.

17. Key Words: benefit cost analysis,

safety data, roadway inventory data, traffic data, 

data collection, crash data

18. Distribution Statement

No restrictions.

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this 

page) Unclassified

21. No. of Pages

33

22. Price

Form DOT F 1700.7 (8-72) Reproduction of completed pages authorized
FINAL REPORT SAFETY DATA BCA GUIDEBOOK

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FINAL REPORT SAFETY DATA BCA GUIDEBOOK

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ACRONYMS

B/C Benefit-Cost

BCA Benefit-Cost Analysis

DOT Department of Transportation

FARS Fatality Analysis Reporting System

FDE Fundamental Data Elements

FHWA Federal Highway Administration

FTE Full Time Equivalent

HPMS Highway Performance Monitoring System

HSIP Highway Safety Improvement Program

LRS Linear Referencing System

MAIS Maximum Abbreviated Injury Severity Score

MIRE Model Inventory of Roadway Elements

MPO Metropolitan Planning Organization

NHTSA National Highway Traffic Safety Administration

NPV Net Present Value

OMB Office of Management and Budget

VAC Value of Avoided Crashes
FINAL REPORT SAFETY DATA BCA GUIDEBOOK

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TABLE OF CONTENTS

1. INTRODUCTION TO THE GUIDEBOOK ..................................... 1

2. BENEFIT-COST ANALYSIS (BCA)................................................. 3

2.1.DEFINE THE PROPOSED INVESTMENT ..........................................................3

2.2.ESTABLISH FRAMEWORK FOR THE ANALYSIS ...........................................4

2.3.QUANTIFY THE BENEFITS.................................................................................5

2.3.1. Reduced Staff Time from More Efficient Project Identification ..............5

2.3.2. Excess Crash Savings from Faster Project Programming........................6

2.3.3. Excess Crash Savings from Improved Project Prioritization...................7

2.3.4. Reduced Staff Hours from Streamlined Evaluation ................................13

2.3.5. Annualize Benefits.......................................................................................14

2.4.QUANTIFY THE COSTS ....................................................................................15

2.4.1. Initial Investment Cost of Data Collection...............................................15

2.4.2. Operations and Maintenance Costs ..........................................................18

2.4.3. Cost for Locating and Coding Crashes on Non-Federal-Aid Roads ......20

2.4.4. Data Storage and Other Costs ..................................................................20

2.4.5. Annualize Costs...........................................................................................21

2.5.EVALUATING THE BENEFITS AND COSTS .................................................24

2.5.1. Convert into Common Units.....................................................................24

2.5.2. Calculate Present Values............................................................................24

2.5.3. Calculate Net Present Value and Benefit-Cost Ratio .............................27

2.5.4. Evaluate Risk................................................................................................27

2.5.5. Decision Rules..............................................................................................27

3. DATA AVAILABILITY AND ESTIMATION METHODS ............29

4. CHECKLIST...................................................................................... 31

REFERENCES ............................................................................................ 33
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LIST OF TABLES

Table 1. Reduced Staff Time in Project Identification. .................................................6

Table 2. Sample of Ranking Exercise...............................................................................9

Table 3. Crash Performance of Ranked List.................................................................10

Table 4. Comparison of Crash Reduction Potential of New and Old Bundles. ........11

Table 5. Sample of Base Case and Alternative Case Decisions on Safety 

Improvements..................................................................................................................12

Table 6. Value of Data in Decision Making for Entire System....................................13

Table 7. Example of Streamlined Evaluation Benefits from Safety Data Collection.

...........................................................................................................................................14

Table 8. Example of Annualization of Benefits (Thousands)......................................14

Table 9. Overall Benefits to the Average State (Thousands).....................................15

Table 10. Average Base Cost Estimate.........................................................................17

Table 11. Roadway Characteristics by State Size........................................................17

Table 12. Cost Estimates for Small, Large, and Average States (Thousands). ........18

Table 13. Average Annual Inventory Maintenance Costs (Thousands). ...................19

Table 14. Average Annual Cost to Locate Crashes.....................................................20

Table 15. Assumptions on Timing of Data Collection.................................................21

Table 16. Yearly Cost Estimates by State Size for Base Data Collection 

(Thousands)......................................................................................................................22

Table 17. Inventory Maintenance Costs (Thousands). ................................................23

Table 18. Cost to Locate Crashes (Dollars). ................................................................24

Table 19. Total Annual Benefits Prior to and After Discounting for an Average 

State (Thousands)............................................................................................................26
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Table 20. Total Annual Costs Prior to and After Discounting for an Average State 

(Thousands)......................................................................................................................26

Table 21. Net Present Value and Benefit-Cost Ratio..................................................27

Table 22. Potential Approaches to Developing Datasets for Analysis ......................30

LIST OF FIGURES

Figure 1. The Five Steps of a BCA...................................................................................3
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1. INTRODUCTION TO THE GUIDEBOOK

Deciding to invest in data is a challenge for many transportation agencies. State transportation 

agencies frequently face budget constraints and pressure to use their limited resources on 

more tangible projects than data and information collection. Data investments often compete 

for funding with safety improvement projects to the infrastructure. Infrastructure 

improvements are visible to the driving public and typically have immediate safety impacts; the 

impact of data investments may not be as obvious to the public. Investments in safety data, 

however, inform States’ decision-making process regarding which safety improvements can have 

the most impact, and where the improvements can be most effective.

The Federal Highway Administration (FHWA) Office of Safety commissioned this research to 

develop guidance on the methodologies that States can use to determine the benefits of 

investing in data, data systems, and processes for achieving a data-driven safety program. This is 

a crucial next step to help the FHWA Office of Safety achieve its goal of reducing highway 

fatalities by providing decision-makers the tools they need to make informed decisions through 

an evidenced-based approach to safety implementation.

The intent of the Guidebook is to assist State and local transportation agencies in justifying the 

decision to invest in additional data collection efforts related to safety. For example, if a State 

Department of Transportation (DOT) is having a difficult time justifying the allocation of 

resources to data collection projects, this Guidebook provides instructions on how agencies 

may assess the potential impact of investment in safety data improvement. Several sections of 

this Guidebook discuss and provide examples using segment data, though it should be 

understood that the methodologies described apply to the collection of ramp and intersection 

data as well.

This Guidebook is a companion to the FHWA final report Benefit-Cost Analysis of Investing in 

Data Systems and Processes for Data-Driven Safety Programs (1). The report includes further 

descriptions of the literature, project purpose, and the methodology that are shared in this 

Guidebook. Other related publications readers might also find useful include:

1. Lefler, N., et al., Background Report: Guidance for Roadway Safety Data to Support the 

Highway Safety Improvement Program, Federal Highway Administration, FHWA-SA-11-39, 

Washington, DC, 2011.

2. Lefler, N., et al., Market Analysis of Collecting Fundamental Roadway Data Elements to 

Support the Highway Safety Improvement Program, Federal Highway Administration, 

FHWA-SA-11-40, Washington, D.C., 2011.

3. Li, Z., et al., Project-Level Life-Cycle Benefit-Cost Analysis Approach for Evaluating Highway 

Segment Safety Hardware Improvements, Transportation Research Record Vol. 2160, 

Washington, DC, 2010.
FINAL REPORT SAFETY DATA BCA GUIDEBOOK

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A full list of references is provided in the final report referenced above. Additional information 

can also be found on the FHWA Office of Safety web page at http://safty.fhwa.dot.gov.

The remainder of this Guidebook is organized into the following sections:

• Section 2 details the benefit-cost analysis (BCA) methodology developed for assessing 

the investments in data collection and discusses the application of the methods for a 

hypothetical safety analysis example.

• Section 3 addresses the lack of data availability and provides suggestions for States that 

do not have sufficient levels of data to undertake this analysis.

• Section 4 presents a checklist of the data requirements for each step of the analysis.
FINAL REPORT SAFETY DATA BCA GUIDEBOOK

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2. BENEFIT-COST ANALYSIS (BCA)

BCA is a process used to assess the economic viability of projects. In a BCA, an analyst 

estimates the total benefits and costs of a project, compares total benefits with total costs, and 

recommends the implementation of the project if benefits exceed the costs, often by an 

established ratio, such as 2:1. FHWA’s Economic Analysis Primer outlines the basic steps to BCA, 

which serves as the general framework for this guide (2). Figure 1 shows the five steps that 

constitute a BCA.

Figure 1. The Five Steps of a BCA.

Agencies can apply this standard approach to almost any significant investment project. The 

remainder of this Guidebook describes how States can apply this approach specifically to data 

collection projects associated with roadway safety.

2.1. DEFINE THE PROPOSED INVESTMENT

The analyst will first need to define the objectives of the project. Definition of the 

objective of the data collection project will help determine the scope of activity, and therefore 

the benefits and costs of the project. For example, one project might have an objective of 

collecting intersection data, while another might focus on ramp data. Determining the clear 

objective of the project will help limit the analysis to only those projects that most closely 

target the objectives sought.

It is also important to identify any constraints and assumptions that might affect what 

alternatives could be accepted. For example, an agency may wish to perform a pilot data 

collection effort prior to implementation on a larger scale. Alternatively, an agency may have 

1. Define 

scenario(s) 

for analysis

2. Establish 

the 

framework 

for analysis

3. Analyze 

the effects

4. Estimate 

the benefits 

and costs

5. Evaluate and 

make 

recommendations
FINAL REPORT SAFETY DATA BCA GUIDEBOOK

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made the strategic decision to focus its data collection primarily on urban and Federal-aid 

roads, rather than on local roads.

On the basis of the defined project objectives and constraints, the analyst will define both a 

status quo case (base case), as well as one or more alternatives. The base case 

represents the continued operation of the current facility under good management practices 

but without major investments in data collection. As a central part of the BCA, the analyst will 

compare the projected benefits and costs of alternative projects to those of the 

base case.

2.2. ESTABLISH FRAMEWORK FOR THE ANALYSIS

Once the analyst defines the investment alternatives and base case and identifies the objectives 

and constraints on the facility, they will also need to establish the overall framework for 

the analysis. This includes the project life, or the number of years over which the benefits 

and costs of the project will be evaluated, and the appropriate rate at which to discount future 

streams of benefits and costs.

Setting these boundaries on the analysis provides a framework for quantifying the benefits and 

costs for comparable projects and alternatives. Defining the life of the safety data 

collection program allows the analyst to distribute initial investments over the expected life 

of the investment, and to compare the stream of benefits and costs over the same period. For 

example, a life-cycle of 20 years may include a 10-year roll out period for data collection and an 

additional 10 years of implementation. It is important to note that the lifecycle of the data 

collection effort must include the period of the data collection effort itself (where costs will 

likely far outweigh any benefits from the effort), as well as the period of implementation of the 

data (where benefits from having the data will likely far outweigh the costs of any database 

maintenance). The project life of data collection programs may differ somewhat between 

individual States, depending on the specifics of the program.

The analyst will need to discount future costs and future benefit streams within the 

life of the project to the current year using a common discount rate. If a State DOT 

has a specific discount rate that is used to evaluate benefits and costs for other roadway 

investment projects, the analyst may choose to use this discount rate. If the State DOT does 

not have an established BCA discount rate, they may choose to adopt the 7.0% discount rate 

used in the FHWA Market Analysis of Collecting Fundamental Roadway Data Elements, which is 

based on the Office of Management and Budget (OMB) guidance Circular A-94 (3).
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2.3. QUANTIFY THE BENEFITS

Once the major categories of benefits and costs are defined, the analyst will calculate total 

benefits and costs of the project for each year of the project life. If the benefits and costs are 

monetary in nature, such as the number of hours spent on the collection at a certain hourly 

wage, the calculations will be relatively straightforward. If the benefits and costs are not 

monetary in nature, such as the number of fatal crashes avoided, the analyst will have to make 

certain assumptions on the calculations to monetize the benefits and costs. The following 

sections provide a suggested approach for calculating benefits in the following categories:

1. More efficient project identification.

2. Faster project programming.

3. Improved project prioritization.

4. Streamlined evaluation.

2.3.1. Reduced Staff Time from More Efficient Project Identification

Without a data collection system in place, State DOT staff may undertake time-consuming,

indirect estimation techniques to derive information in lieu of having actual data. Multiple 

offices within the same agency that use the same, or similar, data elements may be duplicating 

efforts due to the lack of an officially accepted dataset. One of the potential benefits of data 

collection during the project identification stage of the safety improvement process is a 

reduction in the State DOT staff time needed to identify potential sites due to the 

existence of the database.

To determine the amount of State DOT staff time that could potentially be saved, the analyst 

will identify and query staff in relevant offices to determine how many hours are spent in 

project identification by each department dealing with the lack of sufficient data. The analyst 

will then define how many hours of full time equivalent (FTE) labor would be saved 

annually by each staff member in the other State DOT offices if the data elements were 

obtained through the proposed data collection effort. The analyst will then identify different 

labor categories and assign an hourly rate by department, or an average hourly 

rate, to this saved labor. Different offices might apply a common hourly rate, or may choose 

different hourly rates. Table 1 provides an example of business process savings from data 

collection for a fictitious State DOT. Section 2.3.5 will discuss the annualization of these 

benefits.
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Table 1. Reduced Staff Time in Project Identification.

Department

Number of 

annual 

FTE Staff 

Hours 

Saved

Hourly 

Rate

Total 

Annual 

Savings

Safety Investment Planning 480 $95 $45,600

Highway Investment Planning 960 $95 $91,200

Asset Management 80 $95 $7,600

Traffic Engineering 80 $120 $9,600

Pavement Engineering 80 $120 $9,600

MPO and Local Planners 160 $95 $15,200

County Engineers 960 $95 $91,200

Emergency Response 160 $50 $8,000

Total 2,960 $278,000

2.3.2. Excess Crash Savings from Faster Project Programming

In addition to the change in the number of staff hours required to undertake the project 

identification process, additional data collection will reduce the lag between the beginning of a 

safety improvement initiative and the actual implementation of safety improvements on a State’s 

roadways. The Highway Safety Improvement Program (HSIP) decision-making process involves 

identifying the problem sites, selecting appropriate countermeasures, prioritizing projects, 

programming the funding, and completing construction. The lack of a data collection system 

and readily available data creates a lag in this process. For this analysis, excess crashes are 

defined as the incidents that may not have occurred if safety projects were programmed more 

quickly than traditional time horizons, due to the availability of data to expedite decision-making 

processes.

To develop the excess crash-savings estimate, the analyst must first identify candidate sites 

where safety improvements have been implemented. Candidate sites may include 

roadway segments, ramps, or intersections. The analyst will determine the length of time 

from the identification of a safety problem location to the completion of 

construction of the improvement projects, and where the State needed to collect data for 

these projects. The analyst will also compile before and after crash data for these 

sample sites. Data for two to three years before and after the project completion is 

required. This exercise will provide before and after reference points.

If the State has not collected data at these sites, the analyst will need to collaborate with DOT 

staff to develop an assumption of how much earlier, if any, programming decisions could have 
FINAL REPORT SAFETY DATA BCA GUIDEBOOK

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been made if the data elements were available. The analyst will apply these assumptions to the 

sample sites to calculate the number of crashes that, in theory, occurred as a result of the lag 

time. The following equation demonstrates this approach:

Cij = L * (M0 – M1)ij

Where:

Cij = Excess Crashes 

i = Crash severity by Maximum Abbreviated Injury Severity (MAIS) Score

j = Infrastructure category (intersection, road segment, ramp)

L= Reduction in months of lag

M0 = Average monthly crashes prior to improvement

M1= Average monthly crashes after improvement

The analyst will need to calculate the excess crash estimates by crash severity, 

according to the MAIS score: 0-not injured, 1-minor, 2-moderate, 3-serious, 4-severe, 5-

critical, and 6-maximum (fatal).

Using control data from sites with similar improvements, the analyst will scale down the 

total number of crashes so that only a proportion will be assumed as excess 

crashes. The analyst will then look at the past several years of highway safety improvements 

and estimate the average number of roadway segment, intersection, and ramp 

improvements per year. On this basis, the analyst can estimate the system-wide excess 

crashes.

Finally, the analyst will monetize the costs of these excess crashes using the National Highway 

Traffic Safety Administration (NHTSA) comprehensive cash cost estimates (4). The resulting 

dollar values may then be considered the value of accelerated safety analysis and project 

programming resulting from data system investments.

2.3.3. Excess Crash Savings from Improved Project Prioritization

To assess the benefits of the availability of additional data for improved project prioritization, 

the analyst will need to conduct two sample evaluations of roadway safety 

improvement candidate locations, one with the additional data included in the safety data 

collection project and one without this additional data.

To do this, the agency will need to collect all data elements for a subset of roadways which 

have been involved in a safety improvement project at least two to three years ago. This could 

be a sample of roadways from the State’s own roadway system that are comparable to the total 

set of roadways for which data will be collected. The agency may gather the data through 

alternative means (such as Google Earth) on a sample of roadway segments, intersections, and 
FINAL REPORT SAFETY DATA BCA GUIDEBOOK

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ramps in order to undertake the analysis. Alternatively, if the agency has not yet collected or 

does not yet collect the relevant data elements, they may seek data collected by another agency 

to conduct the analysis. The analyst will then identify the cost of the safety 

improvements that were undertaken at each of these sites.

The analyst will then apply the method for safety improvement candidate 

prioritization and selection that has been used in the past, without the assistance of the 

additional data elements. The ranking of sites should be based on the crash records and road 

characteristics prior to the safety improvement. The application of the method will result in a 

ranking of sites.

The analyst will then conduct a second ranking exercise, using the new method for 

safety improvement candidate prioritization and selection that incorporates the 

additional data. Again, the ranking of sites should be based on the crash record and road 

characteristics prior to the safety improvement. This may include a variation of one of the 

previously mentioned methods, or the use of a specialized tool, such as SafetyAnalyst.

Depending on the scope of the evaluation, the analyst will compile a table with information 

for the top 20 to 50 sites in both ranking exercises. This table should include the 

following information:

• Average annual frequency of road crashes on each segment by crash category prior to 

the safety improvement (with at least two to three years of data).

• Average annual frequency of road crashes on each segment by crash category after the 

safety improvement (with at least two to three years of data).

• Cost of the safety improvement.

• Average cost of crashes by type. This report uses national values for cost of crashes;

however, if an agency has values specific to their State or jurisdiction, they should use 

those values.

Using the two rankings, the analyst will set a fixed budget for safety improvements, for 

example $15 million. The budget should be proportional to the size of the sample of roadways 

used. For example, if the sample would be 1⁄4 of the size of the list normally examined for 

roadway safety improvement planning, then the budget should also be about 1⁄4 of the annual 

budget available for safety improvement. Working from the highest to the lowest ranked 

project on the old ranking list, the analyst will add projects until the budget threshold 

has been reached. This will be the bundle of interventions selected using the old data and 

methods, referred to as the “old bundle”. Again, working from the highest to the lowest 

ranked project on the new ranking list, the analyst will add projects until the budget 

threshold has been reached. This will be the bundle of interventions selected using the new 

data and methods, referred to as the “new bundle”. Table 2 shows an example of this creation 
FINAL REPORT SAFETY DATA BCA GUIDEBOOK

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of the new and old bundles. The highlighted cells show the improvements that have been 

selected under the two ranking methods.

Table 2. Sample of Ranking Exercise.

Road 

Segment 

Number

Ranking

Old Method

Ranking

New Method

Cost of Safety 

Improvement

1 6 4 $500,000

2 17 13 $400,000

3 13 14 $475,000

4 11 11 $400,000

5 2 1 $1,250,000

6 14 17 $450,000

7 4 5 $1,750,000

8 9 2 $200,000

9 16 3 $200,000

10 7 6 $1,950,000

11 19 18 $500,000

12 20 19 $15,000

13 18 20 $450,000

14 5 7 $1,000,000

15 12 15 $500,000

16 1 8 $3,000,000

17 8 10 $1,750,000

18 3 9 $3,000,000

19 15 12 $400,000

20 10 16 $600,000

For the old bundle and the new bundle, the analyst will calculate the total crash

reductions by category associated with these bundles, and assign a monetary value 

to this crash reduction, based on the accepted value of crash by type.

Table 3 shows a list of all the ranked road segments with information on the change in average 

annual crashes associated with an improvement to that road segment.
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Table 3. Crash Performance of Ranked List.

Road 

Seg. 

No.

Ranking 

Old 

Method

Ranking 

New 

Method

Average Annual 

Crashes 2-3 Yrs 

Before Improvement

Average Annual 

Crashes 2-3 Yrs 

After Improvement

Change in Average 

Annual Crashes

MAIS 

0-1

MAIS 

2-4

MAIS 

5-6

MAIS 

0-1

MAIS 

2-4

MAIS 

5-6

MAIS 

0-1

MAIS 

2-4

MAIS 

5-6

A B C D E F G H I J = 

G – D

K = 

H – E

L = 

I – F

1 6 4 3 8 0 1 6 0 -2 -2 0

2 17 13 3 7 0 1 6 0 -2 -1 0

3 13 14 3 7 0 1 6 0 -2 -1 0

4 11 11 6 10 0 3 9 0 -3 -1 0

5 2 1 0 8 6 0 7 5 0 -1 -1

6 14 17 3 7 0 1 6 0 -2 -1 0

7 4 5 16 8 0 7 2 0 -9 -6 0

8 9 2 3 7 0 0 6 0 -3 -1 0

9 16 3 2 7 0 2 6 0 0 -1 0

10 7 6 19 3 4 21 5 3 2 2 -1

11 19 18 20 7 0 19 6 0 -1 -1 0

12 20 19 3 5 0 2 5 0 -1 0 0

13 18 20 20 7 0 19 6 0 -1 -1 0

14 5 7 3 10 0 1 6 0 -2 -4 0

15 12 15 3 7 0 1 6 0 -2 -1 0

16 1 8 2 5 4 2 1 3 0 -4 -1

17 8 10 3 5 2 2 8 1 -1 3 -1

18 3 9 11 5 4 11 4 3 0 -1 -1

19 15 12 3 7 0 1 6 0 -2 -1 0

20 10 16 7 8 0 0 7 0 -7 -1 0

Table 4 aggregates the total crash reduction potential by crash type associated with the new 

and old bundles of safety improvements. The old bundle is associated with an average annual 

reduction of 19 MAIS category 0-1 crashes, compared with a reduction of 17 crashes by the 

new bundle. In contrast, the new bundle is more effective in reducing moderate crashes. The 

new bundle is associated with a reduction of 20 MAIS 2-4 level crashes compared with a 

reduction of 16 by the old bundle, and a reduction of 8 MAIS 5-6 crashes for the new bundle,

compared with 5 for the old bundle. These numbers will be referred to as the marginal 
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change in excess crashes. The number of excess crashes is then multiplied by the cost 

of the relevant crash severity to determine the monetary value of the crash 

reductions associated with that bundle. As shown in Table 4, the old bundle is associated 

with total value of avoided crashes (VAC) worth $37,720,318, while the new bundle is 

associated with avoided crashes worth $56,309,275.

Table 4. Comparison of Crash Reduction Potential of New and Old Bundles.

Total Avoided 

Crashes by Severity

Value of Avoided Crash

(VAC) by Severity

MAIS 

0-1

MAIS 

2-4

MAIS 

5-6

MAIS 

0-1

MAIS 

2-4

MAIS 

5-6

Total Value of 

Additional Avoided 

Crashes

Old Method 19 16 5 $12,811 $691,351 $5,283,059 $37,720,318

New Method 17 20 8 $12,811 $691,351 $5,283,059 $56,309,275

Difference -2 4 3 --- --- --- $18,588,958

Source: Provided by NHTSA.

The difference between the VAC of the old and new bundles is the value received from the 

having collected and used the new information for making safety decisions on an annual basis. 

In this example, the value of data in improving roadway safety via improved decision-making is 

about $19 million for the sample of roadways. Table 5, on the following page, provides the 

entire ranking and valuation process in a single table. Note that in this example, because the 

total amount spent on roadway improvements at candidate locations did not change from the 

old bundle to the new bundle, no additional costs were incurred. If an agency identifies a 

bundle of candidate sites that have a higher VAC at either a higher or lower total cost for the 

bundle, the expenditure by the State DOT to achieve the improvements on these candidate 

sites may be increased or decreased, respectively. To justify an increased expenditure on the 

overall bundle, the analyst should ensure that the benefits (total VAC) accrued by the new 

bundle exceed the additional costs of the new bundle.
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Table 5. Sample of Base Case and Alternative Case Decisions on Safety Improvements.

Seg. 

No.

Old 

Rank 

New 

Rank

Cost of Safety 

Improvement

Average Annual Crashes

2-3 Yrs before 

Improvement

Average Annual Crashes 

2-3 Yrs After 

Improvement

Change in Average 

Annual Crash Value of Reduction per Crash Type Total Value 

of Crash

Reductions 

Value/Cost 

Ratio MAIS 

0-1

MAIS 

2-4

MAIS 

5-6

MAIS 

0-1

MAIS 

2-4

MAIS 

5-6

MAIS 

0-1

MAIS 

2-4

MAIS 

5-6

MAIS 0-

1 MAIS 2-4 MAIS 5-6

A B C D E F G H I J K = 

H - E

L = 

I - F

M = 

J - G

N = K * 

VAC1

O = L * 

VAC2

P = M * 

VAC3 Q = N+O+P

1 6 4 $500,000 3 8 0 1 6 0 -2 -2 0 -$25,622 -$1,382,702 $0 -$1,408,324 -2.82

2 17 13 $400,000 3 7 0 1 6 0 -2 -1 0 -$25,622 -$691,351 $0 -$716,973 -1.79

3 13 14 $475,000 3 7 0 1 6 0 -2 -1 0 -$25,622 -$691,351 $0 -$716,973 -1.51

4 11 11 $400,000 5 8 1 3 5 0 -2 -3 -1 -$25,622 -$2,074,053 -$5,283,059 -$7,382,734 -18.46

5 2 1 $1,250,000 0 8 6 0 7 5 0 -1 -1 $0 -$691,351 -$5,283,059 -$5,974,410 -4.78

6 14 17 $450,000 3 7 0 1 6 0 -2 -1 0 -$25,622 -$691,351 $0 -$716,973 -1.59

7 4 5 $1,750,000 16 8 0 7 2 0 -9 -6 0 -$115,299 -$4,148,106 $0 -$4,263,405 -2.44

8 9 2 $200,000 3 7 0 0 6 0 -3 -1 0 -$38,433 -$691,351 $0 -$729,784 -3.65

9 16 3 $200,000 0 8 2 2 6 0 2 -2 -2 $25,622 -$1,382,702 -$10,566,117 -$11,923,197 -59.62

10 7 6 $1,950,000 19 3 4 19 4 3 0 1 -1 $0 $691,351 -$5,283,059 -$4,591,708 -2.35

11 19 18 $500,000 20 7 0 19 6 0 -1 -1 0 -$12,811 -$691,351 $0 -$704,162 -1.41

12 20 19 $15,000 3 5 0 2 5 0 -1 0 0 -$12,811 $0 $0 -$12,811 -0.85

13 18 20 $450,000 20 7 0 19 6 0 -1 -1 0 -$12,811 -$691,351 $0 -$704,162 -1.56

14 5 7 $1,000,000 3 10 0 1 6 0 -2 -4 0 -$25,622 -$2,765,404 $0 -$2,791,026 -2.79

15 12 15 $500,000 3 7 0 1 6 0 -2 -1 0 -$25,622 -$691,351 $0 -$716,973 -1.43

16 1 8 $3,000,000 2 5 4 2 1 3 0 -4 -1 $0 -$2,765,404 -$5,283,059 -$8,048,463 -2.68

17 8 10 $1,750,000 3 5 2 2 8 1 -1 3 -1 -$12,811 $2,074,053 -$5,283,059 -$3,221,817 -1.84

18 3 9 $3,000,000 11 5 4 11 4 3 0 -1 -1 $0 -$691,351 -$5,283,059 -$5,974,410 -1.99

19 15 12 $400,000 3 7 0 1 6 0 -2 -1 0 -$25,622 -$691,351 $0 -$716,973 -1.79

20 10 16 $600,000 7 8 0 5 7 0 -2 -1 0 -$25,622 -$691,351 $0 -$716,973 -1.19

Key for VAC on Table 5:

VAC by Severity

MAIS 0-1

VAC1

MAIS 2-4

VAC2

MAIS 5-6

VAC3

$12,811 $691,351 $5,283,059

Source: NHTSA (MAIS values not yet published) 

Sum of total value of crash reductions for Old Rank, Segments 1, 5, 7, 8, 10, 14, 16, 17, 18, 20 = -$37,720,318 (negative sign indicates savings from 

crashes avoided, though benefits from this calculation will be incorporated as a positive figure when cumulating overall benefits).

Sum of total value of crash reductions for New Rank, Segments 1, 4, 5, 7, 8, 9, 10, 14, 16, 17, 18 = -$56,309,275.

Total Cost of Safety Improvements, Old and New Bundles: $15,000,000.
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The analyst will also need to expand the value of the sample to represent the total 

value for the entire roadway system. This expansion factor will be the same factor used 

to determine the budget size for the ranking exercise. In this example, the sample used in the 

ranking exercise is 1⁄4 of the size of the list normally examined for roadway safety improvement 

planning. Therefore, the expansion factor for the benefits is four. Table 6 demonstrates the 

application of the expansion factor to the annual benefit estimate from the sample.

Table 6. Value of Data in Decision Making for Entire System.

Method

Total Value of Crash 

Reduction of Sample

Expansion 

Factor

Estimated Total Crash 

Reduction for System

Old $37,720,318 4 $150,881,270

New $56,309,275 4 $225,237,100

Difference $18,588,958 --- $74,355,830

2.3.4. Reduced Staff Hours from Streamlined Evaluation

As with project identification, the absence of comprehensive and reliable data may result in staff 

undertaking time-consuming, indirect estimations of the information needed to evaluate safety 

improvement projects. These inefficiencies are not limited to the safety investment planning 

office of the State DOT, but may also occur in other offices that use the same, or similar, data 

elements.

Having spoken to staff in relevant offices to see how they handle the lack of the targeted data 

elements, the analyst should define how many yearly hours of full time equivalent labor 

would be saved in the evaluation of the safety improvement process, and assign an 

average hourly rate to this saved labor. Different offices might apply a common hourly 

rate, or may choose different hourly rates. Table 7 shows an example savings from streamlined 

evaluation for a fictitious State DOT.
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Table 7. Example of Streamlined Evaluation Benefits from Safety Data Collection.

Department

Number of 

Annual FTE Staff 

Hours Saved

Hourly 

Rate

Total 

Annual 

Savings

Safety Investment Planning 160 $95 $15,200 

Highway Investment Planning 280 $95 $26,600 

Asset Management 80 $95 $7,600 

Traffic Engineering 80 $120 $9,600 

Pavement Engineering 80 $120 $9,600 

MPO and Local Planners 80 $95 $7,600 

County Engineers 280 $95 $26,600 

Emergency Response 80 $50 $4,000 

Total 1,120 --- $106,800 

2.3.5. Annualize Benefits

The analyst will need to apply the estimated annual values for each category of 

benefits to every year in the project period. In this example, the analyst will assume a 

ramping-up period of seven years to allow for the gradual implementation of the data collection 

program. In the first year of data collection, 1/7th of the estimated $278,000 benefit from 

business process savings in project identification will be recorded, or about $40,000. (Table 

1identified a total of $278,000 in savings in reduced staff time in project identification from 

safety data collection.) For the second year, 2/7 of the estimated $278,000 benefit will be 

recorded, and so on until the seventh year, when the initial data collection process is complete. 

From this year onward, the analyst will assume that the annual benefit will remain constant at 

$278,000. An individual agency might choose a different method to phase in benefits, and in 

some cases may make the assumption that benefits do not accrue for this category until the 

data collection phase is complete. Table 8 provides an example of the annualized benefit for 

efficiency savings in project identification, based on the seven year ramp-up period. Table 9

summarizes the benefits to the Average State from all of the categories identified.

Table 8. Example of Annualization of Benefits (Thousands).

Year: 1 2 3 4 5 6 7 8 9 10

More Efficient Project 

Identification Savings --- $39.7 $79.4 $119.1 $158.9 $198.6 $238.3 $278.0 $278.0 $278.0

Year: 11 12 13 14 15 16 17 18 19 20

More Efficient Project 

Identification Savings $278.0 $278.0 $278.0 $278.0 $278.0 $278.0 $278.0 $278.0 $278.0 $278.0
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Table 9. Overall Benefits to the Average State (Thousands).

Year: 1 2 3 4 5 6 7 8 9 10

Elements 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Annual Business 

Process Savings --- $39.7 $79.4 $119.1 $158.9 $198.6 $238.3 $278.0 $278.0 $278.0

Annual Decision 

Making Savings --- $10,622.3 $21,244.5 $31,866.8 $42,489.0 $53,111.3 $63,733.6 $74,355.8 $74,355.8 $74,355.8

Annual Streamlined 

Evaluation Savings --- $15.3 $30.5 $45.8 $61.0 $76.3 $91.5 $106.8 $106.8 $106.8

Total Annual 

Benefits --- $10,677.2 $21,354.5 $32,031.7 $42,708.9 $53,386.2 $64,063.4 $74,740.6 $74,740.6 $74,740.6

Year: 11 12 13 14 15 16 17 18 19 20

Elements 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Annual Business 

Process Savings $278.0 $278.0 $278.0 $278.0 $278.0 $278.0 $278.0 $278.0 $278.0 $278.0

Annual Decision 

Making Savings $74,355.8 $74,355.8 $74,355.8 $74,355.8 $74,355.8 $74,355.8 $74,355.8 $74,355.8 $74,355.8 $74,355.8

Annual Streamlined 

Evaluation Savings $106.8 $106.8 $106.8 $106.8 $106.8 $106.8 $106.8 $106.8 $106.8 $106.8

Total Annual 

Benefits $74,740.6 $74,740.6 $74,740.6 $74,740.6 $74,740.6 $74,740.6 $74,740.6 $74,740.6 $74,740.6 $74,740.6

2.4. QUANTIFY THE COSTS

The cost estimations developed in this report calculate the additional costs that States would 

incur in order to gather the data that are not already collected through the Highway 

Performance Monitoring System (HPMS), or other efforts. This example assumes that all data 

collection beyond HPMS requirements would be new collection. An individual State’s cost 

estimates will vary by the circumstances in each State. This section guides the analyst in 

quantifying the following categories of costs:

1. Investment costs.

2. Operations and maintenance costs.

3. Cost for locating and coding crashes.

4. Data storage and other costs.

2.4.1. Initial Investment Cost of Data Collection

To establish the initial investment, or base cost, of data collection, the analyst should collect 

cost estimates for the collection of roadway, intersection, ramp, and linear 

referencing system data elements. These costs should incorporate data collection, data 

reduction, and integration into a State’s existing system.
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The Fundamental Data Elements (FDE), a subset of 38 elements from FHWA’s Model Inventory 

or Roadway Elements (MIRE), was released to States in 2011 through an FHWA Office of 

Safety Guidance Memo and supplemental report, Background Report: Guidance for Roadway Safety 

Data to Support the Highway Safety Improvement Program (5). This report also provided 

information on the estimated cost of collecting these elements, for three types of collection 

efforts:

1. Condensed FDE List: Cost of collecting data on Federal-aid roadways for the 22 

FDE that are not required under HPMS. Additional costs would only be incurred on 

Federal-aid roadways since 16 of the 38 total FDE are already required for HPMS on 

Federal-aid highways.

2. Linear Referencing System (LRS): Cost of collecting data on non-Federal-aid 

roadways for a common relational LRS. Additional costs would only be incurred on 

all non-Federal-aid roadways since HPMS currently requires this for Federal-aid 

roadways.

3. Full FDE List: Cost of collecting data on non-Federal-aid roadways for the 

complete 38 FDE. Additional costs would be incurred on all non-Federal-aid 

roadways since HPMS does not require data collection of these elements on nonFederal-aid roadways.

In addition to the inventory of FDE that describe the State’s roadway infrastructure, 

States will wish to collect traffic data, which is far more time consuming, and therefore 

costly, on a per mile, intersection, or ramp basis. The average base costs of data collection of 

the Condensed FDE, LRS, and Full FDE are provided in Table 10. This table identifies the 

separate costs of collecting the FDE and traffic data for segments, intersections, and ramps. A 

State may use these estimates as a base for building out the total cost of their program, or may 

choose to use more specialized estimates of the base costs of establishing, or expanding, their 

data collection programs.
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Table 10. Average Base Cost Estimate (5).

Segments

(Per Mile)

Intersections 

(Each)

Ramps

(Each)

Condensed FDE List

Inventory Elements $60 $130 $100

Traffic Data -- $590 $400

Condensed List - Total $60 $720 $500

Location Referencing System 

Total $40 -- --

Full FDE List

Inventory Elements $70 $130 $100

Traffic Data* $460 $590 $400

Full List - Total $530 $720 $500

*Assuming one traffic count per mile.

Segments - when the same range in given for the Comprehensive List and the Condensed List, take 

the high point for Comprehensive and the mid-point for Condensed.

Intersections/Ramps - when the same range in given for the Comprehensive List and the 

Condensed List - take the mid-point for both, since the midpoints do not change between the two.

In addition to the segment, intersection, ramp, and LRS cost estimates, States may wish to 

determine the cost of a data collection effort for an entire State. Table 11 provides 

roadway characteristics for three types of States: a small State (i.e., Rhode Island), a large 

State (i.e., California), and an “average” State.

Table 11. Roadway Characteristics by State Size (5).

State 

Segments (miles) Intersections (each) Ramps (each)

FederalaidNonFederal-aid FederalaidNonFederal-aid FederalaidNonFederal-aid 

Small 1,750 4,600 27,560 72,440 380 0

Large 55,230 103,490 132,370 248,030 14,660 0

Average 19,430 57,390 70,430 208,020 4,450 0

Using the average base cost estimates and the roadway characteristics, the analyst may 

estimate total costs for the collection of the remaining 22 elements not already collected for 

HPMS on Federal-aid roadways, as well as the collection of the complete list of 38 FDE 

elements, and implementation of an LRS on non-Federal-aid roadways. Table 12 provides the 

base costs estimates for the collection of this data for the small, large, and average State 

models.
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Table 12. Cost Estimates for Small, Large, and Average States (Thousands).

State

Federal-aid Non-Federal-aid Total 

Cost LRS Segments Intersections Ramps LRS Segments Intersections Ramps 

Small N/A $105.0 $19,843.2 $190.0 $184.0 $2,438.0 $52,156.8 $0 $74,917.0

Large N/A $3,313.8 $95,306.4 $7,330.0 $4,139.6 $54,849.7 $178,581.6 $0 $343,521.1

Average N/A $582.9 $50,709.6 $2,225.0 $1,147.8 $15,208.4 $149,774.4 $0 $219,648.1

2.4.2. Operations and Maintenance Costs

In addition to the costs of initial data collection, the analyst should calculate the costs to 

maintain the data. This includes costs to update the data as conditions change. The 

frequency of updating this data may differ between States, and between categories of data, 

based on how quickly and dramatically conditions in the State change. In each calculation, the 

analyst must first determine the manner and frequency of data updates.

For roadway segments, suppose five percent of the roadway mileage will be updated 

annually, and updating the inventory takes two hours per mile by an employee earning $20 

an hour (approximately $40,000 per year). Based on these assumptions, the equation to 

determine the annual cost of operation and maintenance for roadway segment data is:

Annual Operation and Maintenance Cost for Roadway Segments =

Total Miles * 5%*Maintenance Cost per Mile

Maintenance Cost per Mile = $20 * 2 hours = $40

For intersection data, suppose the inventory will be updated on a three-year cycle for 

signalized intersections and a five-year cycle for unsignalized intersections. This 

assumes that traffic volumes will not change dramatically at unsignalized intersections. The total 

number of intersections for the small, large, and average State is known. A split of 20 percent 

of signalized intersections and 80 percent of unsignalized intersections was used for the 

calculations in Table 12. The cost of the update of the intersection inventory is assumed to be 

the same as a one-mile roadway segment, $40, or two hours by an employee earning $20 

an hour.

Annual Operation and Maintenance Cost for Intersections =

(Total Signalized Intersections*1/3*Maintenance Cost per Intersection) +

(Total Unsignalized Intersections*1/5* Maintenance Cost per Intersection)

Maintenance Cost per Intersection = $20 * 2 hours = $40
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Individual State DOTs may choose to apply a different time or cost estimation, based on their 

experience.

For ramp data, in this example, suppose ramp inventory will be updated on a six-year cycle, 

with counts and inventory updates collected on one-sixth of the ramps per year. The cost of 

the update of the intersection inventory is assumed to be the same as for roadway segments 

and intersections, $40, or two hours by an employee earning $20 an hour.

Annual Operation and Maintenance Cost for Ramps =

Total Ramps*(1/6)* Maintenance Cost per Ramp

Maintenance Cost per Ramp = $20 * 2 = $40

For the LRS, the analyst will apply a similar approach as used on the other types of 

infrastructure. In this case, however, as an LRS is required for HPMS on Federal-aid roads, the 

analyst will only calculate the cost of the maintenance of the LRS on non-Federal-aid 

roads. As with segment data, it is assumed that five percent of the roadway mileage would 

be updated annually, and updating the LRS would take two hours per mile by an employee 

earning $20 an hour. The equation to determine the annual cost of operation and 

maintenance for the LRS is:

Annual Operation and Maintenance Cost for LRS =

Total Miles of non-Federal-aid roadways * 5% * Maintenance Cost per Mile

Maintenance Cost per Mile = $20 * 2 = $40

These equations provide a guide for analysis. Individual State DOTs may choose to apply 

a different time or cost estimation, based on their experience.

Using the cost figures provided in Table 10 through Table 12, and the equations and 

assumptions in this section, the analyst can estimate the annual operations and maintenance 

costs, as demonstrated in Table 13.

Table 13. Average Annual Inventory Maintenance Costs (Thousands).

Elements Small State Large State Average State

LRS $9.2 $207.0 $57.4

Segments $12.7 $317.4 $76.8

Intersections $13,573.3 $51,633.0 $21,782.6

Ramps $17.6 $677.6 $204.7
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2.4.3. Cost for Locating and Coding Crashes on Non-Federal-Aid Roads

The analyst can calculate the costs for locating and coding crashes on non-Federal-aid roads 

using the costs and roadway characteristics provided in Table 10 and Table 11, and a similar 

formula to those used in the previous section. For this example, assume that five crashes can

be coded per hour at a labor cost of $20/hour. These costs only pertain to the costs of 

coding and locating fatal and injury crashes. The number of property damage-only crashes on 

non-Federal-aid roads could be not reasonably estimated and are not included here.

Annual Cost for Locating and Coding Crashes = (Total Annual Injury and Fatal Crashes Not 

Automatically Located / 5) * 20

According to the Fatality Analysis Reporting System (FARS) 2009 crash data, there were 30 

crashes not able to be automatically located in Rhode Island, the small State in this Guidebook,

780 in California, the Large State, and a national average of 240 per State (6). Table 14 lists the 

costs estimates that analysts can use based on the equation above and the available FARS data 

on the number of crashes that need to be located and coded manually.

Table 14. Average Annual Cost to Locate Crashes.

States 

Crashes Not 

Automatically 

Located

Average 

Annual Cost

Small State 30 $120

Large State 780 $3,120

Average 240 $960

As with maintenance costs, note that the data collection system will not cover the entire 

roadway for several years. Analysts may choose to apply the cost of coding and locating 

crashes to a smaller proportion of crashes, corresponding to the proportion of 

miles of roadway that are already completed.

2.4.4. Data Storage and Other Costs

Costs of data storage depend on the type of storage used, but generally range from about 

$0.75-$1.00 per GB per month, or about $90-$120 per GB per year (7). The price of 

storage capacity, per gigabyte, will most likely fall over time.

The new methods of data analysis may be more time consuming than old data analysis methods. 

If this is the case, the analyst should incorporate the costs of these elements into the cost 

tables. The cost would be calculated by multiplying the additional hours of staff time required 

to undertake analysis with the hourly wage rate of the employees who would be undertaking 
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this work. For this Guidebook, it is assumed that States have sufficient data storage 

infrastructure to accommodate additional data collection efforts, so data storage costs are 

omitted from further calculations.

2.4.5. Annualize Costs

The analyst should project the total cost estimates over the number of years that it 

might take to complete the data collection process. Table 15 provides an example for 

one potential scenario for the timing of the completion of data collection, and, therefore, the 

timing of the payment for the data collection, maintenance, and location of crashes. The

method used in this Guidebook assumes that costs are split evenly over the years of data 

collection; however, a State DOT may employ a different cost distribution.

Table 15. Assumptions on Timing of Data Collection.

Roadway Type Data Elements Time Frame (Years)

Federal-aid Condensed FDE

Segments: 5

Intersections: 7

Ramps: 9

Non-Federal-aid Location Referencing System, 

Full FDE

LRS: 7

Segments: 6

Intersections: 8

Ramps: 10

This sample scenario assumes that the cost of expanding the collection of the Condensed FDE 

on Federal-aid roadway segments will be implemented over the course of five years. Therefore, 

the total cost of implementing this aspect of the data collection will be distributed over five 

years. Collection of the Full FDE, versus the Condensed FDE, is expected to encompass an 

additional year for segments, intersections, and ramps. The installation of the LRS on nonFederal-aid roadways is projected to take seven years. Individual States will need to determine 

their own timeframe for the installation of their data collection systems.

Based on these assumptions of the allocation of the total costs of the data collection over time, 

separate yearly cost estimates were made, and are reported in Table 16, Table 17, and Table 

18. Note that all figures are in thousands of dollars.
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Table 16. Yearly Cost Estimates by State Size for Base Data Collection (Thousands).

Year: 1 2 3 4 5 6 7 8 9 10

Elements 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Average State

LRS - $164 $164 $164 $164 $164 $164 $164 - -

Segments - $2,651 $2,651 $2,651 $2,651 $2,651 $2,651 - - -

Intersections - $25,966 $25,966 $25,966 $25,966 $25,966 $25,966 $25,966 $18,722 -

Ramps - $247 $247 $247 $247 $247 $247 $247 $247 $247

Small State 

LRS - $26 $26 $26 $26 $26 $26 $26 - -

Segments - $427 $427 $427 $427 $427 $406 - - -

Intersections - $9,354 $9,354 $9,354 $9,354 $9,354 $9,354 $9,354 $6,520 -

Ramps - $21 $21 $21 $21 $21 $21 $21 $21 $21

Large State

LRS - $591 $591 $591 $591 $591 $591 $591 - -

Segments - $9,804 $9,804 $9,804 $9,804 $9,804 $9,142 - - -

Intersections - $35,938 $35,938 $35,938 $35,938 $35,938 $35,938 $35,938 $22,323 -

Ramps - $814 $814 $814 $814 $814 $814 $814 $814 $814
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Table 17. Inventory Maintenance Costs (Thousands).

Average State

Year: 1 2 3 4 5 6 7 8 9 10

Elements 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

LRS - - $8.2 $16.4 $24.6 $32.8 $41.0 $49.2 $57.4 $57.4

Segments - - $13.5 $26.9 $40.4 $53.8 $67.3 $76.8 $76.8 $76.8

Intersections - - $327.0 $654.0 $980.9 $1,307.9 $1,634.9 $1,961.9 $2,288.9 $2,524.6

Ramps - - $3.3 $6.6 $9.9 $13.2 $16.5 $19.8 $23.1 $26.4

Year: 11 12 13 14 15 16 17 18 19 20

Elements 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

LRS $57.4 $57.4 $57.4 $57.4 $57.4 $57.4 $57.4 $57.4 $57.4 $57.4

Segments $76.8 $76.8 $76.8 $76.8 $76.8 $76.8 $76.8 $76.8 $76.8 $76.8 

Intersections $2,524.6 $2,524.6 $2,524.6 $2,524.6 $2,524.6 $2,524.6 $2,524.6 $2,524.6 $2,524.6 $2,524.6 

Ramps $29.7 $29.7 $29.7 $29.7 $29.7 $29.7 $29.7 $29.7 $29.7 $29.7 

Small State 

Year: 1 2 3 4 5 6 7 8 9 10

Elements 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

LRS - - $1.3 $2.60 $3.9 $5.3 $6.6 $7.9 $9.2 $9.2

Segments - - $2.2 $4.5 $6.7 $8.9 $11.2 $12.7 $12.7 $12.7

Intersections - - $117.8 $235.6 $353.4 $471.2 $589.0 $706.8 $824.6 $906.7

Ramps - - $0.3 $0.6 $0.8 $1.1 $1.4 $1.7 $2.0 $2.3

Year: 11 12 13 14 15 16 17 18 19 20

Elements 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

LRS $9.2 $9.2 $9.2 $9.2 $9.2 $9.2 $9.2 $9.2 $9.2 $9.2

Segments $12.7 $12.7 $12.7 $12.7 $12.7 $12.7 $12.7 $12.7 $12.7 $12.7

Intersections $906.7 $906.7 $906.7 $906.7 $906.7 $906.7 $906.7 $906.7 $906.7 $906.7

Ramps $2.5 $2.5 $2.5 $2.5 $2.5 $2.5 $2.5 $2.5 $2.5 $2.5

Large State 

Year: 1 2 3 4 5 6 7 8 9 10

Elements 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

LRS - - $29.6 $59.1 $88.7 $118.3 $147.8 $177. $207.0 $207.0

Segments - - $56.6 $113.2 $169.8 $226.4 $282.9 $317.4 $317.4 $317.4

Intersections - - $452.6 $905.1 $1,357.7 $1,810.2 $2,262.8 $2,715.3 $3,167.9 $3,449.0

Ramps - - $10.9 $21.7 $32.6 $43.4 $54.3 $65.2 $76.0 $86.9

Year: 11 12 13 14 15 16 17 18 19 20

Elements 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

LRS $207.0 $207.0 $207.0 $207.0 $207.0 $207.0 $207.0 $207.0 $207.0 $207.0

Segments $317.4 $317.4 $317.4 $317.4 $317.4 $317.4 $317.4 $317.4 $317.4 $317.4

Intersections $3,449.0 $3,449.0 $3,449.0 $3,449.0 $3,449.0 $3,449.0 $3,449.0 $3,449.0 $3,449.0 $3,449.0

Ramps $97.7 $97.7 $97.7 $97.7 $97.7 $97.7 $97.7 $97.7 $97.7 $97.7
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Table 18. Cost to Locate Crashes (Dollars).

States 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Average $137 $274 $411 $549 $686 $823 $960 $960

Small State $17 $34 $51 $69 $86 $103 $120 $120

Large State $446 $891 $1,337 $1,783 $2,229 $2,674 $3,120 $3,120

States 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Average $960 $960 $960 $960 $960 $960 $960 $960 $960 $960

Small State $120 $120 $120 $120 $120 $120 $120 $120 $120 $120

Large State $3,120 $3,120 $3,120 $3,120 $3,120 $3,120 $3,120 $3,120 $3,120 $3,120

2.5. EVALUATING THE BENEFITS AND COSTS

Once the analyst has completed the calculation of annual benefits and costs, the analyst will 

evaluate the benefits and costs. This involves five steps:

1. Ensure that all benefits and costs are in common units.

2. Calculate present values.

3. Calculate total net benefits/net present value.

4. Evaluate risk, if applicable.

5. Apply decision rules.

2.5.1. Convert into Common Units

To perform the benefit-cost evaluation, all quantified benefits and costs must be 

expressed in the same units. The above methodology was designed to estimate all benefits 

and costs in dollars. If a State DOT wishes to include any additional benefits and costs that 

were not covered here, the analyst should seek out conversion factors to ensure easy 

comparison of the benefits and costs. 

It is also important to ensure that the same assumptions are made throughout the analysis as to 

whether or not to apply an inflation factor to the benefits and costs. The analyst may 

choose not to apply an inflation factor to any of the benefits and costs. If an inflation factor is 

applied, it should be applied to all benefits and costs. For the sake of clarity, no inflation factor 

has been applied in the examples in this Guidebook.

2.5.2. Calculate Present Values

Once benefits and costs are in dollars, the analyst will then calculate the present values of 

these benefits and costs using an appropriate discount rate. Present value is the value of a 
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future cost or benefit discounted to reflect the time value of money and, if applicable, the risk 

associated with these future streams.

To evaluate present values of benefits and costs, the analyst will discount the future values 

through the application of a compound interest rate. OMB guidance suggests a 7.0 

percent discount rate (3). The equations to apply this discounting principle to costs and 

benefits are as follows:

PVcost = Cost

(1 + i)t and PVbenefit = Benefit(1 + i)t

In this equation, i represents the discount rate, and t represents the number of years into the 

project cycle. For example, if the year of evaluation were 2010, and the calculation were for 

benefits and costs in the year 2014, 2010 would be year 0, 2014 would be year four, and t

would equal four. The analyst would apply this equation to the costs and benefits for every 

year of the project life. By using a compound interest rate, costs in later years in the project 

life are discounted to a greater extent than those early in the project cycle.

The following tables identify the total discounted and undiscounted benefits and costs for the 

average State. Table 19 includes all benefits prior to, and after, discounting. The undiscounted 

benefits were presented in Table 9. The total present value of benefits is the sum of the 

present value of benefits for each year in the project life. In this example the total discounted 

annual benefits (in present value using constant $2010) is $546,118,554. Table 20 includes all 

costs prior to and after discounting. The undiscounted benefits were presented in Table 16, 

Table 17, and Table 18. The total present value of costs is the sum of the present value of 

costs for each year in the project life. In this example, the total discounted annual cost (in 

present value) is $299,090,299.
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Table 19. Total Annual Benefits Prior to and After Discounting for an Average State (Thousands).

Year: 1 2 3 4 5 6 7 8 9 10

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Undiscounted Annual 

Benefits (Current $) --- $10,677.2 $21,354.5 $32,031.7 $42,708.9 $53,386.2 $64,063.4 $74,740.6 $74,740.6 $74,740.6

Discounted Annual Benefits 

(Constant $2010) --- $9,325.9 $17,431.6 $24,436.8 $30,450.9 $35,573.5 $39,895.5 $43,499.7 $40,654.0 $37,994.4

Year: 11 12 13 14 15 16 17 18 19 20

2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Undiscounted Annual 

Benefits (Current $) $74,740.6 $74,740.6 $74,740.6 $74,740.6 $74,740.6 $74,740.6 $74,740.6 $74,740.6 $74,740.6 $74,740.6

Discounted Annual Benefits 

(Constant $2010) $35,508.7 $33,185.7 $31,014.7 $28,985.7 $27,089.4 $25,317.2 $23,661.0 $22,113.1 $20,666.4 $19,314.4

Table 20. Total Annual Costs Prior to and After Discounting for an Average State (Thousands).

Year: 1 2 3 4 5 6 7 8 9 10

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Undiscounted Annual 

Cost (Current $) --- $29,028.5 $29,380.6 $29,732.7 $30,084.7 $30,436.8 $30,672.3 $28,485.7 $21,416.1 $2,933.4

Discounted Annual Cost 

(Constant $2010) --- $25,354.6 $23,983.3 $22,682.9 $21,450.0 $20,281.3 $19,101.1 $16,578.9 $11,649.0 $1,491.2

Year: 11 12 13 14 15 16 17 18 19 20

2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Undiscounted Annual 

Cost (Current $) $38,235.7 $38,235.7 $38,235.7 $38,235.7 $38,235.7 $38,235.7 $38,235.7 $38,235.7 $38,235.7 $38,235.7

Discounted Annual Cost 

(Constant $2010) $18,165.5 $16,977.1 $15,866.5 $14,828.5 $13,858.4 $12,951.7 $12,104.4 $11,312.6 $10,572.5 $9,880.8
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2.5.3. Calculate Net Present Value and Benefit-Cost Ratio

Once the discounting process is complete, the analyst will calculate the net present value 

(NPV), or total net benefits, and the benefit-cost (B/C) ratio. NPV is calculated by 

subtracting the total present value of costs from the total present value of benefits.

NPV = Total Present Value of Benefits – Total Present Value of Costs

The NPV of investments in data collection for the fictional DOT example used in this 

Guidebook is $546,118,554 minus $299,090,299, or $247,028,255.

The B/C ratio is calculated through the following equation:

B/C Ratio = Total Present Value of Benefits

Total Present Value of Costs

In this example, the B/C ratio equals 1.83. Table 21 summarizes these figures.

Table 21. Net Present Value and Benefit-Cost Ratio.

Net Present Value $247,028,255

Benefit-Cost Ratio 1.83

2.5.4. Evaluate Risk

If the analyst has any uncertainty in the assumptions made or the estimates obtained, a 

sensitivity analysis may be performed to show how the results are affected by changes in 

the parameters. For example, if the analyst feels the final costs of the collection of data are too 

low, or omit unforeseen costs, they may choose to apply a 10-15 percent price escalation 

factor to the costs used in the base model to see how this changes the final NPV or B/C ratio. 

The analyst may also take a similar approach might on certain benefits for which there remains 

uncertainty regarding the true values. As the estimates described in this Guidebook rely on 

assumptions and, in some cases, on approximations, it may be preferable to the State DOT to 

have a range of values for the NPV and B/C ratio. One of the advantages of conducting a 

sensitivity analysis is that the analyst will be able to prepare a normal estimate and 

conservative estimate for both benefits and costs, allowing a range to be presented.

2.5.5. Decision Rules

To determine whether the implementation of a data collection project is 

economically justified, and help an analyst choose between competing alternatives, the 

following decision rules may be applied:
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• If NPV ≥ 0, a project is economically justified.

• If B/C ≥ 1, a project is economically justified.

• NPV and the B/C ratio can also be compared with the NPV or B/C ratios of alternative 

projects in order to select the most beneficial option. The highest NPV or B/C ratio for 

a given level of constraint will be the best alternative.

In the sample case, the NPV is $247,028,255, which is greater than zero, and the benefit cost 

ratio is 1.83, which is greater than one. This indicates that this data collection project is 

economically justified. It is possible that in some cases the project may be economically 

justifiable in the base case, and not in the sensitivity or risk analysis scenarios, or vice versa. In 

these cases, the analyst will need to assess the likelihood of the risk coming to pass when 

making a judgment. For some projects where there are many alternatives, the State may 

require a higher benefit cost ratio, such as two, in order for an alternative to be considered for 

implementation. In this case, our project would fail in its current formulation. This might lead 

to the overall rejection of the proposal, or may lead to attempts to reduce the cost of 

implementation, or find new ways to benefit from the data. If there are two or more different 

options for data collection, the analyst may compare the B/C of the two projects to assess 

which project is more advantageous. In addition to the B/C ratio, agencies often need to 

account for other factors in decision-making, such as priority and policy.
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3. DATA AVAILABILITY AND ESTIMATION METHODS

The method to calculate the benefits and costs of safety data will vary based on the amount of 

data available for a given State. A State that already collects all of the data elements for at least 

some of their system may be able to undertake some, or all, of the analysis using the data they 

already collect. These “high data collection States” may also choose to conduct an ex-post 

evaluation of the effectiveness of the data collection efforts already undertaken. Some “medium 

data collection States” may collect some, but not all, of the FDEs. These States may choose to 

extrapolate the effects of the collection of these data elements to the collection of data 

elements, or may choose to create a representative sample database of information borrowed 

from another State with a more extensive data collection program. Medium data collection 

States might also choose to conduct a pilot data collection effort on a small sample of roadways 

to gather sufficient data to undertake the analysis. A similar approach might be taken for “low 

data collection States,” which collect only the minimum HPMS required data.

Low or medium data collection States may not have sufficient data to conduct this analysis for 

even a small sample of roadways. If funds are not available to undertake a small data collection 

effort for this analysis, an analyst from a low or medium data collection State may construct a 

sample from a high data collection State. The analyst constructing this sample should, as much 

as possible, select the road segments and intersections that match the characteristics of the 

State’s own system. For example, the analyst would want to ensure that the percent of rural 

and urban road segments are roughly equivalent between the sample and the State’s road 

system.

This process of constructing a sample data set, or collecting data on a sample of roadways, may 

be expensive and time consuming for individual States. However, a next step may be the

development of national multipliers for improvements in roadway safety related to data. These 

national multipliers could be developed on a per-mile, intersection, and ramp basis, to allow the 

benefits to be scaled up or down to any State size. Separate multipliers might be obtained for 

roadways and intersections of different types. Additionally, adjustment factors could be 

proposed for States with certain distinctive attributes (e.g., mountainous, extreme weather 

conditions, etc.).

In addition, FHWA may also seek to create a table of national average benefits to improve the 

ease and cost-effectiveness of this type of analysis. With a national benefits estimator, States 

can adjust the national average values according to the particulars of their State. The methods 

described in this Guidebook can be used on pooled national data to derive national averages for 

benefits from specific types of data. With a generic national estimate on the benefits of data 

collection on a per-mile, per-ramp, or per-intersection basis, States could have a benchmark 

upon which to base their own investments in segment, ramp, and intersection data.
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A State might base their entire analysis on these national average benefits, or may use the 

national average benefits to fill in for gaps in data collection. If a State had no data, it could 

apply the national averages to its system by multiplying the average per-mile/intersection/ramp 

benefit with the number of miles, intersections, or ramps. If a State had all required data for its 

road segments and ramps but not for its intersections, then it could use national average 

benefits from intersection data collection on a per-intersection basis to approximate the 

benefits of collecting intersection data for the State.

This technique of transferring benefits from a similar source, in this case a national average 

instead of State-specific data, is a well-established practice in economics (8, 9, 10). In many 

practical areas, adequate data do not exist to perform rigorous economic analysis. Instead of 

abandoning the analysis altogether for lack of data, proxy data is used that is as similar to the 

initially-sought data as possible.

A national benefits estimator can allow States to perform BCAs prior to expending resources 

on direct data collection. In the future, FHWA, TRB, NHTSA, or others may choose to 

develop a national benefits estimator. Additional effort is required at the national level to 

determine the individualized benefits of investment in various safety data from pooled national 

averages.

Table 22 provides a summary of potential approaches for developing a dataset to undertake 

analysis. As illustrated in Table 22, all States could benefit from having a national benefits 

estimator, whether they are currently classified as a having a high, medium, or low level of data 

collection.

Table 22. Potential Approaches to Developing Datasets for Analysis

Category

Calculate 

benefits 

based on own 

data only

Collect 

representative 

sample of data 

from own State

Create 

representative 

sample of data from 

national or other 

State’s data

Use national 

average 

benefits (not 

currently 

available)

High Data 

Collection X X

Medium Data 

Collection X X X

Low Data 

Collection X X X
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4. CHECKLIST

The following is a list of data elements required for an agency to perform the BCA of data 

collection.

1. Saved staff hours in project identification (Safety Office and others).

• Number of hours spent on project identification through estimation techniques used in 

the absence of existing data and wage rate of staff employed to perform the estimation.

• Estimated number of hours required to collect real data (instead of relying on estimates) 

and associated wage rate.

• Departments that would benefit from data collection. A sample list may include:

o Safety Investment Planning.

o Highway Investment Planning.

o Asset Management.

o Traffic Engineering.

o Pavement Engineering.

o Metropolitan Planning Organizations (MPOs) and Local Planners.

o County Engineers.

o Emergency Response.

• Number of estimated annual FTE staff hours, for other departments, spent in 

performing estimates in lieu of relying on existing data, and associated hourly rate.

2. Faster project programming (this will require the State to choose a project for 

this example).

• Total amount of time (in months) from the identification of a safety problem location to 

the completion of countermeasure implementation.

• Average monthly crashes, by MAIS 0-1, MAIS 2-4, and MAIS 5-6 crash types that 

occurred prior to countermeasure implementation.

• Average monthly crashes, by MAIS 0-1, MAIS 2-4, and MAIS 5-6 crash types that 

occurred after countermeasure implementation.

• Estimated amount of time (in months) that programming decisions could have been

made earlier if the data elements were available.

3. Improved project prioritization (for this calculation, the State needs to collect 

some data or borrow data from a similar State).

• Total cost of the bundle of safety interventions for an improvement project (budget for 

safety improvements).

• List of 20-50 candidate sites from a previous safety improvement project (suggest at 

least two to three years ago) for ramps, segments, or intersections (the example will 
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use one set of data, such as segments, though the approach would be the same for the 

other types).

• The cost of the safety improvements required for each of the candidate sites.

• A ranking of the sites based on crash records and road characteristics, prior to the 

safety improvement, using the identification and prioritization methods preferred by the 

State in the absence of data (for example, if segment data is collected but ramps are not, 

use the method or prioritization for ramps on the candidate sites, such as total number 

of crashes versus total cost of crashes, weighted by crash severity).

• A ranking of the sites based on existing data (the assumption is that existing data 

provides more information that can be used to rank and prioritize candidate sites, and 

that the resultant ranking would be different based on improved data collection).

• Average annual crashes, by MAIS type, two to three years before improvement, for each 

site.

• Average annual crashes, by MAIS type, two to three years after improvement, for each 

site.

• Locally accepted average cost of crashes by type.

• Percentage of total segments/intersections/ramps for which data has been collected in 

the State (this will be used as a multiplier to scale up the calculations to the state level).

4. Saved staff hours in streamlined evaluation (Safety Office and others).

• Number of hours and wage rates saved through streamlined evaluation due to the 

availability of data, across all departments identified in item 1 of this checklist.

5. Data collection costs.

• Costs of collecting roadway, ramp, intersection, and LRS data, if other than those 

provided in the Market Analysis of Collecting Fundamental Data Elements to Support the 

Highway Safety Improvement Program (5).

• Roadway characteristics for the State, including number of miles, ramps, and

signalized/unsignalized intersections.

6. Operations and maintenance costs.

• Roadway characteristics for the State, including number of miles, ramps, and 

signalized/unsignalized intersections from the previous list item.

• The estimated amount of staff time and hour wages required to update and maintain the 

inventory of roadway elements.

7. Crash location and coding costs.

• Total annual injury and fatal crashes not automatically located.
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REFERENCES

1. Lawrence, M., D. Cartwright-Smith, J. Mans, P. Nguyen, and N. Lefler, Benefit Cost Analysis of 

Investing in Data Systems and Processes for Data-Driven Safety Programs: Project Report, Federal 

Highway Administration, FHWA-SA-12-029, Washington, DC, 2012.

2. Federal Highway Administration, Economic Analysis Primer, FHWA Office of Asset 

Management, FHWA-IF-03-032, Washington, DC, 2003, updated April 2011,

<http://www.fhwa.dot.gov/infrastructure/asstmgmt/primer05.cfm>.

3. Office of Management and Budget. Circular A-94 Revised, October 1992. Accessed online 

<http://www.whitehouse.gov/omb/circulars_a094/>.

4. Blincoe, L., A. Seay, E. Zaloshnja, T. Miller, E. Romano, L. Luchter, and R. Spicer, The 

Economic Impact of Motor Vehicle Crashes 2000. National Highway Traffic Safety 

Administration, Washington, DC, 2002.

5. Lefler, N., R. Fiedler, H. McGee, R. Pollack, and J. Miller, Market Analysis of Collecting 

Fundamental Roadway Data Elements to Support the Highway Safety Improvement Program, 

Federal Highway Administration, FHWA-SA-11-40, Washington, D.C., 2011.

6. National Highway Traffic Safety Administration, Traffic Safety Facts: Highlights of 2009 Motor 

Vehicle Crashes, DOT-HS-811-363, Washington, DC, 2010.

7. “Data Center Rates.” Center for Information Technology Service Catalog. National Institute of 

Health, 2011. Accessed online 23 November 2011, 

<http://cit.nih.gov/ServiceCatalog/DataCenterRates.htm>.

8. Brouwer, R. and F. A. Spaninks, The Validity of Environmental Benefits Transfer: Further 

Empirical Testing, Environmental and Resource Economics 14. Pp. 95-117. 1999.

9. Bergstrom, J. C. and L. O. Taylor. Using meta-analysis for benefits transfer: Theory and practice,

Ecological Economics 60 (2006). Pp.351-360.

10. King, D. M., and M. J. Mazzotta, Ecosystem Valuation Methods, Section 8: Benefit Transfer 

Method, Accessed online: http://www.ecosystemvaluation.org/benefit_transfer.htm.
For More Information:

Visit http://safety.fhwa.dot.gov/

FHWA, Office of Safety

Dr. Heather Rothenberg

Heather.Rothenberg2@dot.gov

(202) 366-2193

FHWA-SA-12-030