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SAFETY PERFORMANCE ANALYSIS OF TSMO: 

A PRACTICAL APPROACH FOR ASSESSING 

SAFETY SERVICE PATROL EFFECTS ON 

SECONDARY CRASHES 
SAFETY PERFORMANCE ANALYSIS OF TSMO: A PRACTICAL APPROACH FOR ASSESSING 

SAFETY SERVICE PATROL EFFECTS ON SECONDARY CRASHES 

ii

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SAFETY PERFORMANCE ANALYSIS OF TSMO: A PRACTICAL APPROACH FOR ASSESSING 

SAFETY SERVICE PATROL EFFECTS ON SECONDARY CRASHES 

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TECHNICAL DOCUMENTATION PAGE

1. Report No.

FHWA-SA-19-042

2. Government Accession 

No.

3. Recipient's Catalog No.

 

4. Title and Subtitle

Safety Performance Analysis of TSMO: A Practical Approach for Assessing 

Safety Service Patrol Effects on Secondary Crashes

5. Report Date

November 2019 

6. Performing Organization Code

7.Author(s) 

Porter, R.J., Gooch, J., and Dunn, M. 

8. Performing Organization Report No.

9. Performing Organization Name and Address

Vanasse Hangen Brustlin, Inc (VHB)

940 Main Campus Driver, Suite 500

Raleigh, NC 27606 

10. Work Unit No.

11. Contract or Grant No.

DTFH61-05-D-00024 (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, July 2017 – November 

2019 

14. Sponsoring Agency Code

FHWA

15. Supplementary Notes

The contract manager for this report was Jerry Roche, Federal Highway Administration Office of Safety. 

16. Abstract

The Federal Highway Administration (FHWA) Office of Safety, in cooperation with the Highway Safety Manual (HSM) 

Implementation Pooled Fund Study, recently completed a safety analysis needs assessment for TSMO. The safety analysis 

needs assessment characterized the current state of practice, knowledge, and skills for quantifying the safety performance 

effects of TSMO. It also identified gaps in the existing body of knowledge and corresponding needs, which will provide the 

foundation for future research activities and advancements in practice. One group of needs focused on exploring sub-annual 

safety data collection and analysis methods to more effectively address the dynamic conditions under which TSMO 

strategies operate. This document presents one such sub-annual analysis method: a probabilistic model of crash occurrence

to estimate the extent to which strategies that reduce primary incident clearance times affect the potential for secondary 

crashes. The analysis method is presented in the context of evaluating the secondary crash effects of safety service patrols 

(SSPs). The result of the analysis is a benefit/cost (B/C) ratio based on the expected change in secondary crash costs and 

the anticipated costs of SSP operation. The document also contains an example application of the 10-step approach based 

on a previously published analysis of an SSP program in Indiana. Because the SSP effects on secondary crashes are derived 

from reductions in traffic incident clearance times, the method can also be extended to other TIM tactics or functions that 

are aimed at reducing incident clearance times. 

17. Key Words: 

traffic incident management; safety performance; 

secondary crashes, safety service patrols

18. Distribution Statement

No restrictions.

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this 

page) Unclassified

21. No. of Pages

39

22. Price

Form DOT F 1700.7 (8-72) Reproduction of completed pages authorized 
SAFETY PERFORMANCE ANALYSIS OF TSMO: A PRACTICAL APPROACH FOR ASSESSING 

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SAFETY PERFORMANCE ANALYSIS OF TSMO: A PRACTICAL APPROACH FOR ASSESSING 

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Table of Contents

INTRODUCTION......................................................................................... 1

SAFETY PERFORMANCE ANALYSIS QUESTION................................ 3

DATA NEEDS............................................................................................... 4

SAFETY PERFORMANCE ANALYSIS STEPS – ESTIMATING 

POTENTIAL BENEFITS OF PROGRAM IMPLEMENTATION ............. 8

SUPPORTING TOOLS.............................................................................. 25

DOCUMENTING ANALYSIS AND REPORTING RESULTS............... 26

BROADER APPLICATIONS..................................................................... 28

REFERENCES.............................................................................................. 29
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vi 

List of Tables

Table 1. Variables, notation, and definitions for probabilities...................................... 5

Table 2. Variables, notation, and definitions for crash prediction. .............................. 5

Table 3. Variables, notation, and definitions for economic analysis. ........................... 6

Table 4. Sample data used to estimate the effect of primary incident duration on 

the probability of a secondary curve using binary logistic regression. ........................ 7

Table 5. Binary logistic regression model reported by Latoski et al. to determine 

how primary incident clearance time affects the probability of a secondary crash. 13

Table 6. Average primary incident conditions with and without SSP for the 

example. ...........................................................................................................................13

Table 7. Estimated probability of a secondary crash given average primary incident 

conditions. ........................................................................................................................14

Table 8. Severity distribution of secondary crashes on Arizona roads from 2014-

2016. ..................................................................................................................................21

Table 9. Secondary crashes by severity for the example agency. ..............................23
SAFETY PERFORMANCE ANALYSIS OF TSMO: A PRACTICAL APPROACH FOR ASSESSING 

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List of Figures

Figure 1. Graphic. Procedure to estimate the safety benefits of SSP program 

implementation. ................................................................................................................ 8

Figure 2. Equation. Binary logistic regression model illustrating the probability of a 

secondary crash.................................................................................................................. 9

Figure 3. Equation. Proportion of crashes with SSPs in average incident durations.

...........................................................................................................................................10

Figure 4. Equation. Proportion of crashes without SSPs in average incident 

durations...........................................................................................................................11

Figure 5. Equation. Probability model for crashes during winter. .............................14

Figure 6. Equation. Probability model for crashes not during winter. ......................14

Figure 7. Equation. Sum of primary crashes. ...............................................................15

Figure 8. Equation. Predicted number of primary crashes as a function of traffic 

volume, duration of the evaluation period, and segment length. ..............................15

Figure 9. Equation. Example calculation for a ratio adjustment to the predicted 

number of primary crashes. ...........................................................................................16

Figure 10. Equation. Prediction of the average secondary crash frequency without 

SSP. ...................................................................................................................................17

Figure 11. Equation. Sum of predicted average secondary crash frequency. ...........18

Figure 12. Equation. Predicted average secondary crash frequency without SSP...19

Figure 13. Equation. Combination of the prediction for winter and non-winter. ....19

Figure 14. Equation. Calculation for annual difference. ..............................................20

Figure 15. Equation. Estimation of monetary benefit. ................................................20

Figure 16. Equation. Calculation for average crash cost.............................................20

Figure 17. Equation. Calculation for the B/C ratio. .....................................................22

Figure 18. Equation. Calculation for the Uniform Series Worth Factor. .................22
SAFETY PERFORMANCE ANALYSIS OF TSMO: A PRACTICAL APPROACH FOR ASSESSING 

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Figure 19. Equation. Comprehensive crash cost calculation. .....................................23

Figure 20. Equation. Example calculation for B/C ratio..............................................24
SAFETY PERFORMANCE ANALYSIS OF TSMO: A PRACTICAL APPROACH FOR ASSESSING 

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ACRONYMS

AASHTO American Association of State Highway and Transportation Officials

B/C benefit/cost 

CMF crash modification factors

FHWA Federal Highway Administration

HSM Highway Safety Manual

MAP-21 Moving Ahead for Progress in the 21st Century

SSP safety service patrols

TSMO Transportation Systems Management and Operations
SAFETY PERFORMANCE ANALYSIS OF TSMO: A PRACTICAL APPROACH FOR ASSESSING 

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1 

INTRODUCTION

Moving Ahead for Progress in the 21st Century (MAP-21) defines Transportation Systems 

Management and Operations (TSMO) as an “integrated set of strategies to optimize the 

performance of existing infrastructure through the implementation of multimodal and 

intermodal cross-jurisdictional systems, services, and projects designed to preserve capacity and 

improve security, safety, and reliability of the transportation system.” TSMO offers agencies a 

wide range of potential strategies for addressing system- and project-level performance needs 

with cost-effective, tailored strategies. State and local agencies are increasingly recognizing 

TSMO as a core business area in support of maximizing the performance of their transportation 

infrastructure and making better use of resources. Some regions in the United States have 

found it useful to develop TSMO plans to define a common vision for TSMO in the region, 

develop performance objectives to guide the selection of TSMO strategies, and identify 

performance measures that will enable a region to track progress towards their objectives. 

TSMO plans also identify potential policies, services, and projects to make progress towards 

the performance objectives.

Performance analysis helps agencies make sound decisions on which TSMO policies, services, 

and projects to pursue as part of performance-based planning and programming. A performance 

analysis of a TSMO strategy might quantify, for example, how the strategy would be expected 

to affect measures of travel time, travel time reliability, pollutants/air quality, and the number 

and severity of traffic crashes. An agency could monetize these changes and determine an 

overall benefit-cost ratio for the investment. 

The ability for agencies to quantify the effects of TSMO strategies on the number and severity 

of traffic crashes is limited when compared to similar abilities for other performance measures 

(e.g., travel times, vehicle emissions). The Federal Highway Administration (FHWA) Office of 

Safety, in cooperation with the Highway Safety Manual (HSM) Implementation Pooled Fund 

Study, recently completed a safety analysis needs assessment for TSMO. While safety and 

TSMO have clear interrelationships, the needs assessment concluded there is incompatibility 

between many existing safety performance analysis methods and tools and the characteristics of 

TSMO. For example, few TSMO strategies have robust crash modification factors (CMFs). In 

addition, the predictive analysis methods in the American Association of State Highway and 

Transportation Officials (AASHTO) HSM do not consider daily, hourly, or sub-hourly variations 

in traffic characteristics and the road environment that are a key part of fully assessing the 

safety performance of TSMO. The safety analysis needs assessment characterized the current 

state of practice, knowledge, and skills for quantifying the safety performance effects of TSMO. 

It also identified gaps in the existing body of knowledge and corresponding needs, which will 

provide the foundation for future research activities and advancements in practice. 
SAFETY PERFORMANCE ANALYSIS OF TSMO: A PRACTICAL APPROACH FOR ASSESSING 

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One group of needs focused on exploring sub-annual safety data collection and analysis 

methods to more effectively address the dynamic conditions under which TSMO strategies 

operate. This document presents one such sub-annual analysis method: a probabilistic model of 

crash occurrence to estimate the extent to which strategies that reduce primary incident 

clearance time affect the potential for secondary crashes. The analysis method is presented in 

the context of evaluating the secondary crash effects of safety service patrols (SSPs). The result 

of the analysis is a benefit/cost (B/C) ratio based on the expected change in secondary crash 

costs and the anticipated costs of SSP operation. The document also contains an example 

application of the 10-step approach based on a previously published analysis of an SSP program 

in Indiana.(3) In addition to Indiana’s SSP evaluation, other examples that relate secondary crash 

potential to primary traffic incident clearance time are also available in the published 

literature.(4,5,6)

Because the SSP effects on secondary crashes are derived from reductions in traffic incident 

clearance time, the method can also be extended to other TIM tactics or functions that are 

aimed at reducing incident clearance times. Following an overview of SSPs and the analysis 

question, the content covers the data definitions and requirements, analysis steps, supporting 

tools, and effective practices for reporting the results and documenting assumptions and 

limitations.
SAFETY PERFORMANCE ANALYSIS OF TSMO: A PRACTICAL APPROACH FOR ASSESSING 

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3 

SAFETY PERFORMANCE ANALYSIS QUESTION 

Safety service patrols (SSPs) have been a key part of traffic incident management since the 

1960s.(1) SSPs exist in many forms, all of which seek to reduce the duration of traffic incidents 

and improve the safety of incident victims, responders, and the traveling public by performing 

one or more of the following duties:(1)

• Provide the initial report, response, or verification of an event.

• Provide initial traffic control and scene safety for responders and victims.

• Support the incident commander as needed to manage the lane closure.

• Communicate resource needs to their agencies.

• Assist in clearance of debris, disabled vehicles, or crashes in or along the travel portion 

of the roadway.

Along with decreasing travel time delays caused by incident-related capacity reductions, 

reducing incident duration is expected to lower the potential for secondary crashes. Secondary 

crashes are generally identified as those crashes that occur within the scene of a primary 

incident, or within a queue resulting from a primary incident.(2) As part of performance-based 

planning and programming, agencies seeking to reduce the number and severity of secondary 

crashes may want to determine whether the secondary crash reduction benefits of SSPs 

outweigh the costs of operation and to what degree. 

The following sections of this document outline a method for estimating the effects of SSPs on 

the potential for secondary crashes. The result of the analysis is a benefit/cost (B/C) ratio based 

on the expected change in secondary crash costs and the anticipated costs of SSP operation. 
SAFETY PERFORMANCE ANALYSIS OF TSMO: A PRACTICAL APPROACH FOR ASSESSING 

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DATA NEEDS

Incident data, including data on crashes, are the key data required for this analysis. The 

incidents of interest are those that will be the focus of SSPs, including, for example, traffic 

crashes, disabled vehicles, and/or spilled cargo. Crash and other incident-level data of interest 

should include, at a minimum, location, location characteristics (e.g., cross section, alignment, 

ramp presence and location) severity (for crashes), prevailing operational conditions, and 

incident clearance time. Incident clearance time is the time between the first recordable 

awareness of an incident by a responsible agency and the time at which the last responder has 

left the scene. Incident clearance time is one part of the overall incident duration, which is the 

time from when the incident first occurs to the time when any queue resulting from that 

incident dissipates. The timing of the events that bound incident clearance time can be clearly 

identified, and therefore incident clearance time is more directly measurable than the overall 

incident duration. Incident duration is typically estimated using a variety of information sources 

(e.g., crash reports, analysis of traffic data from stationary and mobile sensor data). 

Parameters are also needed for distinguishing primary incidents (including primary crashes) 

from secondary crashes. Goodall et al. identified three primary methods for detecting and 

classifying secondary crashes: 1) crashes that occur within a defined distance or time period of a 

primary crash, 2) crashes that occur within the modeled queue of a primary crash as 

determined using cumulative arrival and departure curve models, and 3) crashes that occur 

within an observed queue from empirical measurements during the primary incident.(5) As an 

example, one analysis in the literature identified a crash as secondary if it occurred within three 

miles upstream of the primary crash and from the time the incident occurred to 15 minutes

after the incident was cleared.

(4) Another study also included crashes occurring in the opposite 

direction of travel under the definition of secondary crashes, if the opposite direction crash 

occurred within the hour following the primary incident, within a half-mile upstream of the 

primary incident, and in a queue.(6) Some states now include a “primary crash” or “secondary 

crash” variable on their crash report forms. Secondary crash identification is an area of ongoing 

work, and ultimately the reliability of the analysis results will be dependent on the accuracy of 

the available data, including the primary and secondary crash distinctions.

If an incident prediction model is used for predicting primary incidents expected to occur in the 

future, traffic and geometric data for the patrolled facilities and other similar facilities to serve 

as potential predictor variables will be necessary in addition to the incident data. Data on the 

specific routes and operational times intended for the SSPs, the proportions of primary 

incidents within the coverage areas and times expected to receive SSP assistance, the associated 

costs of implementing and operating the SSP program, and agency crash costs to monetize the 

reduction in secondary crash potential are also needed. FHWA’s Crash Costs for Highway Safety 

Analysis proposes national crash costs and provides guidance on establishing State-specific crash 
SAFETY PERFORMANCE ANALYSIS OF TSMO: A PRACTICAL APPROACH FOR ASSESSING 

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5 

costs.(7) If an agency does not have experience with SSPs, peer exchange (either formal or 

informal) with other agencies with existing SSP programs may help to gather data or identify 

assumptions with respect to factors such as patrol response rate, expected reduction in 

primary incident clearance time, and program costs.

Table 1 through table 3 contains the notation and definitions of the variables referenced in the 

safety analysis.

Table 1. Variables, notation, and definitions for probabilities. 

Variable Description

PSC The probability of a secondary crash occurring

PSC,SSP The probability of a secondary crash occurring with SSP

PSC,NoSSP The probability of a secondary crash occurring without SSP

PSC,Winter The probability of a secondary crash occurring in Winter, per Latoski et al.(3)

PSC,NotWinter The probability of a secondary crash occurring not in Winter, per Latoski et al.(3)

PSSP,Resp 

The proportion of the predicted average primary incident frequency within the SSP 

coverage areas and times that will receive service from SSPs

Table 2. Variables, notation, and definitions for crash prediction. 

Variable Description (Units)

xi

Covariate i describing the primary incident, can include primary incident clearance 

time, vehicle type, etc. (units vary)

a, bi

Regression constant (a) and coefficients corresponding to each covariate (bi) 

(unitless) 

NPI

The predicted average primary incident frequency for study sites within the study 

area (primary incidents per year)

NPC

The predicted average primary crash frequency for study sites within the study area

(primary crashes per year)

NOI

The predicted average primary other incident frequency for study sites within the 

study area (other primary incidents per year)

pOI

The ratio of predicted average primary other incident frequency to predicted average 

primary crash frequency (unitless)

NSC,NoSSP 

Predicted average secondary crash frequency without SSPs (secondary crashes per 

year)

NSC,SSP Predicted average secondary crash frequency with SSPs (secondary crashes per year)

NSC,Difference 

The difference in predicted average secondary crash frequency with and without an 

SSP program (secondary crashes per year)
SAFETY PERFORMANCE ANALYSIS OF TSMO: A PRACTICAL APPROACH FOR ASSESSING 

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Table 3. Variables, notation, and definitions for economic analysis. 

Variable Description (Units)

$SC,Avg The average, comprehensive costs of a secondary crash (dollars)

$SC,Difference 

The predicted comprehensive cost of the difference in average secondary crash 

frequency with and without an SSP program (dollars)

$i 

The comprehensive cost for a secondary crash of severity i, where i can be any 

severity level on the KABCO scale (dollars)

Nx 

The observed number of secondary crashes of severity x, where x can be any severity 

level on the KABCO scale (secondary crashes)

B/C Benefit-cost ratio for the SSP program (unitless)

$CapitalCost Estimated capital cost of the SSP program (dollars)

$AnnualCost Estimated annual costs of the SSP program (dollars)

P/A Uniform-series present worth factor – converts the annual cost of secondary crash 

reduction and maintenance costs to a present value (unitless)

i Discount factor for monetary growth (unitless)

t Estimated service life of the SSP program (years)

Table 4 shows an example database structure for estimating the models described in this 

document. In this example, one row of the database represents one primary incident. Each 

entry provides information about the primary incident, including the roadway characteristics at 

the location of the primary incident and whether a secondary crash occurred. Analysts applying 

this method may choose to include other variables that are available or of interest to their 

agencies.
SAFETY PERFORMANCE ANALYSIS OF TSMO: A PRACTICAL APPROACH FOR ASSESSING SAFETY SERVICE PATROL EFFECTS 

ON SECONDARY CRASHES 

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Table 4. Example data structure for estimating the effect of primary incident clearance time on the probability 

of a secondary crash. 

Primary 

Incident 

ID

Primary 

Incident 

Type

Date Day of 

Week

Hour 

[Military]

Did a 

secondary 

crash 

occur? 

[Yes or 

No]

Incident 

clearance 

time 

[minutes]

Horizontal 

Geometry 

[tangent or 

curve]

Lane 

Width 

[feet]

Outside 

Shoulder 

width

Number 

of Lanes 

in 

Direction 

of Travel

Did the 

primary 

incident 

occur in 

congested 

flow?

459871 Crash 7/12/15 Monday 16 No 29 Tangent 12 8 3 Yes 

459872 Disabled 

Vehicle

7/13/15 Tuesday 7 No 26 Tangent 12 10 2 Yes 

459872 Crash 7/13/15 Tuesday 8 Yes 47 Tangent 11 8 3 Yes 

459873 Disabled 

Vehicle

7/16/15 Friday 13 No 18 Curve 12 8 2 No

459874 Spilled 

Cargo

7/22/15 Thursday 9 No 82 Tangent 12 10 3 Yes 

459875 Crash 7/22/15 Thursday 9 No 34 Curve 12 12 2 Yes 

459876 Crash 7/23/15 Friday 6 Yes 40 Curve 12 6 2 No

459877 Disabled 

Vehicle

7/23/15 Friday 17 No 39 Tangent 11 3 3 Yes 

459878 Disabled 

Vehicle

7/28/15 Wednesday 18 No 23 Tangent 12 4 3 Yes 
NOTEWORTHY PRACTICES IN SAFETY PERFORMANCE ANALYSIS OF TSMO: SAFETY 

SERVICE PATROLS

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SAFETY PERFORMANCE ANALYSIS STEPS – ESTIMATING 

POTENTIAL BENEFITS OF PROGRAM IMPLEMENTATION

This section describes a nine-step procedure for estimating the B/C ratio of SSPs. Figure 1

provides a visual representation of the procedure. Both “simple” and “advanced” approaches 

are provided for most steps, depending on the agency capabilities for conducting this B/C 

analysis. Secondary crash frequency is predicted on an annual basis. Where possible, agencies 

are encouraged to use the advanced approach in each step, as it will provide more detailed and 

localized information. 

Source: FHWA.

Figure 1. Graphic. Procedure to estimate the safety benefits of SSP program 

implementation. 

1. Estimate the probability of a secondary crash as a function of primary incident

clearance time.

Research suggests the probability of a secondary crash occurring is a function of the primary

incident clearance time.

(4,5,6) The first step involves determining the relationship between

primary incident clearance time and the probability of a secondary crash.

Outcome

The probability of a secondary crash occurring given that a primary incident has occurred

and given the characteristics of the primary incident.
NOTEWORTHY PRACTICES IN SAFETY PERFORMANCE ANALYSIS OF TSMO: SAFETY 

SERVICE PATROLS

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Simple Approach

The literature indicates that the probability of a secondary crash occurring increases by 

approximately one percentage point for every additional 2-3 minutes of primary incident

clearance time.

(4,5,6) In the absence of agency-specific data, the simple approach to predicting 

the probability of a secondary crash based on primary incident clearance time would be to 

base the probability on this general relationship. 

Advanced Approach

A more advanced approach applies binary logistic regression to a local dataset like the

example dataset in table 4. The resulting model predicts the probability that a secondary 

crash will occur during the primary incident as a function of primary incident characteristics, 

including incident clearance time. 

Binary logistic regression can be used to model dependent variables with two possible 

outcomes (e.g., a secondary crash occurs, a secondary crash does not occur). The resulting 

model predicts the probabilities of the binary outcomes occurring as a function of variables 

that affect the probabilities. In this case, the probability being predicted will be the

probability of a secondary crash occurring during the primary incident, while the predictive 

variables will describe the primary incident (e.g., hour of the day, day of week, clearance 

time, type of incident, vehicle types involved, number of lanes, shoulder width). The key

predictor for implementing the method in this document is the primary incident clearance 

time; the other variables are considered in an effort to more accurately capture the

contribution of clearance time on secondary crash occurrence. 

The equation in Figure 2 illustrates the form of the binary logistic regression model 

describing the probability of a secondary crash (PSC).(8)

Figure 2. Equation. Binary logistic regression model illustrating the probability of a 

secondary crash. 

Where:

PSC = the probability of a secondary crash occurring given the characteristics of the primary 

incident. 
NOTEWORTHY PRACTICES IN SAFETY PERFORMANCE ANALYSIS OF TSMO: SAFETY 

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xi = covariates describing the primary incident (e.g., hour of the day, day of the week,

clearance time, type of incident, vehicle types involved, number of lanes, shoulder width). 

a, bi = regression coefficients.

As shown by the example dataset in table 4, the observations building the modeling dataset 

are primary incidents. In other words, one row of the dataset represents one primary 

incident.. For each primary incident, the data should indicate whether a secondary crash 

occurred. This variable is the dependent variable (e.g., 1 = a secondary crash occurred, 0 = 

a secondary crash did not occur). The incident clearance time, type of incident, vehicle 

types involved, time of day, and other factors are the independent variables. 

Binary logistic regression is a relatively robust and informative method for modeling 

dependent variables with two possible outcomes. Model results are straight forward to 

interpret and incorporate into a benefit-cost analysis. Researchers have applied binary 

logistic regression in multiple contexts in the TSMO-related literature, including evaluations 

of traffic signal coordination and models that relate measures of traffic operational 

performance to crash probability over short time intervals.(9,10,11,12)

As with any statistical method, analysts should be aware of how to assess the dataset for 

accuracy and sample size. This includes whether the dataset contains an adequate number 

of cases for each binary outcome (e.g., an adequate number of cases with a secondary crash 

and without a secondary crash for different combinations of traffic volume and roadway 

cross section). Analysts should also be familiar with how to specify models, assess the 

model goodness of fit, and validate the model by testing its predictive performance on 

different datasets than that used to develop the model. Binary logistic regression is one of 

several approaches available to analyze binary outcomes. Other alternatives include probit

regression, classification and regression trees, and rare event logistic regression.

2. Predict the probability of a secondary crash occurring with and without SSP.

Using the binary logistic regression model estimated in Step 1, estimate the proportion of

primary crashes expected to produce a secondary crash with and without SSPs based on

differences in average incident clearance times with and without SSPs (see figure 3 and

figure 4).

Figure 3. Equation. Proportion of primary crashes with SSPs expected to result in a 

secondary crash. 
NOTEWORTHY PRACTICES IN SAFETY PERFORMANCE ANALYSIS OF TSMO: SAFETY 

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Figure 4. Equation. Proportion of primary crashes without SSPs expected to result 

in a secondary crash. 

This step requires estimating the average incident clearance times with and without SSPs. 

Incident clearance times can be estimated using existing traffic monitoring systems, police 

incident reports, and other types of field observations. If the agency has already 

implemented an SSP program and is evaluating its effectiveness, it can use observed average 

primary incident clearance times with and without SSP assistance. If the agency is 

considering an SSP program, they can use their own data to observe existing primary 

incident clearance times (without SSP assistance) and use information from other SSP 

programs to estimate expected clearance times with SSP assistance. 

Average values for the other primary incident characteristics can be used for the remainder 

of the variables in the model. 

Outcome

The probabilities of secondary crashes given average primary incident conditions with and 

without SSPs.
NOTEWORTHY PRACTICES IN SAFETY PERFORMANCE ANALYSIS OF TSMO: SAFETY 

SERVICE PATROLS

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Example – Steps 1 and 2

To demonstrate application of the method outlined in this document, this example adopts 

the binary logistic regression model reported by Latoski et al. to evaluate the effectiveness 

of the Hoosier Helper program in Indiana.(3) In applying this model as an example, the key 

model coefficients will be those that relate the incident clearance times to the probability of 

a secondary crash. Agencies wishing to test this method are encouraged to estimate binary 

logistic regression models as outlined in Step 1 using their own data, as these key model 

coefficients may change across locations and time periods. This model and other similar 

models in the literature indicate that the probability of a secondary crash occurring 

increases by approximately one percentage point for every additional 2-3 minutes of 

primary incident clearance time.

(4,5,6)

Table 5 displays the binary logistic regression model reported by Latoski et al. Positive 

coefficients indicate that the presence of a variable, or an increase in the magnitude of the 

variable, increases the probability of a secondary crash. For example, the sixth variable in 

table 5 references whether the incident occurs on a weekday. If so, the probability of a 

secondary crash is higher than if the primary incident is on a weekend. The first and second 

variables in table 5 show that the probability of a secondary crash increases as the clearance 

time increases, regardless of the season. However, the rate of increase in secondary crash 

probability with clearance time was higher during spring, summer, and fall (coefficient of 

0.031) than it was during winter (coefficient of 0.017) in this data. Possible explanations 

include differences in traffic volumes (i.e., lower volumes on average during winter) and 

differences in speeds approaching the incident location (i.e., lower speeds on average during 

winter). Alternatively, negative coefficients indicate a decrease in secondary crash 

probability in the presence of a variable, or an increase in the magnitude of the variable. The 

model in table 5 shows that if the primary incident occurred near a ramp or median, the 

probability of a secondary crash is lower than if the primary incident did not occur near a 

ramp or median.

The following represents an example application of the model in table 5 from Latoski et al 

in the context of analyzing SSPs.(3) Average inputs for the example are provided in table 6. 

To account for average incident conditions, most indicator variables in the probability 

function are treated as averages. For instance, 80 percent of primary incidents are 

passenger car incidents, so in place of entering the typical binary inputs of 0 or 1 for the 

indicator variable of “primary incident vehicle is a passenger car,” analysts can use the 

proportion of incidents involving a passenger car, 0.80. 

The binary logistic regression model is used to calculate the probability of a secondary crash 

with and without SSP. However, because the relationship between primary incident 

clearance time and secondary crash probability is different in the winter compared to the 
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remainder of the year in the Latoski et al. model, this process is executed twice, for both 

winter and non-winter seasons. Figure 5 and figure 6 show the probability models for 

secondary crashes during winter and other seasons. The calculated probabilities for winter, 

non-winter, with, and without SSP are provided in table 7.

Table 5. Binary logistic regression model reported by Latoski et al. to 

determine how primary incident clearance time affects the probability of a 

secondary crash.

Variable (xi) Coefficient 

(bi) 

tstatisticp-value

Primary incident clearance time (if the season is 

winter) 0.017 3.26 < 0.001

Primary incident clearance time (if the season is not 

winter) 0.031 6.69 < 0.001

Primary incident vehicle is a passenger car 0.964 2.34 0.019 

Primary incident vehicle is a single-unit truck 0.415 0.67 0.506 

Primary incident vehicle is a combination truck 0.731 1.67 0.096 

Primary incident occurs on a weekday 0.353 1.83 0.071 

Primary incident occurs on a ramp or in the median -0.248 1.21 0.232 

Constant -2.440 5.61 < 0.001

Table 6. Average primary incident conditions with and without SSP for the 

example. 

Variable (xi) Without 

SSP

With SSP

Primary incident clearance time (if the season is winter) 

(x1,Winter) 

20 Minutes 15 Minutes

Primary incident clearance time (if the season is not 

winter) (x1,NotWinter) 

20 Minutes 15 Minutes

Primary incident vehicle is a passenger car (x2) 80% of 

incidents 

80% of 

incidents 

Primary incident vehicle is a single-unit truck (x3) 10% of 

incidents 

10% of 

incidents 

Primary incident vehicle is a combination truck (x4) 10% of 

incidents 

10% of 

incidents 

Primary incident occurs on a weekday (x5) 90% of 

incidents 

90% of 

incidents 

Primary incident occurs on a ramp or in the median (x6) 10% of 

incidents 

10% of 

incidents 
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Figure 5. Equation. Probability model for crashes during winter.

Figure 6. Equation. Probability model for crashes not during winter. 

Table 7. Estimated probability of a secondary crash given average primary 

incident conditions. 

Without SSP

(PSC,NoSSP) 

SSP (PSC,SSP) 

Winter 0.285 0.268 

Not 

Winter

0.345 0.311 

As expected, the calculated probabilities in table 7 show the probability of a secondary 

crash is lower when the primary incident clearance time is shorter.
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3. Predict average primary incident frequency within the SSP coverage areas and

times.

This step provides a prediction of the number of primary incidents (NPI) that have the

potential to lead to secondary crashes. The number of primary incidents is the sum of

primary crashes (NPC) and the number of other incidents (NOI), such as vehicle breakdowns

(as shown in figure 7).

Figure 7. Equation. Number of primary incidents. 

For analyzing the potential impacts of SSPs, this prediction of primary incident frequency 

should be limited to anticipated patrol times (e.g., peak hours, AM peak only, PM peak only, 

24 hours) and facilities on which the patrols will occur (e.g., all freeways, selected freeways 

and arterials, tunnels). 

Figure 8 shows that the predicted number of primary crashes (NPC) is typically a function of 

exposure variables such as the traffic volume, duration of the evaluation period and segment 

length. Primary crash frequency is also expected to be a function of other characteristics, 

including horizontal and vertical alignment; lane, shoulder, and median widths; number of 

lanes; barrier presence and offset; and location and type of access. Detailed guidance for 

estimating crash prediction models is available through FHWA.

(13)

 

Figure 8. Equation. Predicted number of primary crashes as a function of traffic 

volume, duration of the evaluation period, segment length, and site geometrics. 

Models that predict the number of other incidents (NOI) are not typically available. One 

option is to predict the number of other incidents using a ratio adjustment to the predicted 

number of primary crashes. Figure 9 provides an example of this calculation. Agencies can 

review police incident reports along the subject facilities and find the proportional ratio 

(pOI) between primary crashes and other incidents. Assuming this ratio is fixed, agencies can 

use their primary crash prediction model to predict average primary crash frequency then 

apply the ratio of other incidents to determine the predicted average frequency of other 

incidents. 
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Figure 9. Equation. Example calculation for a ratio adjustment to the predicted 

number of primary crashes. 

In general, pOI ranges from 0.053 to 0.111, which means that crashes are expected to make 

up 90 to 95 percent of primary incidents. (14)

Outcome

The predicted average primary incident frequency on the patrolled facilities during the 

patrolled times. 

Simple Approach

Estimate an average primary incident frequency for patrol facilities during anticipated patrol 

times using historical incident and crash data. 

Advanced Approach

Develop a primary incident prediction model for facilities like those to be patrolled. Use the 

prediction model to predict annual average incident frequency for patrolled roadways 

during anticipated patrol times. 

4. Estimate the predicted average number of primary incidents within the

coverage areas and times that will receive assistance from the SSPs.

It is unlikely the SSPs will be able to respond to all primary incidents occurring on the

targeted facilities and during the targeted times. As a result, this step incorporates the

proportion of primary incidents where the patrols will provide assistance (PSSP,Resp) and, by

default, the remaining proportion of primary incidents where patrols will not provide

assistance (1 – PSSP,Resp). Agencies implementing these programs should reach out to

agencies with similar programs to determine the coverage their programs achieve. As one

example, Maryland’s Coordinated Highways Action Response Team (CHART) was able to

assist with 95% of eligible reported incidents in 2017.(15) The method should predict the

number of secondary crashes that occur for both treated and untreated primary incidents

when predicting secondary crash frequency with an SSP program. This will occur in Step 6.
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Outcome

The predicted average annual number of primary incidents that will receive assistance from 

SSPs. 

5. Predict average secondary crash frequency without SSP.

Combine the probability of a secondary crash occurring without SSPs from Step 2 and the

predicted average primary incident frequency from Step 3 to predict the average secondary

crash frequency without SSP, as shown in figure 10.

Figure 10. Equation. Prediction of the average secondary crash frequency without 

SSP. 

Outcome

The predicted average secondary crash frequency without an SSP program. 
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6. Predict average secondary crash frequency with SSP.

Combine the probability of a secondary crash occurring with and without SSPs from Step 2,

the predicted average primary incident frequency from Step 3, and the proportion of

primary incidents which will receive a response from SSPs from Step 4. The total predicted

average secondary crash frequency with SSPs is the sum of predicted average secondary

crash frequency for primary incidents assisted by SSPs and primary incidents not assisted by

SSPs, as shown in figure 11.

Figure 11. Equation. Sum of predicted average secondary crash frequency. 

Outcome

The predicted average secondary crash frequency with an SSP program.
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Example – Steps 3 through 6

An agency is considering implementing an SSP program along 10 miles of freeway during all 

hours. Using a calibrated version of the safety performance functions (SPF) for freeways in 

the Highway Safety Manual (HSM), the agency predicts 400 primary crashes per year (NPC) 

on this segment of freeway. Additionally, the agency reviewed police incident reports along 

this section of freeway and determined the ratio (pOI) of other primary incidents to primary 

crashes is 0.07. Using the equation in figure 9, they predict an additional 28 primary other 

incidents per year (NOI) on the freeway section, leaving the total predicted average primary 

incident frequency (NPI) at 428 primary incidents per year as calculated using the equation in

figure 7. In total, the agency expects 25 percent of these incidents to occur in winter (107 

primary incidents) and the rest during non-winter (321 primary incidents).

Using the probabilities calculated in table 7 and the equation in figure 10, the agency 

calculated the predicted average secondary crash frequency without SSP. This calculation is 

provided below in figure 12. If the agency does not implement SSPs, they predicted 141 

secondary crashes per year on this 10-mile section of freeway.

Figure 12. Equation. Predicted average secondary crash frequency without SSP. 

The agency must now calculate the predicted average secondary crash frequency with SSPs. 

Based on consultations with neighboring agencies, the agency assumes their SSPs will 

address 95 percent of primary incidents (PSSP,Resp) within the SSP coverage area. As such, 

the agency uses the equation in figure 11 to predict average secondary crash frequency. This 

calculation is performed below in figure 13, which combines the prediction for winter and 

non-winter. If the agency implements the SSP program, they predict an average secondary 

crash frequency of 129 secondary crashes per year.

Figure 13. Equation. Combination of the prediction for winter and non-winter. 
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7. Calculate the difference in average secondary crash frequency with and without 

SSP.

The difference in average secondary crash frequency with and without SSPs represents the 

annual safety effects of the SSP program. This annual difference is calculated in figure 14.

Figure 14. Equation. Calculation for annual difference. 

Outcome

The predicted difference in average secondary crash frequency with and without an SSP 

program.

8. Monetize the difference in predicted average secondary crash frequency.

Using the equation in figure 15, estimate the monetary benefit due to the reduction in 

secondary crashes. 

Figure 15. Equation. Estimation of monetary benefit. 

The average cost of a secondary crash can be estimated by computing a weighted average of 

observed secondary crash severity. Comprehensive KABCO-level severity crash costs were 

recently updated to 2016 dollars by FHWA.(7) Additionally, Harmon et al. describe 

procedures for adjusting these costs to specific states and future years. 

The weighted average cost of secondary crashes ($SC,Avg) is based on their observed severity 

distribution. Figure 16 represents the procedure for calculating this average crash cost. This 

cost is commonly based on three to five years of secondary crash data.

Figure 16. Equation. Calculation for average crash cost. 
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$X = average cost of a secondary crash for severity X, where X can be K (fatal crash), A 

(suspected serious injury crash), B (suspected minor injury crash), C (possible injury crash), 

and O (property damage only crash). The average cost at each severity level is obtained 

from Harmon et al.(7)

NX = the number of secondary crashes for severity X during the evaluation period, where X 

can be K, A, B, C, or O.

Arizona is one state which collects data on secondary crashes. Table 8 shows the 

distribution of these secondary crashes by severity, as well as the associated costs by 

severity proposed by Harmon et al.(7) Combining these crash costs and severity distribution, 

the average comprehensive crash cost of a secondary crash would be $125,209. Agencies 

wishing to apply this method are encouraged to collected their own data on the severity of 

secondary crashes.

Table 8. Severity distribution of secondary crashes on Arizona roads from 2014-

2016. 

Severity 2014-

2016 

Crashes

Percent of 

Secondary 

Crashes

Comprehensive 

Crash Cost, 2016 

Dollars 

Fatal (K) 19 0.4% $11,295,400 

Suspected Serious Injury (A) 124 2.9% $655,000 

Suspected Minor Injury (B) 590 13.7% $198,500 

Possible Injury (C) 735 17.1% $125,600 

Property Damage Only (O) 2,837 65.9% $11,900 

Outcome

The monetary value of the expected annual reduction in secondary crashes. 

9. Estimate capital and annual costs for the SSP program.

The costs can be generated internally and can be adjusted based on findings from similar

patrol programs.

Outcome

Costs for the SSP program which will serve as the denominator for the B/C calculation.
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10.Calculate the B/C ratio for SSPs.

Using the annual benefit calculated in Step 5 and the costs calculated in Step 6, determine

the B/C ratio with the equation in figure 17. Annual costs and benefits can be amortized

over the lifetime of the program using the Uniform Series Present Worth Factor (figure 18);

discount factor and project lifetime should be assumed based on present economic

conditions and anticipated operation of the program. A B/C ratio greater than one shows

the program is expected to be economically efficient based on the reduction in secondary

crashes. The analysis could also be extended to monetize and include other potential effects

(e.g., reduced delay from non-recurring congestion, safety of occupants involved in primary

incident, or other potential benefits).

Figure 17. Equation. Calculation for the B/C ratio. 

Figure 18. Equation. Calculation for the Uniform Series Worth Factor. 

Outcome

B/C ratio to assess the potential economic efficiency of an SSP program from a safety 

perspective.
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Example – Steps 7 through 10

The agency predicted an average secondary crash frequency of 141 secondary crashes per 

year without the SSP program and 129 secondary crashes per year with the SSP program. 

The difference in predicted average secondary crash frequency, calculated using the 

equation in figure 14, is 12 secondary crashes per year. 

In order to monetize this benefit, the agency uses observed secondary crash data to 

calculate an average secondary crash cost. This average cost is calculated using the equation 

in figure 16 and the comprehensive crash costs proposed by Harmon et al.(7) 

Table 9. Secondary crashes by severity for the example agency. 

Severity Secondary 

Crashes

Percent of 

Secondary 

Crashes

Fatal (K) 30 0.4% 

Suspected Serious Injury (A) 265 2.9% 

Suspected Minor Injury (B) 483 13.7% 

Possible Injury (C) 801 17.1% 

Property Damage Only (O) 4,132 65.9% 

The comprehensive average crash cost is calculated in figure 19.

 

 

Figure 19. Equation. Comprehensive crash cost calculation. 

Given an annual reduction of 12 secondary crashes per year and an average cost of 

$132,742 per secondary crashes, the annual benefit calculated using the equation in figure 

15 is $1,592,903. The agency estimates a capital cost of $500,000 to start the program 

($CapitalCost) and an additional $400,000 per year to maintain the program ($AnnualCost), which 

is intended to operate for ten years. Over those ten years, the agency estimates an average 

monetary discount rate of 4 percent. Given a 4 percent discount rate and a ten-year project 

lifetime, the Uniform Series Present Net Worth factor (P/A) calculated using the equation 

in figure 18 is 8.11. The equation in figure 17 is used to combine these components and 

 

 

 
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calculate a B/C ratio, the example calculation is shown in figure 20.

Figure 20. Equation. Example calculation for B/C ratio. 

This analysis by the agency in this example estimates a B/C ratio of 3.45 for the proposed 

incident management program.
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SUPPORTING TOOLS 

Statistical software is required for estimating the binary logistic regression models. It is also 

required for agencies to develop incident prediction models. Statistical software with regression 

models can be found for free or at a cost. As previously mentioned, analysts developing the 

models should have experience in how to assess the analysis dataset for accuracy and sample 

size, specify models, assess the model goodness of fit, and validate the model by testing its 

predictive performance on different datasets than that used to develop the model. The rest of 

the analysis steps can be executed using Microsoft Excel or another spreadsheet software.
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DOCUMENTING ANALYSIS AND REPORTING RESULTS 

Consistent documentation of analyses that inform investment decisions improves the 

transparency and defensibility of those decisions. It helps future officials, planners, and engineers 

know why and how certain decisions were made and provides data and contexts for those 

decisions. Consistent documentation also provides a valuable record in the event of public or 

legal challenges to a decision. Documentation of the analysis described in this document should 

generally include:

• A description of the anticipated program;

• A summary of similar programs (if identified);

• Descriptive statistics for each model developed;

• A list of justified assumptions (listed below);

• The estimated incident frequency model;

• The estimated secondary crash probability model;

• Crash costs, project costs, and economic data; and

• A summary of the results.

The following assumptions are necessary for this analysis and should form part of the analysis 

documentation: 

• The proportion of primary crashes which will be serviced by the SSPs;

• The average primary incident clearance times with and without SSPs;

• The crash cost(s) used;

• The estimated initial and annual costs for the patrol program; and

• The discount rate and service life of the patrol program.

In addition, the procedure in this document has the following methodological assumptions: 

• The method considers the probability of one secondary crash as a result of a primary

incident. Multiple secondary crashes to the same primary incident are not considered.

• The method considers the primary indirect effect of an SSP program on secondary

crashes, where SSPs reduce primary incident clearance time, which in turn reduces the

probability of a secondary crash. Other direct and indirect effects of SSPs on secondary

crash frequency are not considered.

• The method assumes that the crash prediction model applied in Step 3 predicts the

frequency of primary crashes. To meet this assumption, analysts would use only primary

crashes when estimating or calibrating a primary crash prediction model.

• The example in this document assumed no traffic growth, and thus no increase in the

average annual number of crashes as a result of traffic growth. An analyst wishing to

account for traffic growth would do so in the prediction of primary crashes in Step 3.

The analyst would predict primary crash frequency for each year in the analysis period
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separately (accounting for changes in volume), then sum the predictions and divide by 

the number of years in the analysis period for an average primary crash frequency. 
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BROADER APPLICATIONS

This document outlines a method for estimating the effects of SSPs on the number of secondary 

crashes. The result of the analysis is a B/C ratio based on the expected change in secondary 

crash costs and the anticipated costs of SSP operation. Because the SSP effects on secondary 

crashes are derived from reductions in primary traffic incident clearance times, the method can 

also be extended to other TIM tactics or functions that are aimed at reducing primary incident 

clearance times. Examples include integrating computer-aided dispatch into transportation 

management centers, establishing towing service agreements, and integrating transportation 

management centers with law enforcement dispatch centers and emergency operations centers.

When extended to other contexts, analysts should tailor the same general steps of this method 

to the strategy of interest. For example, the probability of a secondary crash for a given primary 

incident will depend on the specific strategy’s expected impact on primary incident clearance 

time. When predicting average primary incident frequency in Step 3, the prediction should be 

limited to the times and facilities to which the strategy of interest will have an impact. The 

proportion of primary incidents where the strategy will impact incident clearance time (Step 4) 

and the strategy implementation costs (Step 9) will also be strategy specific. 

In addition to applications for safety analyses of TIM, the use of binary logistic regression for 

short-term crash prediction has other potential applications to safety performance analyses of 

TSMO. A companion noteworthy practice report shows its application to estimating changes in 

the occurrence and severity of crashes due to implementing traffic signal coordination.

(16) A 

final report documenting a safety analysis needs assessment for TSMO synthesizes research that 

used binary logistic regression to relate traffic operational performance to safety 

performance.(17)
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REFERENCES 

1. Marquess, A., P. Desai, G. Havinoviski, & C. Heise (2017). Safety Service Patrol Priorities

and Best Practices. Report No. FHWA-HOP-16-047. Federal Highway Administration,

Washington, D.C..

2. NCHRP (n.d.). Secondary Crashes. Guidance for Implementation of Traffic Incident

Management Performance Measurement.

3. Latoski, S. P., Pal, R., & Sinha, K. C. (1999). Cost-effectiveness evaluation of Hoosier

Helper freeway service patrol. Journal of Transportation Engineering 125(5), 429-438.

4. Khattak, A., X. Wang, & H. Zhang (2012). Incident management integration tool:

dynamically predicting incident durations, secondary incident occurrence and incident

delays. IET Intelligent Transport Systems, 6(2), 204-214.

5. Goodall, N. J. (2017). Probability of Secondary Crash Occurrence on Freeways with the

Use of Private-Sector Speed Data. Transportation Research Record: Journal of the

Transportation Research Board, 2635, 11-18.

6. Yang, H., Ozbay, K., & Xie, K. (2014). Assessing the risk of secondary crashes on

highways. Journal of Safety Research, 49, 143-149.

7. Harmon, T., G. Bahar, & F. Gross (2018). Crash Costs for Highway Safety Analysis. Report

No. FHWA-SA-17-071. Federal Highway Administration, Washington, D.C.

8. Kennedy, P. (2003). A Guide to Econometrics. MIT press.

9. Li, W. & Tarko, A.P (2011). Effect of Arterial Signal Coordination on Safety.

Transportation Research Record: The Journal of the Transportation Research Board, 2237, 51-

59.

10. Lee, C., Saccomanno, F., & Hellinga, B. (2002). Analysis of Crash Precursors on

Instrumented Freeways. Transportation Research Record: Journal of the Transportation

Research Board, 1784, 1-8.

11. Abdel-Aty, M., Uddin, N., Pande, A., Abdalla, F., & Hsia, L. (2004). Predicting Freeway

Crashes From Loop Detector Data by Matched Case-Control Logistic

Regression. Transportation Research Record: Journal of the Transportation Research Board,

1897, 88-95.

12. Golob, T. F., Recker, W., & Pavlis, Y. (2008). Probabilistic Models of Freeway Safety

Performance Using Traffic Flow Data as Predictors. Safety Science, 46(9), 1306-1333.

13. Srinivisan, R., & K. Bauer (2013). Safety Performance Function Development Guide:

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

Administration, Washington, D.C.

14. Ahrens, M., & B. Evarts (2017). “Fire Department Roadway and Vehicle Incidents”.

National Fire Protection Association, Quincy, MA.

15. Maryland Department of Transportation, State Highway Administration (2018).

Performance Evaluation and Benefit Analysis for CHART in Year 2017. Maryland Department

of Transportation, State Highway Administration, Baltimore, MD.

16. Porter, R.J., Gooch, J., & Dunn, M. (2019). Safety Performance Analysis of TSMO: A Practical

Application for Assessing Traffic Signal Coordination. Report No. FHWA-SA-19-043. Federal

Highway Administration, Washington, D.C.
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17. Porter, R.J., Dunn, M., Hamilton, I., Gooch, J., & Gayah, V. (2019). Safety Analysis Needs

Assessment for Transportation Systems Management and Operations. Report No. FHWASA-19-041. Federal Highway Administration, Washington, D.C.
For More Information:

https://safety.fhwa.dot.gov/

FHWA, Office of Safety

Jerry Roche

Jerry.Roche@dot.gov

515-233-7323

FHWA-SA-19-042