**FMCSA Safety Program**

**Performance Measures**

**Intervention Model:**

**Roadside Inspection and Traffic Enforcement**

**Effectiveness Assessment**

**September 2002**

**(Updated)**

+-------------------------------------+--------------------------------+
| Prepared for:                       | **Prepared by:**               |
|                                     |                                |
| Federal Motor Carrier Safety        | John A. Volpe National         |
| Administration                      | Transportation                 |
|                                     |                                |
| Office of Data Analysis and         | Systems Center                 |
| Information Systems                 |                                |
|                                     | Motor Carrier Safety           |
| Analysis Division, MC-RIA           | Assessment Division, DTS-47    |
|                                     |                                |
| 400 Seventh Street, S.W.            | Kendall Square                 |
|                                     |                                |
| Washington, DC 20590                | Cambridge, MA 02142            |
+-------------------------------------+--------------------------------+

######### PREFACE

This report documents the methodology and results from an improved model
to measure the effectiveness of two of the key safety programs of the
Federal Motor Carrier Safety Administration (FMCSA). The research was
conducted by the Research and Special Programs Administration's (RSPA)
John A. Volpe National Transportation Systems Center (the Volpe Center)
in Cambridge, MA under a project plan agreement with the FMCSA. The work
on FMCSA Program Performance Measures addresses the requirements of the
Government Performance and Results Act (GPRA) of 1993, which obligates
federal agencies to measure the effectiveness of their programs as part
of the budget cycle process.

Work on FMCSA Program Performance Measures was initiated during FY 93.
In December 1994, a report titled "Office of Motor Carriers Safety
Program - Performance Measurement" was prepared. That report provided a
comprehensive breakdown of Office of Motor Carriers (OMC) safety
programs and activities and described about a dozen potential evaluation
models. (Note: The OMC later became the FMCSA.) Based on the OMC's
review, the Volpe Center revised the report and recommended four
evaluation models to assess the key OMC programs: roadside inspections
conducted by participating states under the Motor Carrier Safety
Assistance Program (MCSAP), on-site compliance reviews conducted by the
OMC field offices and the states, commercial vehicle traffic enforcement
also performed by the states under the MCSAP, and a comprehensive
assessment of combined effects. Two initial evaluation models covering
the roadside inspection program and the compliance review program were
described in detail in a December 1998 report titled "OMC Safety Program
Performance Measures." A review panel was convened to evaluate these
models and made recommendations for improvement. The Volpe Center
incorporated these recommendations together with other Volpe Center
defined improvements into two "second-generation" models that measure
the effectiveness of these two programs. This report describes the
implementation of the Intervention Model, which covers not only the
roadside inspection program, but also the traffic enforcement program.

At the FMCSA, the project is managed by Dale Sienicki of the Office of
Data Analysis and Information Systems, Analysis Division. The Volpe
Center project manager is Donald Wright, Chief of the Motor Carrier
Safety Assessment Division in the Office of System and Economic
Assessment. The analysis was performed at the Volpe Center by Donald
Wright, Dennis Piccolo and Emmett Harris of EG&G Services, under
contract to the Volpe Center, with assistance from Dr. Thomas M. Corsi
of the Supply Chain Management Center, Robert H. Smith School of
Business, University of Maryland, College Park, Maryland.

**TABLE OF CONTENTS**

[Section]{.underline} [Page]{.underline}

EXECUTIVE
SUMMARY\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...vii

1\.
INTRODUCTION\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
1-1

1.1. Project
Objective\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
1-1

1.2. Project
Background\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
1-1

1.3. Project
Scope\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
1-1

1.4. Report
Structure\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
1-2

2\. SAFE-MILES: INITIAL
MODEL\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
2-1

2.1. Model
Overview\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
2-1

2.1.1. Direct
Effects\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
2-1

2.1.2. Indirect
Effects\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
2-1

2.2. Model
Limitations\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
2-2

3\. INTERVENTION
MODEL\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
3-1

3.1. Model
Description\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
3-1

3.1.1. Crash Risk
Probabilities\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
3-2

3.1.2. Direct
Effects\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
3-4

3.1.3. Indirect
Effects\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
3-6

3.2. Implementation of the Intervention
Model\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
3-7

3.3. Program
Benefits\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
3-8

4\. ENHANCEMENTS, APPLICATIONS, AND
ANALYSES\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
4-1

4.1.
Introduction\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
4-1

4.2. Intervention Model
Enhancements\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
4-1

4.2.1. Strengthen Crash
Probabilities\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
4-1

4.2.2. Incorporate Hazardous Materials
Violations\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
4-2

**TABLE OF CONTENTS (continued)**

[Section]{.underline} [Page]{.underline}

4.3. Intervention Model
Applications\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
4-2

4.3.1. Carrier Class
Studies\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
4-2

4.3.2. Alternate
Treatments\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
4-2

4.4. Future Intervention Model
Analyses\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
4-2

4.5. Subsequent Model
Runs...............\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
4-3

APPENDIX A. MATHEMATICAL DESCRIPTION OF THE INTERVENTION

> MODEL\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
> A-1

A.1.
Overview\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
A-1

A.2. Intervention
Data\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
A-1

A.2.1. Roadside
Inspections\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
A-1

A.2.2. Traffic
Enforcements\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
A-2

A.3. Intervention-Level
Impact\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
A-2

A.3.1. Violation Crash Risk Probability
Profile\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
A-2

A.3.1.1. Applied to Recorded
Violations\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
A-5

A.3.1.2. Occurrences per Risk
Category\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
A-5

A.3.2. Crashes Avoided per
Intersection\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
A-6

A.3.3.
Examples\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
A-7

A.4. Program-Level
Impact\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
A-9

A.4.1. Direct-Effect
Approach\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
A-9

A.4.1.1. Primary
Determination\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
A-9

A.4.1.2. Roadside
Allowance\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
A-10

A.4.1.3.
Examples\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
A-11

**TABLE OF CONTENTS (continued)**

[Section]{.underline} [Page]{.underline}

A.4.2. Indirect-Effect
Approach\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
A-12

A.4.2.1. Primary
Determination\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
A-13

A.4.2.2. Roadside
Allowance\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
A-19

A.4.2.3.
Examples\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
A-21

A.5. Program
Benefits\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
A-25

A.5.1. Fatal and Injury Crashes
Avoided\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
A-27

A.5.2. Lives
Saved\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
A-29

A.5.3. Injuries
Avoided\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
A-29

A.5.4.
Examples\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
A-30

APPENDIX B.
VIOLATIONS\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
B-1

APPENDIX C. PROGRAM
BENEFITS\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
C-1

C.1. National Program
Benefits.....................................................................C-2

C.2. Roadside Inspection Benefits, by
State........................................................C-6

C.3. Traffic Enforcement Benefits, by
State......................................................C-10

**LIST OF ILLUSTRATIONS**

######### FIGURES

[Figure]{.underline} [Page]{.underline}

3-1. Overview of Intervention
Model\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
3-1

3-2. Violation Crash Risk Probability
Profile\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
3-2

3-3. Direct-Effect
Approach\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
3-5

3-4. Indirect-Effect
Approach\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
3-6

3-5. Program Benefits
Determination\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
3-8

A-1. Direct-Effect Approach with Roadside
Allowance\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
A-9

A-2. Indirect-Effect Approach with Roadside
Allowance\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
A-13

A-3. Program Benefits
Determination\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...
A-26

**TABLES**

[Table]{.underline} [Page]{.underline}

3-1. Relative Weights for Driver and Vehicle Violation Risk
Categories\...\...\...\...\...\...\...\...\...\...\...\...\... 3-4

3-2. Data Inputs Used to Test the
Model\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
3-7

A-1a. Lower Bound Corrected Violation Estimates to Avoid One Crash, by
Risk Category\..... A-3

A-1b. Higher Bound Corrected Violation Estimates to Avoid One Crash,

by Risk
Category\...\..................................................................................
A-4

A-2a. Lower Bound Crash Reduction
Probabilities\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\........
A-4

A-2b. Higher Bound Crash Reduction
Probabilities\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\............
A-4

A-3. Classifying Intervention Violations with the VRCCP: Two
Examples\...\...\...\...\...\...\...\...\...\... A-5

A-4. Violation Occurrences per Risk Category: Two
Examples\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
A-6

A-5. Indirect Effects Example
Data\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
A-21

B-1. Roadside Inspection
Violations\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
B-2

B-2. Traffic Enforcement
Violations\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.....
B-14

C-1a. National Program Benefits,
1998\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
C-3

C-1b. National Program Benefits,
1999\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
C-4

C-1c. National Program Benefits,
2000\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\...\....
C-5

C-2a. Mean Roadside Inspection Program Benefits by State,
1998\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.... C-7

C-2b. Mean Roadside Inspection Program Benefits by State,
1999\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.... C-8

C-2c. Mean Roadside Inspection Program Benefits by State,
2000\...\...\...\...\...\...\...\...\...\...\...\...\...\...\.... C-9

C-3a. Mean Traffic Enforcement Program Benefits by State,
1998\...\...\...\...\...\...\...\...\...\...\...\...\...\...\... C-11

**LIST OF ILLUSTRATIONS (continued)**

C-3b. Mean Traffic Enforcement Program Benefits by State,
1999\...\...\...\...\...\...\...\...\...\...\...\...\...\...\... C-12

C-3c. Mean Traffic Enforcement Program Benefits by State,
2000\...\...\...\...\...\...\...\...\...\...\...\...\...\...\... C-13

**EXECUTIVE SUMMARY**

This report describes the Intervention Model, which is intended to
provide the Federal Motor Carrier Safety Administration (FMCSA) with a
means to gauge the effectiveness of two of its more critical safety
programs -- roadside inspections and traffic enforcements -- in
preventing crashes involving interstate motor carriers and in reducing
related fatalities and injuries. The model is also intended to be a tool
that the FMCSA can use periodically to measure the relative performance
of its programs, and to analyze the effects of implementing different
program changes.

The model measures program effectiveness in terms of reductions in the
numbers of crashes involving commercial vehicles, and in the numbers of
associated fatalities and injuries. Although the methodology is believed
to be sound and roadside inspection results are judged to be complete
and accurate, the model suffers from several limitations resulting from
a lack of empirical data regarding driver behavior and the contribution
that vehicle defects and driver faults have on crash causation.
Nevertheless, the model defaults to other means (including expert
judgment) to compensate for these shortcomings and establishes a
benchmark to measure roadside inspection and traffic enforcement program
effectiveness.

The model is based on the premise that the two programs -- roadside
inspection and traffic enforcement - directly and indirectly contribute
to the reduction of crashes. As a result, the model includes two
submodels that are used for measuring these different effects. Direct
effects are based on the assumption that vehicle and/or driver defects
discovered and then corrected as the results of interventions reduce the
probability that these vehicles/drivers will be involved in subsequent
crashes. The model calculates direct-effect-prevented crashes according
to the number and type of violations detected and corrected during an
intervention.

Indirect effects are considered to be the by-products of the carriers'
increased awareness of FMCSA programs and the potential consequences
that these programs pose if steps are not taken to ensure and/or
maintain higher levels of safety. In order to measure these indirect
effects, which are essentially changes in behavior involving driver
preparation and practices and vehicle maintenance, the model calculates
responses to exposure to the programs and the resulting reduction in
potentially crash-causing violations.

Critical to the model is its ability to link vehicle and driver defects
detected during inspections and/or traffic enforcement actions to crash
probabilities. Currently available research and expert judgments
provided the basis for establishing these linkages and assigning
probabilities. Major investigations focusing on this linkage through
special large truck crash data collections and crash reconstruction
analysis are currently being sponsored by the FMCSA. The model's
structure and analysis approach will enable the incorporation of the
results of these efforts once they become available.

The initial model run calculated the 1998 effects resulting from the
roadside inspection and traffic enforcement programs. Subsequent model
runs calculated program effects for 1999 and 2000. The table below
displays the results.

######### MCSAP Program Benefits: 1998-2000[^1]

  ----------------- ----------------- ----------------- -----------------
                    1998[^2]          1999              2000

  Roadside                                              
  Inspections                                           

  Crashes Avoided   8,612             9,119             9,362

  Lives Saved       369               391               420

  Injuries Avoided  5,902             6,250             6,416

  Traffic                                               
  Enforcement                                           

  Crashes Avoided   2,800             3,021             3,306

  Lives Saved       120               130               142

  Injuries Avoided  1,919             2,071             2,265
  ----------------- ----------------- ----------------- -----------------

This model, which measures the effectiveness of the roadside inspection
and traffic enforcement programs, when combined with the Compliance
Review Impact Assessment Model, forms a powerful performance measurement
capability that will facilitate a combined-effects assessment of the
three FMCSA safety programs. The expectation is that the
combined-effects assessment results will further guide FMCSA
decision-making when directing resources to achieve optimal program
effectiveness.

# 1. INTRODUCTION

## 1.1. PROJECT OBJECTIVE

The Intervention Model is designed to provide the Federal Motor Carrier
Safety Administration (FMCSA) with a means to gauge the effectiveness of
two of its more critical safety programs -- roadside inspections and
traffic enforcements -- in preventing crashes involving interstate motor
carriers and in reducing related fatalities and injuries. The model is
also intended to be a tool that the FMCSA can use periodically to
measure the relative performance of its programs, and to analyze the
effects of implementing different program changes. Its use could provide
a basis for making resource allocation and budgeting decisions that will
help optimize the effectiveness and efficiency of the FMCSA's motor
carrier safety programs.

## 1.2. PROJECT BACKGROUND

During the 1980s, Congress passed several acts intended to strengthen
motor carrier safety regulations. This led to the implementation of
safety-oriented programs both at the federal and state levels, and an
interest in establishing methods for measuring the effectiveness of
these programs.

The Surface Transportation Assistance Act of 1982 established the Motor
Carrier Safety Assistance Program (MCSAP), a grants-in-aid program to
states, to conduct roadside inspection and traffic enforcement programs
aimed at commercial motor vehicles. The 1984 Motor Carrier Safety Act
directed the U.S. Department of Transportation (U.S. DOT) to establish
safety fitness standards for carriers. The U.S. DOT, along with the
states, responded by implementing the MCSAP to fund roadside inspection
and traffic enforcement programs, and the safety fitness determination
process and rating system (based on on-site safety audits called
compliance reviews).

## 1.3. PROJECT SCOPE

The Program Performance Measures project established and managed by the
FMCSA includes roadside inspection, traffic enforcement, and compliance
review activities and programs. This report describes the development of
a model, the Intervention Model, that is intended to measure the
effectiveness of two of the three programs - roadside inspection and
traffic enforcement - in reducing crashes and avoiding fatalities and
injuries.

It is believed that FMCSA safety program elements exert a positive
influence, causing changes in driver behavior and carrier operations
that lead to improvements in the level of motor carrier safety. At the
same time, it is recognized that motor carriers are affected by
exogenous influences, such as those attributable to the highway
environment, that may intervene, impact or have some bearing on motor
carrier safety. However, there is no accounting for these other
influences and their associated consequences (i.e., fatalities and
injuries) in this effort.

Concurrent with the development of the Intervention Model, an improved
model for measuring the effectiveness of compliance reviews (known as
the Compliance Review Impact Assessment Model) was developed and
documented. The ultimate plan is to assess the combined effects of all
three programs. In the meantime, efforts to improve these safety program
measures and models will continue independently, and the models will be
run on a recurring basis to meet program objectives of measuring
effectiveness, and to support annual budgetary planning and resource
allocation decisions.

1.4. REPORT STRUCTURE

This report includes descriptions of the evolution of the Intervention
Model, the effects that it measures, and how the model is to be applied.
The report also explains concepts driving the development process and
affecting the model structure. Report sections include:

-   Background on an earlier model, known as Safe-Miles, with an
    explanation of its limitations,

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```
-   A description of the model with results and descriptions of the
    calculation of direct and indirect effects, and

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```
-   A discussion of applications and future model enhancements.

Technical appendices have been prepared that provide a mathematical
description of the model (Appendix A), detailed information on the types
and classification of violations critical to running the model (Appendix
B), and program benefits as estimated by the model using MCSAP
inspection/violation inputs (Appendix C).

**2. SAFE-MILES: INITIAL MODEL**

2.1. MODEL OVERVIEW

The Safe-Miles Model that was also developed to measure the
effectiveness of the roadside inspection program preceded the
Intervention Model. It is discussed here by way of background, since the
Intervention Model borrows substantially from the experience with the
Safe-Miles Model. Included is a discussion of the direct and indirect
effects approach first used in that model as well as the model's
limitations leading to the development of the "second-generation"
Intervention Model.

The Safe-Miles Model employed a two-step analysis process to perform the
evaluation. Instances were recorded in which vehicles and/or drivers
were taken out of service during roadside inspections. Next, subsequent
travel by the out-of-service (OOS) vehicles and drivers, once conditions
were corrected, was converted into "safe miles" and estimates were made
concerning crashes avoided during the "safe-miles" period.

### **2.1.1. Direct Effects**

Direct-effect benefits were accumulated from the point at which vehicles
or drivers with OOS conditions were detected and removed from service. A
three-month "safe" post-inspection period for vehicles was incorporated
into the model. This time frame was considered appropriate since the
Commercial Vehicle Safety Alliance (CVSA) has a three-month period after
a vehicle receives a satisfactory inspection that it is exempt from
additional inspections.[^3] Lacking an empirical basis with which to
govern the duration of the direct effect findings for drivers, the
post-inspection safe period for corrected driver OOS defects was
shortened to a more conservative period of two months.

### **2.1.2. Indirect Effects**

Indirect effects are an equally important element of the roadside
inspection program. The very existence of the program (as well as its
magnitude) is believed to act as a deterrent. Knowledge of the program
results in motor carrier managers making procedural changes that result
in improvements in vehicle maintenance and inspection and in driver
qualifications and behavior. These indirect effects, although assumed
substantial, are much more difficult to quantify. The indirect effects
are estimated in the Safe-Miles Model by assuming that carriers with a
high frequency of (that is, greater exposure to) either vehicle or
driver inspections, as a result of the enforcement of the roadside
inspection program, change their behavior and voluntarily improve their
safety, resulting in lower vehicle or driver OOS rates.

Direct effects (crashes avoided) were added to indirect effects to
derive total roadside inspection program benefits. These benefits were
also expressed as estimates in dollar terms by using crash cost factors.
There was no traffic enforcement component in the Safe-Miles Model.

## 2.2. MODEL LIMITATIONS

The 1998 Volpe Center report - "OMC Safety Program Performance
Measures" - identified the following limitations associated with the
Safe-Miles Model:

-   No observed evidence existed for the establishment of a driver
    safe-miles period. In future empirical studies of driver behavior,
    post-OOS violation detection would be required to establish the
    reliability of the two-month interval that was used.

```{=html}
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```
-   Each violation was considered in isolation. This precluded any
    heightening of the safety risk as a result of the presence of
    multiple violations found during an inspection.

```{=html}
<!-- -->
```
-   The lack of crash causation statistics hindered the ability to
    estimate the contribution of specified vehicle and driver defects to
    crash likelihood.

The deterrence component of the model (indirect effects) relied on
measured changes in OOS rates of carriers that had multiple inspections
as a foundation for calculating indirect effects from roadside
inspections. However, overall improved preparation and compliance of
drivers and vehicles motivated by the presence of a roadside inspection
program were thought to be greater than improvements that could be
measured by the model.

The research team defined the Intervention Model as a means to remedy
these limitations. As with the Safe-Miles Model, the Intervention Model
includes direct and indirect effect components; however, it:

-   Eliminates the empirically weak "safe-miles" concept,

```{=html}
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```
-   Makes allowances for inspections with multiple violations, and

```{=html}
<!-- -->
```
-   Uses the latest available crash causation statistics to estimate the
    contribution of vehicle and driver faults to crash causation.

The model also considers **total** inspection results. This means that
it includes non-OOS violations, although with a lesser-assigned weight,
in its calculations. Finally, the Intervention model remedies a
Safe-Miles omission by including MCSAP program traffic enforcements in
its analysis. The benefits of the Intervention Model are expressed as
fatalities and injuries avoided as well as crashes avoided.

**3. INTERVENTION MODEL**

3.1. MODEL DESCRIPTION

The Intervention Model was developed to determine the effectiveness of
the MCSAP roadside inspection and traffic enforcement programs in
reducing motor carrier crashes. The roadside inspection program consists
of roadside inspections performed by qualified safety inspectors
following the guidelines of the North American Standard, which was
developed by the Commercial Vehicle Safety Alliance in cooperation with
the FMCSA. Most roadside inspections by the states are conducted under a
grant program (MCSAP) administered by the FMCSA. There are five levels
of inspections including a vehicle component, a driver component or
both. The traffic enforcement program is based on the enforcement of
twenty-one moving violations noted in conjunction with a roadside
inspection. Violations are included in the driver violation portion of
the roadside inspection checklist.[^4]

Figure 3-1 provides an overview of the Intervention Model. The diagram
broadly illustrates:

-   How the model begins with raw inspection violation data;

```{=html}
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```
-   Proceeds to the submodels, where separate algorithms are run to
    determine the direct and indirect effects; and

```{=html}
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```
-   ![](media/image1.wmf)Culminates, finally, with the calculation of
    program benefits for the respective programs. (For a mathematical
    description of the model, see Appendix A.)

######### Figure 3-1. Overview of Intervention Model

As with the Safe-Miles Model, this model is based on the premise that
the two programs -- roadside inspection and traffic enforcement -
directly and indirectly contribute to the reduction of crashes. As a
result, the model includes two submodels that are used for measuring
these different effects. Direct effects are based on the assumption that
vehicle and/or driver defects discovered and then corrected as the
results of interventions reduce the probability that these
vehicles/drivers will be involved in subsequent crashes. Indirect
effects are considered to be the by-products of the carriers' increased
awareness of FMCSA programs and the potential consequences that these
programs pose if steps are not taken to ensure and/or maintain high
levels of safety.

3.1.1. Crash Risk Probabilities

![](media/image1.wmf)In the model, the assumption is made that observed
deficiencies (OOS and non-OOS violations) discovered at the time of
roadside inspections and/or traffic enforcements can be converted into
crash risk probabilities. This assumption is based on the premise that
detected defects represent varying degrees of mechanical or judgmental
faults, and, further, that some are more likely than others to play a
contributory role in motor vehicle crashes. The assumption is that these
deficiencies can be noted and ranked into discrete risk categories, each
of which possesses a probability that reflects the crash risk that it
poses. The process by which the resulting Violation Crash Risk
Probability Profile (VCRRP) is formed appears in Figure 3-2.

Figure 3-2. Violation Crash Risk Probability Profile

The development of risk categories for violations relied upon a recent
study conducted by Cycla Corporation.[^5] Each violation was classified
according to the risk caused by the conditions of the violation. Cycla's
report defined risk as "the likelihood of a violation leading to a
crash" and, subsequently, divided the violations into five categories
based on the level of risk. The risk categories and their descriptions
are as follows:

> Risk Category 1 -- The violation is the *potential single, immediate*
> factor leading to a crash or fatalities/injuries from a given crash.
>
> Risk Category 2 -- The violation is the *potential single, eventual*
> factor leading to a crash or fatalities/injuries from a given crash.
>
> Risk Category 3 -- The violation is a *potential contributing* factor
> leading to a crash or fatalities/injuries from a given crash.
>
> Risk Category 4 -- The violation is an *unlikely potential
> contributing* factor leading to a crash or fatalities/injuries from a
> given crash
>
> Risk Category 5 -- The violation has *little or no connection* to
> crashes or the prevention of fatalities/injuries.

While covering most inspection violations, Cycla's assignment of
violations to risk categories was incomplete. This required Volpe Center
analysts to make violation assignments for those driver or vehicle
violations not included in the Cycla risk assessment. These assignments
were made based on comparability with the Cycla list.

In the Cycla study, recommended weights were given to each of the risk
categories, as shown in Table 3-1. The heaviest weight (1,000) was
assigned to Risk Category 1 since these violations are considered to
represent a significant safety hazard. Risk Categories 2 through 5 were
given lesser weights (100, 10, 1, and 0.1, respectively). Cycla
justifies this by stating that since "each relative numerical weight
represents a different order of magnitude of likelihood, the weights
decrease by a factor of ten." The Cycla study cautions, however, that
the values do not refer to any "absolute" risk level. (The detailed list
of roadside inspection violations and traffic enforcement violations,
and associated risk categories appears in Tables B-1 and B-2 in Appendix
B. Each table indicates the source of the categorization - either Cycla
or Volpe Center.)

To execute the model, Volpe Center analysts converted Cycla's relative
numerical weights into crash reduction probabilities.[^6] Each
probability is an estimate of the portion of a crash avoided when an
inspection uncovers a particular violation. For example, if a violation
carried a probability of 0.001, inspectors would have to discover that
violation 1,000 times in order for the model to "take credit" for
avoiding a crash. Since driver-related errors are thought to be more of
a factor in crash causation relative to mechanical defects, traffic
enforcement violations were assigned higher probabilities. In fact, a 4
to 1 ratio separates the two types of violations based on expert
judgments formed from the results of previous studies and available
data.[^7]

**Table 3-1. Relative Weights for Driver and Vehicle Violation Risk
Categories**[^8]

  ------------ --------------------------------------------------- ---------------
  **Risk                                                           **Relative
  Category**                                                       Weight**

  **1**        **Violation** is the potential single, immediate    1,000
               factor leading to a crash or fatalities/injuries    
               from a given crash.                                 

  **2**        **Violation** is the potential single, eventual     100
               factor leading to a crash or fatalities/injuries    
               from a given crash.                                 

  **3**        **Violation** is a potential contributing factor    10
               leading to a crash or fatalities/injuries from a    
               given crash.                                        

  **4**        **Violation** is an unlikely potential contributing 1
               factor leading to a crash or fatalities/injuries    
               from a given crash.                                 

  **5**        **Violation** has little or no connection to        0.1
               crashes or the prevention of fatalities/injuries.   
  ------------ --------------------------------------------------- ---------------

3.1.2. Direct Effects

This section describes the methodology employed to estimate the number
of direct-effect crashes avoided.

Conceptually, the approach at the heart of the Direct Effects Submodel
is straightforward. Since the occurrence of a single violation implies a
certain degree of crash risk, each inspection that uncovers at least one
violation can be interpreted as having reduced the risk linked with its
noted violation(s). The model expresses this risk reduction in terms of
the likelihood of a crash being avoided by each inspection violation
that was noted and corrected. For an individual intervention, the
avoided crash probability will be dependent upon the number and type of
violations. Multiple violations, of course, will have a compounding
effect, thereby increasing the likelihood of a prevented crash. By
accounting separately for the two types of violations (roadside and
traffic enforcement) and summing the portions of crashes avoided for all
inspections within each group, it is possible to estimate direct-effect
crashes that have been avoided due to the *programs*.

Figure 3-3 depicts the process used to determine program direct effects.

![](media/image2.wmf)

######### Figure 3-3. Direct-Effect Approach

Four steps make-up the direct-effect approach.

-   **Step 1** - One year of inspection data is extracted from the Motor
    Carrier Management Information System (MCMIS) database. The MCMIS
    contains information compiled from federal and state safety
    agencies. Each intervention has its own set of associated driver
    and/or vehicle violations.

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-   **Step 2** - An inspection's violations are matched to the Violation
    Crash Risk Probability Profile, whereby a list of crash reduction
    probabilities becomes attached to that inspection. This list becomes
    the basis for calculating the inspection's effect on avoiding a
    crash.

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-   **Step 3** - The likelihood of an avoided crash for each inspection
    is calculated by using the crash reduction probabilities of the
    inspection. An inspection with multiple violations will have a
    greater likelihood of an avoided crash than will an inspection with
    a single violation. This result reflects the belief that multiple
    violations compound the safety hazard posed from driver deficiencies
    and/or vehicle defects.

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-   **Step 4** - Once each inspection has been assigned its probability
    of avoiding a crash, the inspections are grouped by their initiating
    intervention. An inspection with a traffic enforcement driver
    violation is classified as traffic enforcement with a driver and/or
    vehicle roadside inspection component(s). All other inspections are
    classified as entirely driver and/or vehicle roadside inspections.
    Direct-effect crashes-avoided totals are simply the summation of 1)
    the portions of crashes avoided for all traffic enforcement
    violations and 2) the summation of the portions of crashes avoided
    for all roadside inspection violations.

3.1.3. Indirect Effects

The fundamental premise of the indirect-effect approach is that once
carriers have been exposed to the combination of roadside inspection and
traffic enforcement actions, they will change their behavior. This
change in behavior will result in higher levels of compliance, fewer
future violations, and, therefore, a reduction in the number of crashes.
This section presents a summary of the methods used in the model to
arrive at program indirect effects. The deterrent-effects part of the
model -- that is, the Indirect Effects Submodel - follows a similar
pattern to that of the Direct Effects Submodel.

Indirect effects, by their nature, defy measurement. However, changes in
behavior represented by changes in the number of violations recorded for
a carrier over time can be used to identify and evaluate the *result*s
of the indirect effects. In other words, if a carrier receives fewer and
fewer violations as it is subjected to more inspections, it will be
determined that compliance behavior has been affected and the resulting
likelihood of crashes has been reduced. To measure these effects,
multiple successive years of intervention data are required.

The Indirect Effects Submodel compares the results of inspections
carrier by carrier from one year to the next in order to measure the
effects of the exposure to having inspections on compliance. A carrier's
performance in a base year is compared to its performance in a
subsequent year. What is sought is an improvement, i.e., a reduction, in
the likelihood of a crash resulting from increasingly fewer violations
being recorded. The difference between the totals is calculated as the
indirect-effect crashes-avoided effect. Depending upon the initiating
intervention, it is tallied as indirect-effect crashes avoided for
either the roadside inspection or traffic enforcement programs.

Figure 3-4 illustrates the processes involved in assessing the indirect
effects of the model.

![](media/image3.wmf){width="3.9902777777777776in"
height="2.3208333333333333in"}

######### Figure 3-4. Indirect-Effect Approach

The indirect effects calculation is similar to that of the direct
effects. **Steps 1** and **2** are equivalent, with one exception, to
their counterparts in the Direct Effects Submodel. The Indirect Effects
Submodel uses two years of MCMIS intervention data, whereas the Direct
Effects Submodel uses one. **Step 3** creates year one and year two
average fractional crashes-avoided figures for each carrier. The two
figures are compared and improvements are noted. **Step 4** separates
inspections and attributes the results to the initiating intervention.
Traffic enforcement driver moving violations are assigned to the traffic
enforcement program. All others (including driver and vehicle
inspections done in conjunction with traffic stops) are assigned to the
roadside inspection program. Indirect-effect crashes-avoided totals are
the summation of the improvements in calculated crashes avoided.[^9]

3.2. IMPLEMENTATION OF THE INTERVENTION MODEL

The use of the model requires intervention data inputs (as discussed in
the submodel sections) in order to produce estimates of the numbers of
crashes avoided that are attributable to the roadside inspection and
traffic enforcement programs. For the purpose of testing the model, 1998
data was used, as shown in Table 3-2.

Table 3-2. Data Inputs Used to Test the Model

  ---------------------------------------------------- -------------------
                                                       **1998**[^10]

  Total Interventions                                  2,217,000[^11]

  Roadside Inspections with No Violations              572,000

  Roadside Inspections with Violations                 1,129,000

  Traffic Enforcements with Violations                 516,000
  ---------------------------------------------------- -------------------

The Direct Effects Submodel yielded a mean estimate of 6,995 motor
carrier crashes avoided as a result of the roadside inspection program
in 1998, and another 2,331 crashes avoided due to the traffic
enforcement program. The Indirect Effects Submodel, using the same 1998
input data, produced mean estimates of 1,617 roadside inspection and 469
traffic enforcement crashes avoided. Summation of the submodel totals
provided estimates of the overall roadside inspection and traffic
enforcement program results. Thus, the total numbers of crashes avoided
in 1998 by the roadside inspection program and the traffic enforcement
program were 8,612 and 2,800, respectively.[^12]

3.3. PROGRAM BENEFITS

The model also estimates program benefits expressed in terms of lives
saved and injuries avoided. Figure 3-5 illustrates the overall approach
that is used by the model to determine these program benefits that are
attributable to the roadside inspection and traffic enforcement
programs.

![](media/image4.wmf)

Figure 3-5. Program Benefits Determination

Continuing with the 1998 data, the model converted the 8,612 crashes
avoided by the roadside inspection program into program benefits of 369
lives saved and 5,902 injuries avoided. Likewise, the model converted
the estimate of 2,800 crashes avoided as a result of the traffic
enforcement into 120 lives saved and 1,919 injuries avoided. The set of
tables in Section C.1 of the Appendix displays model-calculated national
program results for calendar year 1998, as well as subsequent years for
which the model was run.

The model's flexibility lends itself to finer divisions of examination,
such as scrutiny by state, which then can be used to guide the
allocation of MCSAP resources and the design of state truck safety
programs. The tables of Sections C.2 and C.3 in Appendix C show the
estimated program benefits resulting from each state's MCSAP programs.
Section C.2 tables show output from the model for state roadside
inspections and the Section C.3 tables summarize traffic enforcement
results.

**4. ENHANCEMENTS, APPLICATIONS, AND ANALYSES**

4.1. INTRODUCTION

Additional model improvements are planned. They include improving the
model inputs, such as the crash probabilities, and conducting additional
assessments and analyses leading to improved application practices. Some
of these improvements include:

-   employing the results of planned studies of crash causation to
    improve crash probabilities, and capturing the compounding impact of
    multiple defects,

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-   incorporating hazardous materials violations, and the potential
    effect of these violations, particularly when combined with driver
    and vehicle effects, and

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-   determining the effectiveness of the programs in reducing crashes
    among different carrier classes allowing for an improved "targeting"
    of resources.

Besides implementing model enhancements that will improve the
measurement of the effectiveness of the roadside inspection and traffic
enforcement programs, there will be ongoing efforts to examine how the
model fits into a combined effects assessment of the three major FMCSA
programs (including the compliance review (CR) program). Work will be
initiated to establish an approach using the Intervention Model and the
Compliance Review Impact Assessment Model to examine the combined
effects and relative separate effectiveness of the programs.

**4.2. INTERVENTION MODEL ENHANCEMENTS**

4.2.1. Strengthen Crash Probabilities

The Intervention Model is conservative in developing crash risk
reduction probability estimates for individual violations as well as for
individual inspections with multiple violations. Though the model
clearly recognizes that multiple vehicle and driver problems occurring
simultaneously greatly enhance the likelihood of a future crash, more
empirical data on the compounding impact of multiple defects could
result in much more accurate estimates of crash probabilities.

While the Cycla effort to differentiate among violations based on their
respective risk category provides a means to estimate the prospect that
a crash would occur had the vehicle/driver not been stopped, further
data on linkages between vehicle/driver problems and crash occurrences
would improve the model's accuracy. The FMCSA and the National Highway
Traffic Safety Administration (NHTSA) are currently conducting detailed
post-crash investigations on a sample of crashes.[^13] The objective of
this study is to obtain information on the connections between
vehicle/driver problems and crash causation.

4.2.2. Incorporate Hazardous Materials Violations

Another enhancement that will be made during future model runs is the
effect of hazardous materials violations. Currently, the model does not
address the issue of hazardous materials violations discovered during
inspections or the effects that these violations (particularly when
combined with driver and vehicle effects) may have on causing crashes or
increasing the severity of crashes. This refinement is clearly
warranted, given the potential effects of hazardous materials
violations, especially when combined with vehicle and driver violations.

4.3. INTERVENTION MODEL APPLICATIONS

4.3.1. Carrier Class Studies

By using motor carrier categories, or classes, such as those developed
by Dr. Thomas Corsi of the Robert H. Smith School of Business at the
University of Maryland, the model can be used to study program
effectiveness among carrier classes. Differences in fleet size, driver
age, length of haul, etc., may contribute to differences in
direct-effect and indirect-effect program impacts. A better
understanding of carrier classes and how they react to interventions
will aid in the application and development of the roadside inspection
and traffic enforcement programs.

4.3.2. Alternate Treatments

As a corollary to the investigation of carrier types, alternate forms of
treatment to reduce crashes should be sought. If patterns were to be
discovered in particular strata of carriers, then the proposal and
implementation of effective means of addressing these groups would
become critical in the effort to increase the number of lives saved and
injuries avoided from intervention programs.

4.4. FUTURE INTERVENTION MODEL ANALYSES

The model is designed to be used as an ongoing measurement tool. It is
anticipated that initial runs of the model will generate benchmarks that
will assist in tracking program performance over time. In particular,
emphasis should be placed on assessing the indirect effects component of
the model, since it is the portion of the model that analyzes the
effects that have an impact on *future* carrier behavior. Additional
years of data would serve to substantiate the concept of the deterrence
effect and improve the measurement of that effect as well.

Finally, the results of the model are to be employed in a comprehensive
assessment of the combined effects of all MCSAP safety programs. It is
expected that combining the results of both the Compliance Review Impact
Assessment and Intervention Models will create a more powerful program
effectiveness measurement capability, which will enable the FMCSA to
meet the requirements of the Government Performance and Results Act of
1993. The FMCSA will also employ this enhanced capability to improve the
safety programs.

4.5. SUBSEQUENT MODEL RUNS

Upon completion of the Model's initial testing, two subsequent Model
runs were performed to determine program benefits in 1999 and 2000.
These runs were to produce program performance benchmarks and act as a
final test of the Model's ability to measure program performance across
multiple years.

Analysis of the new results showed an unanticipated drop in program
benefits between 1998 and 1999. This occurred despite an increase in the
overall number of interventions carried out in 1999. Further
investigation of the underlying data was undertaken to discover the
source of this apparent anomaly (e.g., an actual downward trend in
program benefits, instability of the Model, etc.).

Examination of the data uncovered a reporting inconsistency with one of
the general driver violations (392.2D - "local laws/other driver
violations"). Usage of this violation dropped dramatically between 1998
and 1999 and was offset by a concurrent increase in usage of another
general driver violation (392.2 - "local laws/ general"). Whereas 392.2D
was assigned to Risk Category 1, violation 392.2, as well as all other
general driver violations, was classified as Risk Category 2.
Consequently, Volpe Center staff opted to reclassify 392.2D as Risk
Category 2 because a) it is a nonspecific violation that obscures the
potential hazard of the behavior being cited, and b) doing so conforms
with the classification established for other general driver violations.

A new set of Model runs was completed after the reclassification for
calendar years 1998, 1999, and 2000. The updated results appear in
Appendix C.

######### APPENDIX A. MATHEMATICAL DESCRIPTION OF THE INTERVENTION MODEL

A.1. OVERVIEW

The Intervention Model measures the effectiveness of the MCSAP roadside
inspection and commercial vehicle traffic enforcement programs.[^14]
Effectiveness, for the purposes of this analysis, is defined as the
estimated reduction in motor carrier crashes attributable to the
existence and implementation of the aforementioned MCSAP safety
programs. The model is a key element of the FMCSA's Program Performance
Measures project.

This appendix presents a more detailed description of the model than
that provided in the preceding text. It also contains mathematical
explanations of the algorithms employed in the model.

A.2. INTERVENTION DATA

Raw intervention data serve as the inputs from which all further
determinations flow. The data consist of individual records of roadside
inspections and traffic enforcements carried out during a given period.
The model creates a crashes-avoided figure for each intervention based
on the number and type of violations present.

A.2.1. Roadside Inspections

Roadside inspections are interventions performed by qualified safety
inspectors at fixed roadside locations (e.g., weigh stations) using
North American Standard (NAS) guidelines.[^15] The NAS is a vehicle and
driver inspection structure established by the FMCSA and the Commercial
Vehicle Safety Alliance. A checklist of each roadside inspection lists
uncovered violations of safety regulations.

**A.2.2. Traffic Enforcements**

MCSAP traffic enforcements are a subset of traffic enforcements in
general.[^16] MCSAP traffic enforcements include only those enforcement
stops that lead to an on-the-spot roadside inspection. The enforcement
agent, if qualified, performs the subsequent roadside inspection.
Otherwise, a safety inspector is called to the scene to conduct it.
Since a traffic infraction precipitates the ensuing roadside inspection,
21 moving violations are incorporated into the driver section of the
roadside checklist. The model classifies an intervention as traffic
enforcement when at least one traffic violation is present in the
intervention record.

A.3. INTERVENTION-LEVEL IMPACT

As the name implies, the Intervention Model places a great deal of
importance on individual interventions. The reason for this is that
violation tabulations come from interventions and those tabulations are
matched against a Violation Crash Risk Probability Profile, which then
serves as a basis for determining the number of crashes avoided for a
given intervention. Aggregates developed from the intervention-level
crashes avoided numbers eventually form national and state statistics.

A.3.1. Violation Crash Risk Probability Profile

The model assumes that observed deficiencies (OOS and non-OOS
violations) can be converted into crash risk probabilities. This
assumption is based on the belief that detected defects represent
varying degrees of mechanical or judgmental faults and, as a result,
some are more likely than others to play contributory roles in causing
motor carrier crashes. These differences can be estimated and ranked
into discrete risk categories. Thus, the Violation Crash Risk
Probability Profile (VCRPP) contains all violation codes, each with an
assigned risk category and a corresponding crash probability.

Using Cycla's risk categories and the relative weights assigned to the
categories, the Volpe Center analysts sought to account for error
margins by opting for two probability sets -- a Higher Bound set and a
Lower Bound set. The outputs computed from the two sets are used to
compute a mean with a range of ± 20 percent. Because crash causation
data is still forthcoming, users are reminded to employ caution
interpreting the Model's results.

The figures in Tables A-1a and A-1b indicate the Higher Bound and Lower
Bound numbers of violations that would have to be discovered to cause
the model to credit one of the programs with an avoided crash. Keep in
mind, however, the numbers in the tables are not meant to be definitive.
They constitute the best guesses of industry experts interpreting
available data. Volpe Center analysts used these figures to test and
calibrate the model. As more reliable crash causation statistics become
available, table quantities may have to be revised.[^17] These revisions
will not affect the overall soundness of the model.

Note that in moving from Risk Category (RC) 1 to RC 2, from RC 2 to RC
3, and so on, each step varies by a factor of ten. This tracks Cycla's
variation in designated relative weights between risk categories. Note
further that the weight given to uncovered traffic enforcement
violations is four times that of the roadside inspection counterpart
violations. Tables A-1a and A-1b illustrate the factor and weighting
differences. For example, the tenfold factor variation can be seen when
Traffic Enforcement RC1 OOS Violations jump from 30 to 300 when stepping
to Traffic Enforcement OOS Violations RC2. Additionally, it takes
quadruple the number of Roadside Inspection OOS Violations in RC1 (120)
to have the same impact as Traffic Enforcement OOS Violations in RC1
(30), demonstrating the reduced weight given to roadside inspection
violations vis-à-vis traffic enforcement violations. Volpe Center
analysts used the latest, preliminary data available from ongoing crash
causation studies to support this difference. The studies found that
driver faults represented by traffic enforcement violations are more
likely to lead to motor carrier crashes than are roadside-inspection
driver or vehicle faults of an equivalent risk category.[^18]

######### Table A-1a. Lower Bound Corrected Violation Estimates to Avoid One Crash, 

######### by Risk Category

+-------------+------------+--------------+------------+--------------+
| #           | **Roadside |              | **Traffic  |              |
| ######## Ri | In         |              | Enf        |              |
| sk Category | spection** |              | orcement** |              |
+-------------+------------+--------------+------------+--------------+
|             | Number of  |              | Number of  |              |
|             | Violations |              | Violations |              |
+-------------+------------+--------------+------------+--------------+
|             | OOS        | Non-OOS      | OOS        | Non-OOS      |
|             |            |              |            |              |
|             | Violations | Violations   | Violations | Violations   |
+-------------+------------+--------------+------------+--------------+
| 1           | 120        | 240          | 30         | 60           |
+-------------+------------+--------------+------------+--------------+
| 2           | 1,200      | 2,400        | 300        | 600          |
+-------------+------------+--------------+------------+--------------+
| 3           | 12,000     | 24,000       | 3,000      | 6,000        |
+-------------+------------+--------------+------------+--------------+
| 4           | 120,000    | 240,000      | 30,000     | 60,000       |
+-------------+------------+--------------+------------+--------------+
| 5           | 1,200,000  | 2,400,000    | 300,000    | 600,000      |
+-------------+------------+--------------+------------+--------------+

Table A-1b. Higher Bound Corrected Violation Estimates to Avoid One
Crash,

by Risk Category

+-------------+------------+--------------+------------+--------------+
| #           | **Roadside |              | **Traffic  |              |
| ######## Ri | In         |              | Enf        |              |
| sk Category | spection** |              | orcement** |              |
+-------------+------------+--------------+------------+--------------+
|             | Number of  |              | Number of  |              |
|             | Violations |              | Violations |              |
+-------------+------------+--------------+------------+--------------+
|             | OOS        | Non-OOS      | OOS        | Non-OOS      |
|             |            |              |            |              |
|             | Violations | Violations   | Violations | Violations   |
+-------------+------------+--------------+------------+--------------+
| 1           | 80         | 160          | 20         | 40           |
+-------------+------------+--------------+------------+--------------+
| 2           | 800        | 1,600        | 200        | 400          |
+-------------+------------+--------------+------------+--------------+
| 3           | 8,000      | 16,000       | 2,000      | 4,000        |
+-------------+------------+--------------+------------+--------------+
| 4           | 80,000     | 160,000      | 20,000     | 40,000       |
+-------------+------------+--------------+------------+--------------+
| 5           | 800,000    | 1,600,000    | 200,000    | 400,000      |
+-------------+------------+--------------+------------+--------------+

Tables A-2a and A-2b display the higher bound and lower bound
probabilities, respectively. The crash reduction probabilities are the
reciprocals of the numbers in Tables A-1a and A-1b, so it follows that
the probabilities also experience a tenfold change between steps. The
crash reduction probabilities associated with each violation form the
VCRPP.

Table A-2a. Lower Bound Crash Reduction Probabilities

+-------------+-----------+--------------+------------+--------------+
| #           | *         |              | **Traffic  |              |
| ######## Ri | *Roadside |              | Enf        |              |
| sk Category | Ins       |              | orcement** |              |
|             | pection** |              |            |              |
+-------------+-----------+--------------+------------+--------------+
|             | Crash     |              | Crash      |              |
|             | Reduction |              | Reduction  |              |
|             | Pr        |              | P          |              |
|             | obability |              | robability |              |
+-------------+-----------+--------------+------------+--------------+
|             | OOS       | Non-OOS      | OOS        | Non-OOS      |
|             |           |              |            |              |
|             | V         | Violations   | Violations | Violations   |
|             | iolations |              |            |              |
+-------------+-----------+--------------+------------+--------------+
| 1           | .00833    | .004167      | .033       | .0167        |
+-------------+-----------+--------------+------------+--------------+
| 2           | .000833   | .0004167     | .0033      | .00167       |
+-------------+-----------+--------------+------------+--------------+
| 3           | .0000833  | .00004167    | .00033     | .000167      |
+-------------+-----------+--------------+------------+--------------+
| 4           | .00000833 | .000004167   | .000033    | .0000167     |
+-------------+-----------+--------------+------------+--------------+
| 5           | .         | .0000004167  | .0000033   | .00000167    |
|             | 000000833 |              |            |              |
+-------------+-----------+--------------+------------+--------------+

Table A-2b. Higher Bound Crash Reduction Probabilities

+-------------+-----------+--------------+------------+--------------+
| #           | *         |              | **Traffic  |              |
| ######## Ri | *Roadside |              | Enf        |              |
| sk Category | Ins       |              | orcement** |              |
|             | pection** |              |            |              |
+-------------+-----------+--------------+------------+--------------+
|             | Crash     |              | Crash      |              |
|             | Reduction |              | Reduction  |              |
|             | Pr        |              | P          |              |
|             | obability |              | robability |              |
+-------------+-----------+--------------+------------+--------------+
|             | OOS       | Non-OOS      | OOS        | Non-OOS      |
|             |           |              |            |              |
|             | V         | Violations   | Violations | Violations   |
|             | iolations |              |            |              |
+-------------+-----------+--------------+------------+--------------+
| 1           | .0125     | .00625       | .05        | .025         |
+-------------+-----------+--------------+------------+--------------+
| 2           | .00125    | .000625      | .005       | .0025        |
+-------------+-----------+--------------+------------+--------------+
| 3           | .000125   | .0000625     | .0005      | .00025       |
+-------------+-----------+--------------+------------+--------------+
| 4           | .0000125  | .00000625    | .00005     | .000025      |
+-------------+-----------+--------------+------------+--------------+
| 5           | .00000125 | .000000625   | .000005    | .0000025     |
+-------------+-----------+--------------+------------+--------------+

A.3.1.1. Applied to Recorded Violations

Because each inspection used in the analysis has one or more violations,
the model classifies recorded violations according to their VCRPP
ratings. Table A-3 displays the classification process for two example
inspections.

Inspection A is a roadside-initiated intervention, since no traffic
enforcement violations are present. It contains roadside RC 1 OOS
violations and both OOS and non-OOS RC 2 violations. Using the VCRPP,
the violations receive their respective probabilities from the Higher
Bound and Lower Bound probability sets.

The VCRPP is also applied to Inspection B. Unlike Inspection A,
Inspection B is classified as a traffic enforcement-initiated
intervention, because it has at least one traffic enforcement violation.
Additionally, several roadside violations were identified during the
subsequent roadside inspection.

**Table A-3. Classifying Intervention Violations with the VCRPP: Two
Examples**

![](media/image5.wmf)

**A.3.1.2. Occurrences per Risk Category**

After the application of the VCRPP, the model aggregates violations
occurring in a particular risk category. Table A-4 continues with the
example interventions from Table A-3 by exhibiting the results of the
aggregation.

**Table A-4. Violation Occurrences per Risk Category: Two
Examples**[^19]

+-----------+------+------+----+-------+----+--------+----+--------+
| I         | #    |      |    |       | *  |        |    |        |
| nspection | #### |      |    |       | *T |        |    |        |
|           | #### |      |    |       | ra |        |    |        |
|           |  Roa |      |    |       | ff |        |    |        |
|           | dsid |      |    |       | ic |        |    |        |
|           | e In |      |    |       | E  |        |    |        |
|           | spec |      |    |       | nf |        |    |        |
|           | tion |      |    |       | or |        |    |        |
|           |      |      |    |       | ce |        |    |        |
|           |      |      |    |       | me |        |    |        |
|           |      |      |    |       | nt |        |    |        |
|           |      |      |    |       | ** |        |    |        |
+-----------+------+------+----+-------+----+--------+----+--------+
|           | Risk |      | Ri |       | Ri |        | Ri |        |
|           | Cate |      | sk |       | sk |        | sk |        |
|           | gory |      | Ca |       | Ca |        | Ca |        |
|           | 1    |      | te |       | te |        | te |        |
|           |      |      | go |       | go |        | go |        |
|           | Vi   |      | ry |       | ry |        | ry |        |
|           | olat |      | 2  |       | 1  |        | 2  |        |
|           | ions |      |    |       |    |        |    |        |
|           |      |      | Vi |       | Vi |        | Vi |        |
|           |      |      | ol |       | ol |        | ol |        |
|           |      |      | at |       | at |        | at |        |
|           |      |      | io |       | io |        | io |        |
|           |      |      | ns |       | ns |        | ns |        |
+-----------+------+------+----+-------+----+--------+----+--------+
|           | OOS  | Non  | O  | No    | O  | N      | O  | N      |
|           |      | -OOS | OS | n-OOS | OS | on-OOS | OS | on-OOS |
+-----------+------+------+----+-------+----+--------+----+--------+
| A         | 2    |      | 3  | 4     |    |        |    |        |
+-----------+------+------+----+-------+----+--------+----+--------+
| B         | 2    |      |    | 2     | 1  |        | 2  |        |
+-----------+------+------+----+-------+----+--------+----+--------+

A.3.2. Crashes Avoided per Intervention

To generate an intervention's crashes avoided, the number of violation
occurrences per risk category is multiplied by the crash probability
associated with that risk category. For instance, if four occurrences of
roadside OOS violations in RC 1 were noted on an inspection report, then
the model would multiply four by the roadside OOS RC 1 probability from
the VCRPP. This would be done for all roadside OOS and non-OOS
violations, along with all traffic OOS and non-OOS violations. Summing
the products creates an initial crash risk reduction for the
inspection's risk category being evaluated.

[Initial Crash Risk Reduction per Risk Category]{.underline}

(*v~rs-rcOOS~* X P*~rs-rcOOS~*) + (*v~rs-rcNON~* X P*~rs-rcNON~*) +
**(A-1)**

(*v~te-rcOOS~* X P*~te-rcOOS~*) + (*v~te-rcNON~* X P*~te-rcNON~*) =
CRR~*rc-ini*t~

where

> *v~rs-rcOOS~* = the number of roadside out-of-service violations in a
> given risk category recorded during an inspection,
>
> *v~rs-rcNON~* = the number of roadside non-out-of service violations
> in a given risk category recorded during an inspection,
>
> *v~te-rcOOS~* = the number of traffic out-of-service violations in a
> given risk category recorded during an inspection,
>
> *v~te-rcNON~* = the number of traffic non-out-of service violations in
> a given risk category recorded during an inspection,
>
> P*~rs-rcOOS~* = crash risk probability for a given roadside
> out-of-service risk category,
>
> P*~rs-rcNON~* = crash risk probability for a given roadside
> non-out-of-service risk category,
>
> P*~te-rcOOS~* = crash risk probability for a given traffic
> out-of-service risk category,
>
> P*~te-rcNON~* = crash risk probability for a given traffic
> non-out-of-service risk category, and
>
> CRR*~rc-init~* = **initial**, calculated crash risk for a given risk
> category within an inspection.

Next, all violations recorded for a risk category during an
intervention, roadside OOS and non-OOS and, if applicable, traffic OOS
and non-OOS, are added together. Multiplying the total by the initial
crash risk reduction calculated in Equation **(A-1)** produces the final
crash risk reduction for a given risk category in a particular
intervention. Equation **(A-2)** is designed to capture the growth in
crash risk arising from the discovery and correction of numerous
violations during a single intervention. The logic behind this is that,
while each violation carries a certain degree of crash risk in
isolation, additional violations occurring in tandem elevate the crash
risk beyond the mere combined, additive, risk levels caused by each
violation alone. In essence, the Final Crash Risk Reduction per Risk
Category equation measures the multiplicative crash risk effect of
compound safety defects.

[Final Crash Risk Reduction per Risk Category]{.underline}

(*v~rs-rcOOS~* + *v~rs-rcNON~* + *v ~te-rcOOS~* + *v ~te-rcNON~*) X
CRR*~rc-init~* = CRR~RC~ **(A-2)**

where

> CRR~RC~ = **final**, calculated crash risk reduction for a given risk
> category within an inspection.

### Note: Equations **(A-1)** and **(A-2)** must be performed for each of the five risk categories.

When all five risk categories have had their respective crash risk
reductions determined, the model calculates the intervention's crashes
avoided by adding the five CRR~RC~ numbers. A cap of 0.75 is placed on
the outcome for each intervention, thus ensuring that the model never
produces a crashes avoided total greater than one. Volpe Center analysts
chose three-quarters of a crash avoided as a cap to maintain a more
conservative tendency in the model, given the lack of empirical crash
causation data.

**Number of Crashes Avoided from an Intervention**

CRR~RC1~ + CRR~RC2~ + ... + CRR~RC5~ = I~A~ **(A-3)**

where

> I~A~ = calculated crashes avoided due to an inspection.

Repeating this process using both Higher Bound and Lower Bound
probabilities yields the crashes avoided range for each intervention.

A.3.3. Examples

[Example A]{.underline}: In Inspection A (see Table A-3), a vehicle
given a roadside inspection is found to have two out-of-service
violations in Risk Category 1, three out-of-service violations in Risk
Category 2, and four non-out-of-service violations in Risk Category 2.
The calculation of the total crashes avoided of this single inspection,
using Higher Bound probabilities, appears below.

Multiplying the crash reduction probability for each risk category by
the number of out-of-service violations in that risk category and adding
it to the product of the risk reduction probability and the number of
non-out-of-service violations gives the initial crash risk reduction.

Thus, CRR*~rc-init~* for each risk category, based on Equation
**(A-1)**:

**Higher Bound**

Risk Category 1, CRR~rc1-init~ (2 X .0125) = .025

Risk Category 2, CRR~rc2-init~ (3 X .00125) + (4 X .000625)= .00625

Final crash risk reduction becomes known after multiplying the initial
crash risk reduction for each risk category by the number of violations
in that risk category. The model supplies total crashes avoided for the
intervention by tallying the final crash risk reduction from each risk
category.

Inspection A's total crashes avoided, based on Equations **(A-2)** and
**(A-3)**:

**Higher Bound**

Risk Category 1, CRR~RC1~ .05 = .025 X 2

Risk Category 2, CRR~RC2~ + [.04375]{.underline} = .00625 X 7

Total Crash Risk Reduction, I~A~ .09375

Therefore, Inspection A's range of crashes avoided begins at the Higher
Bound result, 0.09375, and would extend to the Lower Bound output.

[Example B]{.underline}: In Inspection B (see Table A-3), a traffic
enforcement stop has resulted in both traffic enforcement violations and
roadside inspection violations. The intervention involved one traffic
enforcement out-of-service violation in Risk Category 1 and two
out-of-service violations in Risk Category 2. In addition, the
inspection involved two roadside out-of-service violations in Risk
Category 1 and two non out-of-service violations in Risk Category 2.
Inspection B's computations follow:

**Higher Bound**

[Roadside]{.underline} [Traffic]{.underline}

Risk Category 1, CRR~rc1-init~ (2 X .0125) + (1 X .05) = .075 Using
**(A-1)**

Risk Category 2, CRR~rc2-init~ (2 X .000625) + (2 X .005) = .01125

To account for multiple violations, the model makes the following
intensification adjustments to calculate the final crash risk reduction
for each risk category:

**Higher Bound**

Risk Category 1, CRR~RC1~ .225 = .075 X 3 Using **(A-2)**

Risk Category 2, CRR~RC2~ + .[045]{.underline} = .01125 X 4 and
**(A-3)**

Total Crash Risk Reduction, I~A~ .27

The crashes avoided range for Inspection B starts at 0.27.

A.4. PROGRAM-LEVEL IMPACT

Measuring interventions at the program level is next. It is here,
however, that the model follows two divergent paths, one measuring
direct effects and the other measuring indirect effects. Direct effects,
it should be remembered, are the immediate products of roadside
inspections and traffic enforcement stops performed in a given year,
while indirect effects are based on behavioral changes caused by program
awareness.

A.4.1. Direct-Effect Approach

This section outlines the development of direct-effect crashes-avoided
estimates. Figure A-1 shows the process used to determine the direct
effects of the programs. First, there is a primary crashes avoided
computation. Afterwards, a roadside allocation credits a portion of
traffic enforcement crashes avoided to the roadside inspection program,
recognizing the contribution to the traffic total made by the ensuing
roadside inspection.

![](media/image6.wmf){width="5.414583333333334in"
height="2.4131944444444446in"}

Figure A-1. Direct-Effect Approach with Roadside Allowance

**A.4.1.1. Primary Determination**

The model initially examines all inspections in a given year in terms of
the numbers and types of violations associated with each individual
inspection. Based on the VCRPP described above, inspection violations
(both OOS and non-OOS) are matched with their respective crash risk
reduction probabilities, to produce an estimated range of crashes
avoided for that inspection. The model next segregates the complete set
of inspections into two groups, depending on whether the initiating
intervention was a roadside inspection or a traffic enforcement, and
sums the estimated crashes-avoided ranges across all inspections in each
group. Two overall estimates of crashes avoided emerge: one for the
roadside inspection program and one for the traffic enforcement program.

Roadside Inspection-initiated crashes avoided = I~RS-A1~ + I~RS-A2~ +
... + I~RS-A*n*~, **(A-4)**

where

> I~RS-A~ = crashes avoided per roadside inspection for (1, 2, ..., *n*)
> roadside-initiated inspections.

Likewise,

Traffic Enforcement-initiated crashes avoided = I~TE-A1~ + I~TE-A2~ +
... + I~TE-A*m*~, **(A-5)**

where

> I~TE-A~ = crashes avoided per traffic enforcement for (1, 2, ..., *m*)
> traffic-initiated inspections.

**A.4.1.2. Roadside Allowance**

The process, however, does not end with the primary determination. An
additional allocation of crashes avoided is necessary. As stated above,
when the traffic enforcement action is the initiating event for an
inspection, it is appropriate to credit back to the roadside inspection
program those crashes avoided due to the correcting of roadside
inspection-related violations.

The model accomplishes the roadside allocation by using only the
inspections initiated by traffic enforcement. *Violations* in this group
are separated by type (roadside inspection-related and traffic
enforcement-related) because two sets of crash risk reduction
probabilities are required for each inspection. One set (A) is derived
solely from traffic-related violations; the other (B) consists of the
originally computed traffic enforcement crash risk reduction
probabilities, using both types of violations. Dividing (A) by (B)
provides the percentage of crashes avoided that need to be redistributed
from the traffic enforcement program to the roadside inspection program.

V~TE~

Aadjust~direct~ = \-\-\-\-\-\-\-\-\-- **(A-6)**

V~TE+RS~

where

> V~TE~ = traffic enforcement-initiated crashes avoided from only
> traffic-related violations,
>
> V~TE+RS~ = traffic enforcement-initiated crashes avoided from all
> violations, and
>
> Aadjust~direct~ = the percentage of traffic enforcement direct effect
> crashes avoided that will need to be allocated to the roadside
> inspection program.

The final direct-effect program totals are then:

RS~A-direct~ = A~RS-direct~ + \[(1 - Aadjust~direct~) X A~TE-direct~\]
**(A-7)**

and

TE~A-direct~ = Aadjust~direct~ X A~TE-direct~ **(A-8)**

where

> A~RS-direct~ = the pre-allocation crashes avoided total for roadside
> inspections,
>
> A~TE-direct~ = the pre-allocation crashes avoided total for traffic
> enforcements,
>
> RS~A-direct~ = the post-allocation direct effect crashes avoided total
> for roadside inspections, and
>
> TE~A-direct~ = the post-allocation direct effect crashes avoided total
> for traffic enforcements.

**A.4.1.3. Examples**

Continuing with the example interventions, the results of applying
Equations **(A-5)** through **(A‑8)** to Inspection A and Inspection B
appear below.[^20]

Equation **(A-5)**:

**Higher Bound**

Roadside Inspection-initiated crashes avoided = I~RS-A1~ = 0.09375

Traffic Enforcement-initiated crashes avoided = I~TE-A1~ = 0.27

Roadside Allowance, Equations **(A-1), (A-2), (A-3)**:

(Using Inspection B, the traffic enforcement-initiated intervention)

Traffic Violations Only, Equation **(A-1)**

**Higher Bound**

Risk Category 1, CRR~rc1-init~ (1 X .05) = .05

Risk Category 2, CRR~rc2-init~ (2 X .005) = .01

Traffic Violations Only, Equation **(A-2)**

Higher Bound

Risk Category 1, CRR~rc1-init~ .05 X 1 = .05

Risk Category 2, CRR~rc2-init~ .01 X 2 = .02

Traffic Violations Only, Equation **(A-3)**

**Higher Bound**

Risk Category 1, CRR~RC1~ .05

Risk Category 2, CRR~RC2~ [+ .02]{.underline}

Total Crash Risk Reduction, I~A~ .07

The crashes avoided range for Inspection B, using only traffic
violations begins at 0.07.

Applying Equation **(A-6)** gives the percentage of traffic
enforcement-initiated crashes avoided that will be attributed to the
traffic enforcement program.

**Higher Bound**

.07

\-\-\-\-\-\-\-\-\-- = .259, i.e., 26%

.27

Final direct effects crashes avoided, Equations **(A-7)** and **(A-8)**.

[Roadside Total]{.underline} [Traffic Total]{.underline}

**Higher Bound** **Higher Bound**

.09375 + \[(1 - .26) X .27\] = .29355 .26 X .27 = .0702

Thus, the recalculated higher bound crashes-avoided of the roadside
program is 0.29, and the recalculated higher bound crashes-avoided of
the traffic program is 0.07.

A.4.2. Indirect-Effect Approach

The fundamental premise of the indirect-effect approach is that once
carriers have been exposed to the combination of roadside inspection and
traffic enforcement actions, a change in their behavior will be
manifested by a reduction in crashes. This section presents a summary of
the methods used in the model to arrive at the programs' indirect
effects. As with the direct-effect approach, a primary determination and
a roadside allowance make up the major part of the procedure. Figure A-2
provides a view of the processes involved in assessing the indirect
effects of the model.

![](media/image7.wmf)

######### Figure A-2. Indirect-Effect Approach with Roadside Allowance

Indirect effects require means other than direct measurement to reveal
their presence. For that reason, the model uses *changes* in the number
of violations recorded during inspections to identify and evaluate the
indirect effects. Specifically, the model's algorithm employs two
successive years of inspection data to undertake this process.

To conduct a year-to-year comparison, it is necessary to identify and
link the carriers who were inspected with the inspections each received
during the two-year span. Only in this way can a cross-year evaluation
discern the indirect influence (i.e., behavior modification) that causes
a reduction in crashes. In contrast, this inspection-carrier link is not
needed in the direct-effect approach.

**A.4.2.1. Primary Determination**

Gathered intervention data spanning two years is matched against the
VCRPP, much in the manner laid out in the direct effects explanation.
The model then organizes interventions by carriers. Intervention data
from those carriers who have at least one intervention in both years are
selected for preliminary analysis. The remaining Year One (Y1)
intervention data, where a carrier match with Year Two (Y2) data was not
able to be made, are set aside for later treatment. The nomenclature for
the former group is *Selected*; the latter group is assigned the name
*Remaining*.

A.4.2.1.1. Selected Set

For the Selected Set (S), the model determines each carrier's average
crashes avoided in Y1, and again in Y2. The avoided crashes of each
intervention from a given carrier in a given year (Equation **(A-9)**)
are summed across the number of interventions the carrier had in that
year (Equation **(A-10)**). Dividing the summation by all of the
intervention actions conducted on the carrier for that year (Equation
**(A-11)**) achieves the average crashes avoided. This provides Y1 and
Y2 averages for each carrier in set S.

Improved carriers in set S[^21] are those that have a decrease in
average crashes avoided from Y1 to Y2. The *improved subset* designation
applies to only those carriers with a lower Y2 figure.

A crashes-avoided estimate for carriers in the improved subset of set S
can now be made by multiplying the number of interventions a carrier had
in Y1 by the difference in average crashes avoided it experienced
between Y1 and Y2 (Equation **(A-12)** for roadside and Equation
**(A-13)** for traffic). The model reaches the entire improved subset's
crashes avoided aggregate by adding the crashes avoided totals for all
of the carriers within the subset. A parallel summation for both the
roadside inspection-initiated (Equation **(A-14)**) and traffic
enforcement-initiated (Equation **(A-15)**) interventions supplies each
program with a crashes avoided total from the improved subset of set S.

Note: Calculate indirect effects separately, based on Higher Bound
probabilities and Lower Bound probabilities.

**Carrier Crashes Avoided per Year**

Since every intervention has its own crashes avoided figure, summing the
crashes avoided from each of the interventions a carrier received in a
given year provides a crashes avoided total for that carrier.

*~n~*

Σ I~A*h*~ **(A-9)**

^*h*\ =\ 1^

Carrier Interventions per Year

Carrier interventions are the number of interventions a carrier had
within a given year.

*~n~*

Σ I*~h~* **(A-10)**

^*h*\ =\ 1^

where

> I = intervention, and
>
> I~A~ = an intervention's crashes avoided for *h* (1, 2, ..., *n*)
> interventions for a given carrier in a given year.

Carrier Average Crashes Avoided per Year

Using Equations **(A-9)** and **(A-10)**, a carrier's average crashes
avoided in a given year is calculated by dividing a carrier's crashes
avoided by its total number of interventions.

> ~*n*~

Σ I~A*h*~

> ^*h*\ =\ 1^

C~Aavg~ = \-\-\-\-\-\-\-\-\-\-- **(A-11)**

> ~*n*~

Σ I*~h~*

> ^*h*\ =\ 1^

where

C~Aavg~ = average crashes avoided for a given carrier in a given year.

Carrier Crashes Avoided

When a carrier's average crashes avoided diminishes in Y2, this is taken
to be a positive indication of program indirect effects. Carriers who
meet this condition are placed into an improved subset of set S called
S′.

[Roadside]{.underline}

The model determines an individual carrier's estimated number of
roadside inspection crashes avoided resulting from indirect effects by
taking the difference in its Y1 and Y2 average crashes avoided and
multiplying the difference by the number of roadside inspections the
carrier had in Y1. A modified version of Equation **(A-10)** that only
counts roadside-initiated inspections from Y1 totals the number of
roadside inspections.

> ~*n*~

A~RS~ = (C~Aavg\ --\ Y1~ - C~Aavg\ --\ Y2~) X Σ I~Y1-RS~ **(A-12)**

^*RS*\ =\ 1^

where

> C~Aavg\ --\ Y1~ = carrier average crashes avoided in Y1,
>
> C~Aavg\ --\ Y2~ = carrier average crashes avoided in Y2,
>
> I~Y1-*RS*~ = Y1 roadside inspection,
>
> A~RS~ = roadside inspection crashes avoided by a given carrier in
> subset S′ due to *RS* (1, 2, ..., *n*) roadside inspections in Y1, and
>
> the condition C~Aavg\ --\ Y1~ \> C~Aavg\ --\ Y2~, or subset S′, is
> met.

[Traffic]{.underline}

The model calculates traffic enforcement crashes avoided in a similar
manner.

> ~*n*~

A~TE~ = (C~Aavg\ --\ Y1~ - C~Aavg)\ --\ Y2~) X Σ I~Y1-TE~ **(A-13)**

> ^*TE*\ =\ 1^

where

> C~Aavg\ --\ Y1~ = carrier average crashes avoided in Y1,
>
> C~Aavg\ --\ Y2~ = carrier average crashes avoided in Y2,
>
> I~Y1-*TE*~ = Y1 traffic enforcement,
>
> A~TE~ = traffic enforcement crashes avoided by a given carrier in
> subset S′ due to *TE* (1, 2, ..., *n*) traffic enforcements in Y1, and

the condition C~Aavg\ --\ Y1~ \> C~Aavg\ --\ Y2~, or subset S′, is met.

Set S Preliminary Crashes Avoided

Once Equations **(A-12)** and **(A-13)** have been used to create
crashes avoided totals for each carrier in subset S′, preliminary
program crashes avoided totals for set S are the aggregations of these
totals.

[Roadside.]{.underline}

> *~m~*

A~S′-RS~ = Σ A~RS*i*~ **(A-14)**

> ^*i*\ =\ 1^

where

> A~S′-RS~ = set S roadside inspection crashes avoided for i (1,2, ...,
> *m*) carriers in subset S′.

[Traffic]{.underline}

*~m~*

A~S′-TE~ = Σ A~TE*i*~ **(A-15)**

> ^*i*\ =\ 1^

where

> A~S′-TE~ = set S traffic enforcement crashes avoided for i (1, 2, ...,
> *m*) carriers in subset S′.

**A.4.2.1.2. Remaining Set**

Though crashes avoided have been calculated for the improved subset
(S'), carrier and intervention data from the subset and its parent, set
S, must still be used to impute crashes avoided totals to the Remaining
Set (R). Because a definitive carrier-inspection link is absent over the
course of Y1 and Y2, the R set requires estimations from general,
intervention-related propositions. Therefore, two determinations are
essential: the first is the ratio of interventions that are likely to be
positively influenced by deterrence; the second characterizes the
General Deterrence Impact of an intervention (described below).

Since not all carriers in set S showed an improvement in their average
crashes avoided from Y1 to Y2, the model assumes only a certain
proportion of all interventions performed in Y1 carry an indirect
influence. Dividing the total number of interventions in the improved
subset (S') by the total number of interventions in the entire set S
approximates the deterrence-to-intervention influence.

The General Deterrence Impact (GDI) per intervention, on the other hand,
attempts to quantify the portion of an avoided crash that is
attributable to a single inspection, based again on the experience of
the improved carrier subset. A unique GDI is calculated for each
intervention type. The GDI for roadside inspections is the ratio of all
improved subset roadside inspection crashes avoided divided by the total
number of interventions in the subset, while the traffic enforcement GDI
is the division of all improved subset traffic enforcement crashes
avoided by the total number of interventions in the subset.

Having determined these percentages, set R calculations may proceed. The
percentage of interventions likely to be influenced by deterrence is
multiplied by the total number of interventions in set R. The outcome is
the estimated number of R interventions that would register an
improvement in average crashes avoided. Next, the model estimates the
number of indirect influenced set R interventions by the General
Deterrence Impact per roadside inspection. The product of this
calculation is the estimated roadside inspection crashes avoided for set
R. Lastly, using the General Deterrence Impact per traffic enforcement,
the same procedure develops R set estimated crashes avoided for traffic
enforcement.

**The following equations, derived from Set S, provide the basis for
estimating crashes avoided from Set R.**

**Positive Influence of Deterrence.**

*~l~*

Σ I*~j~*

^*j*\ =\ 1^

D = \-\-\-\-\-\-\-\-\-- **(A-16)**

*~q~*

Σ I*~k~*

^*k*\ =\ 1^

where

> D = percentage of interventions positively influenced by deterrence,
> and
>
> I = inspection for *j* (1, 2, ..., *l*) interventions in subset S′ and
> for *k* (1, 2, ..., *q*) interventions in set S.

**General Deterrence Impact**

[Roadside]{.underline}

The roadside inspection general deterrence impact is the ratio of all
set S roadside inspection crashes avoided to the number of interventions
(of either type) that are part of subset S′.

A~S′-RS~

GDI~RS~ = \-\-\-\-\-\-\-\-\-\-\-- **(A-17)**

*~l~*

Σ I*~j~*

> ^*j*\ =\ 1^

where

> GDI~RS~ = general deterrence impact per roadside inspection, and
>
> A~S′-RS~ = set S roadside inspection crashes avoided for *j* (1, 2,
> ..., *l*) interventions in subset S′.

[Traffic]{.underline}

The traffic enforcement general deterrence impact is the ratio of all
set S traffic enforcement crashes avoided to the number of interventions
(of either type) that are part of subset S′.

A~S′-TE~

GDI~TE~ = \-\-\-\-\-\-\-\-\-\-\-- **(A-18)**

*~l~*

Σ I*~j~*

> ^*j*\ =\ 1^

where

> GDI~TE~ = general deterrence impact per traffic enforcement, and
>
> A~S′-TE~ = set S traffic enforcement crashes avoided for *j* (1, 2,
> ..., *l*) interventions in subset S′.

**Set R Indirect-Influenced Interventions**

With the results from Equation **(A-16)**, it is possible to estimate
the number of set R interventions that would be influenced by deterrence
by multiplying the number of interventions in set R by the positive
influence of deterrence.

> ~*r*~

R~I~ = Σ I*~g~* X D **(A-19)**

> ^*g*\ =\ 1^

where

> R~I~ = the number of set R interventions positively influenced by
> deterrence, and

D = the positive influence of deterrence for *g* (1, 2, ..., *r*)
interventions in set R.

**Set R Preliminary Crashes Avoided.**

[Roadside]{.underline}

The number of roadside inspection crashes avoided for set R is
calculated by multiplying the general deterrence impact of a roadside
inspection by the number of set R interventions positively influenced by
deterrence.

A~R-RS~ = R~I~ X GDI~RS~ **(A-20)**

where

> A~R-RS~ = set R crashes avoided from roadside inspections,
>
> R~I~ = the number of set R interventions positively influenced by
> deterrence, and
>
> GDI~RS~ = general deterrence impact per roadside inspection.

[Traffic]{.underline}

The number of traffic enforcement crashes avoided for set R is
calculated by multiplying the general deterrence impact of a traffic
enforcement by the number of set R interventions positively influenced
by deterrence.

A~R-TE~ = R~I~ X GDI~TE~ **(A-21)**

where

> A~R-TE~ = set R crashes avoided from traffic enforcements,
>
> R~I~ = the number of set R interventions positively influenced by
> deterrence, and
>
> GDI~TE~ = general deterrence impact per traffic enforcement.

**A.4.2.2. Roadside Allowance**

Here too, the model allocates a portion of the crashes avoided derived
from traffic enforcement actions back to the roadside program. Before
doing so, overall indirect effect preliminary crashes avoided are
obtained by adding the set S and R figures.

[Roadside]{.underline}

A~RS-indirect~ = A~S′-RS~ + A~R-RS~ **(A-22)**

where

> A~RS-indirect~ = the pre-allocation crashes avoided total for roadside
> inspections,
>
> A~S′-RS~ = set S roadside inspection crashes avoided, and
>
> A~R-RS~ = set R roadside inspection crashes avoided.

[Traffic]{.underline}

A~TE-indirect~ = A~S′-TE~ + A~R-TE~ **(A-23)**

where

> A~TE-indirect~ = the pre-allocation crashes avoided total for traffic
> enforcements,
>
> A~S′-TE~ = set S traffic enforcement crashes avoided, and
>
> A~R-TE~ = set R traffic enforcement crashes avoided.

Equations **(A-1)**, **(A-2)**, and **(A-3)** are used to calculated
crashes avoided totals for each intervention of the improved subset,
using only the traffic-related violations. Dividing this by the results
from Equation **(A-15)** provides the percentage of traffic
enforcement-initiated crashes avoided that will need to be allocated to
the roadside inspection program.

V′~TE~

Aadjustin~direct~ = \-\-\-\-\-\-\-\-\-- **(A-24)**

V′~TE+RS~

where

> V′~TE~ = traffic enforcement-initiated crashes avoided from only
> traffic-related violations in subset S′,
>
> V′~TE+RS~ = traffic enforcement-initiated crashes avoided from all
> violations in subset S′, and
>
> Aadjust~indirect~ = the percentage of indirect effect traffic
> enforcement crashes avoided that will need to be allocated to the
> roadside inspection program.

The final allocation of indirect effects is then:

> Indirect-effect crashes avoided from roadside inspections

RS~A-indirect~ = A~RS-indirect~ + \[(1 - Aadjust~indirect~) X
A~TE-indirect~\] **(A-25)**

and

> Indirect effects crashes avoided from traffic enforcements

TE~A-indirect~ = Aadjust~indirect~ X A~TE-indirect~ **(A-26)**

where

> A~RS-indirect~ = the pre-allocation crashes avoided total for roadside
> inspections,
>
> A~TE-indirect~ = the pre-allocation crashes avoided total for traffic
> enforcements,
>
> RS~A-indirect~ = the post-allocation indirect effect crashes avoided
> total for roadside inspections, and
>
> TE~A-indirect~ = the post-allocation indirect effect crashes avoided
> total for traffic enforcements.

A.4.2.3. Examples

Because indirect effects require more than a single year of data, the
previous example interventions will not suffice. Therefore, a new set of
example data appears in Table A-5.

**Table A-5. Indirect Effects Example Data**

  --------- ------------ ------- ----- ------- ----- ------- ----- ------- ----- ------- --------------- ------- ----- ------- ----- ------- ----- ------- ----- -------
  **Y/C**   Roadside                                                                     **Traffic                                                               
            Inspection                                                                   Enforcement**                                                           

            RC1                  RC2           RC3           RC4           RC5           RC1                     RC2           RC3           RC4           RC5   

            oos          n-oos   oos   n-oos   oos   n-oos   oos   n-oos   oos   n-oos   oos             n-oos   oos   n-oos   oos   n-oos   oos   n-oos   oos   n-oos

  Y1                                                                                                                                                             

  A                              2     3             3             2             1                                                                               

  A                              1     7             7                           4                               1     1                                         

  A                                                                              1                                     1                                         

  A                                    6             2             4             3                       1             2                                         

  A                                    4       3     5             3             2                                                                               

  B                                                                                                                                  1                           

  B                                    1                                                                                                                         

  B                                                  2                           2                                                                               

  B                                                                1             4                                             1                                 

  B         1            1       2     1             1                           1                               1     1                                         

  Y2                                                                                                                                                             

  A                                    1                           1                                                   1                                         

  A                      1             2             2             1                                                   1                                         

  A                                    2             3             1                                                   1                                         

  A                                                  5                           2                       1             1                                         
  --------- ------------ ------- ----- ------- ----- ------- ----- ------- ----- ------- --------------- ------- ----- ------- ----- ------- ----- ------- ----- -------

Y/C -- Year/Carrier RC -- Risk Category oos -- out-of-service n-oos --
non-out-of-service

The first column identifies intervention data by carrier (Carrier A and
Carrier B) over a two-year period. Note that Carrier A has interventions
in both years, while Carrier B has interventions in Y1 only. This does
not necessarily indicate that Carrier B had no interventions in Y2.
Instead, it reflects the fact that interventions are not always able to
be associated with a particular carrier and the model requires a carrier
match in Y1 and Y2. Based on the criteria outlined in Section A.4.2.1,
Carrier A would fall into the Selected Set and Carrier B would make up
the Remaining Set.

Equations **(A-1**) through **(A-3)** provide avoided crashes totals for
each of the inspections in Table A-5. These figures form the input to
the equations from the indirect-effect approach. Here, only the results
created from the Higher Bound probabilities will be displayed. Lower
Bound calculations follow the same steps.

Summing the crashes avoided for each carrier in each year (Equation
**(A-9)**) yields:

Carrier A Crashes Avoided in Y1 0.05033

0.28487

0.00500

0.61023

\+ [0.05453]{.underline}

1.00496

Carrier A Crashes Avoided in Y2 0.00939

0.07092

0.02761

\+ [0.25032]{.underline}

0.35824

The number of interventions per carrier per year, Equation **(A-10)**

Carrier A Number of Interventions in Y1 = 5

Carrier A Number of Interventions in Y2 = 4

Equation **(A-11)** supplies carrier average crashes avoided per carrier
per year

Carrier A Crashes Avoided in Y1 1.00496

\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--
= \-\-\-\-\-\-\-\-\-\-- = 0.20099

Carrier A Number of Interventions in Y1 5

Carrier A Crashes Avoided in Y2 0.35824

\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--
= \-\-\-\-\-\-\-\-\-\-- = 0.08956

Carrier A Number of Interventions in Y2 4

Carrier A's average crashes avoided in Y2 is less than the average in
Y1. Thus, it meets the criterion to be included in the Improved Subset
of the Selected Set.

Indirect-effect roadside crashes avoided for Carrier A follow from
Equation **(A-12)**.

(Carrier A Avg. Crashes Avoided in Y1 -- Carrier A Avg. Crashes Avoided
in Y2)

X (Carrier A Number of Roadside Inspections in Y1)

= (0.20099 -- 0.08956) X 2

= 0.2229

Equation **(A-13)** supplies Carrier A's traffic crashes avoided.

(Carrier A Avg. Crashes Avoided in Y1 -- Carrier A Avg. Crashes Avoided
in Y2)

X (Carrier A Number of Traffic Enforcements in Y1)

= (0.20099 -- 0.08956) X 3

= 0.3343

The output of Equations **(A-14)** and **(A-15)** is, in this example
case, identical to **(A-12)** and **(A-13)**, respectively, because
Carrier A is the sole carrier within the Selected set. Were other
carriers present, the outputs of **(A-12)** would be added to arrive at
Selected set roadside inspection crashes avoided. Traffic enforcement
crashes avoided would be the summation of the outputs from **(A-13)**.

Positive Influence of Deterrence, Equation **(A-16)**

Number of Interventions in the Improved Subset 4

\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--
= \-\-\-- = 0.8

Number of Interventions in the Selected Set 5

General Deterrence Impact for Roadside Inspections, Equation **(A-17)**

Selected Set Roadside Crashes Avoided 0.2229

\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--
= \-\-\-\-\-\-\-\-- = 0.04457

Number of Interventions in the Improved Subset 5

General Deterrence Impact for Traffic Enforcements, Equation **(A-18)**

Selected Set Traffic Crashes Avoided 0.3343

\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--
= \-\-\-\-\-\-\-\-- = 0.06686

Number of Interventions in the Improved Subset 5

**The calculations for the Remaining Set are next.**

Remaining Set Indirect-Influenced Interventions, Equation **(A-19)**

= Number of Interventions in the Remaining Set X Positive Influence of
Deterrence

= 5 X 0.8

= 4

Remaining Set Preliminary Roadside Crashes Avoided, Equation **(A-20)**

= Remaining Set Indirect-Influenced Interventions X General Deterrence
Impact for

> Roadside Inspections

= 4 X 0.04457

= 0.1783

Remaining Set Preliminary Traffic Crashes Avoided, Equation **(A-21)**

= Remaining Set Indirect-Influenced Interventions X General Deterrence
Impact for

> Traffic Enforcements

= 4 X 0.06686

= 0.2674

Adding the Selected Set Crashes Avoided to the Remaining Set Crashes
Avoided provides the pre-roadside allowance indirect-effects totals for
each program.

Roadside Inspection Preliminary Indirect Effect Crashes Avoided,
Equation **(A-22)**

0.2229 + 0.1783 = 0.40115

Traffic Enforcement Preliminary Indirect Effect Crashes Avoided,
Equation **(A-23)**

0.3343 + 0.2674 = 0.60173

Roadside Allowance, Equation **(A-24)**

Traffic Enforcement Crashes Avoided from only Traffic-Related Violations

\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--

Traffic Enforcement Crashes Avoided from All Violations

0.13163

= \-\-\-\-\-\-\-\-\--

0.60173

= 0.22

Indirect Effects Crashes Avoided from Roadside Inspections, **(A-25)**.

Roadside Inspection Preliminary Indirect Effect Crashes Avoided +

\[(1 -- Roadside Allowance)

X Traffic Enforcement Preliminary Indirect Effect Crashes Avoided\]

= 0.40115 + \[(1- 0.22) X 0.60173\]

= 0.8705

Indirect Effects Crashes Avoided from Traffic Enforcements, **(A-26)**.

Roadside Allowance X

Traffic Enforcement Preliminary Indirect Effect Crashes Avoided

= 0.22 X 0.60173

= 0.1324

**A.5. PROGRAM BENEFITS**

Crash severity varies. Some crashes may result in no more than minor
property damage, while others may result in bodily harm or loss of life.
Of the many gradations possible, two classifications of crashes suffice
for calculating program benefits, fatal crashes and injury crashes. Any
motor carrier crash that results in at least one fatality is a fatal
crash. A fatal crash may also involve injuries, but the fatality governs
the crash's classification. Any motor carrier crash that results in at
least one injury requiring transport for immediate medical attention but
no fatalities, is an injury crash.

Statistics of fatal and injury crashes supply the basis for creating
lives saved and injuries avoided figures. This follows NHTSA established
practice, which expresses program benefits in terms of lives saved and
injuries avoided. Fatal crashes avoided translate to lives saved and
injury crashes avoided translate to injuries avoided.

Obtaining program benefits from estimated crashes-avoided figures
requires two prior determinations, the first being a proportional
identification of crashes by severity and the second being the average
numbers of fatalities and injuries per crash. Fortunately, each has been
completed elsewhere. According to a report[^22]done for the Federal
Highway Administration's (FHWA) Office of Motor Carriers (OMC),[^23] of
the trucks involved in crashes on U.S. roads in 1995, 3.6 percent were
involved in fatal crashes, 40.0 percent were involved in injury crashes,
and 56.4 percent were involved in towaway crashes.[^24]

The average number of fatalities per fatal crash was calculated from
data from the Fatality Analysis Reporting System (FARS), which is
maintained by the NHTSA. For 1999 crashes involving large trucks or
intercity buses, the ratio was 1.19 fatalities per fatal crash.

The number of injuries per crash involves fatal as well as injury
crashes, since fatal crashes can also result in injuries. State-reported
crash data in the MCMIS were used to compute the average numbers of
injuries in fatal and injury crashes. For 1999 large truck and bus
crashes, the averages were as follows:

-   Fatal crashes: 1.26 injuries per crash

```{=html}
<!-- -->
```
-   Injury crashes: 1.60 injuries per crash

Figure A-3 shows the process used to calculate program benefits.

![](media/image8.wmf)

######### Figure A-3. Program Benefits Determination

**Program Crashes Avoided (Direct and Indirect).**

The input to the program benefits portion of the model requires the
union of crashes avoided attributable to direct effects and indirect
effects. The program benefits calculations use the output of Equations
**(A-27)** and **(A-28)**. The calculations entail the development of
estimated totals of crashes by severity as well as the final tally of
lives saved and injuries avoided.

[Roadside]{.underline}

RS~A~ = RS~A-direct~ + RS~A-indirect~ **(A-27)**

where

> RS~A~ = roadside inspection crashes avoided from both direct and
> indirect effects,
>
> RS~A-direct~ = the post-allocation direct-effect crashes avoided total
> for roadside inspections, and
>
> RS~A-indirect~ = the post-allocation indirect-effect crashes avoided
> total for roadside inspections.

[Traffic]{.underline}

TE~A~ = TE~A-direct~ + TE~A-indirect~ **(A-28)**

where

> TE~A~ = traffic enforcement crashes avoided from both direct and
> indirect effects,
>
> TE~A-direct~ = the post-allocation direct-effect crashes avoided total
> for traffic enforcements, and
>
> TE~A-indirect~ = the post-allocation indirect-effect crashes avoided
> total for traffic enforcements.

**A.5.1. FATAL AND INJURY CRASHES AVOIDED**

The model breaks out program crashes-avoided figures into the numbers of
program crashes avoided by severity. The proportions from the Center for
National Truck Statistics report (9) mentioned previously are used by
the model to calculate estimates of the numbers of fatal crashes and
injury crashes avoided due to the roadside inspection and traffic
enforcement programs.

[Roadside]{.underline}

Multiplying the roadside crashes avoided from Equation **(A-27)** and
the proportion of all highway crashes that resulted in fatalities
provides the roadside fatal crashes avoided. Roadside injury crashes
avoided are calculated similarly, only substituting the injury
proportion of all highway crashes in place of the fatality proportion.

> RS~A-Fatal~ = RS~A~ X CSP~Fatal~ **(A-29)**
>
> RS~A-Injury~ = RS~A~ X CSP~Injury~ **(A-30)**

where

> RS~A-Fatal~ = number of fatal crashes avoided due to the roadside
> inspection program,
>
> RS~A-Injury~ = number of injury crashes avoided due to the roadside
> inspection program,
>
> RS~A~ = number of roadside inspection crashes avoided,
>
> CSP~Fatal~ = proportion of all crash types that are fatal crashes, and
>
> CSP~Injury~ = proportion of all crash types that are injury crashes.

[Traffic]{.underline}

Fatal crashes for the traffic enforcement flow from Equation **(A-28)**.

> TE~A-Fatal~ = TE~A~ X CSP~Fatal~ **(A-31)**
>
> TE~A-Injury~ = TE~A~ X CSP~Injury~ **(A-32)**

where

> TE~A-Fatal~ = number of fatal crashes avoided due to the traffic
> enforcement program,
>
> TE~A-Injury~ = number of injury crashes avoided due to the traffic
> enforcement program,
>
> TE~A~ = number of traffic enforcement crashes avoided,
>
> CSP~Fatal~ = proportion of all crash types that are fatal crashes, and
>
> CSP~Injury~ = proportion of all crash types that are injury crashes.

A.5.2. LIVES SAVED

To calculate the number of lives saved, the number of fatal crashes
avoided is multiplied by the average number of fatalities per fatal
crash.

[Roadside]{.underline}

LS~RS~ = RS~A-Fatal~ X FC~Fatal~ **(A-33)**

where

> LS~RS~ = lives saved due to the roadside inspection program,
>
> RS~A-Fatal~ = number of fatal crashes avoided due to the roadside
> inspection program, and
>
> FC~Fatal~ = average fatalities per fatal crash.

[Traffic]{.underline}

LS~TE~ = TE~A-Fatal~ X FC~Fatal~ **(A-34)**

where,

> LS~TE~ = lives saved due to the traffic enforcement program, and
>
> TE~A-Fatal~ = number of fatal crashes avoided due to the traffic
> enforcement program, and
>
> FC~Fatal~ = average fatalities per fatal crash.

**A.5.3. INJURIES AVOIDED**

To calculate the number of injuries avoided, the number of fatal crashes
avoided is multiplied by the average number of injuries per fatal crash,
and the number of injury crashes avoided is multiplied by the average
number of injuries per injury crash. The two products are then added to
obtain the total number of injuries avoided.

[Roadside]{.underline}

IA~RS~ = (RS~A-Fatal~ X FC~Injury~) + (RS~A-Injury~ X IC~Injury~)
**(A-35)**

where

> IA~RS~ = number of injuries avoided due to roadside inspections,
>
> RS~A-Fatal~ = number of fatal crashes avoided due to the roadside
> inspection program,
>
> RS~A-Injury~ = number of injury crashes avoided due to the roadside
> inspection program,
>
> FC~Injury~ = average injuries per fatal crash, and
>
> IC~Injury~ = average injuries per fatal crash.

[Traffic]{.underline}

IA~TE~ = (TE~A-Fatal~ X FC~Injury~) + (TE~A-Injury~ X IC~Injury~)
**(A-36)**

where

> TE~A-Fatal~ = number of fatal crashes avoided due to the traffic
> enforcement program,
>
> TE~A-Injury~ = number of injury crashes avoided due to the traffic
> enforcement program,
>
> FC~Injury~ = average injuries per fatal crash, and
>
> IC~Injury~ = average injuries per fatal crash.

**A.5.4. EXAMPLES**

**Program Crashes Avoided**

Roadside Program Crashes Avoided (Direct and Indirect), **(A-27)**

= Roadside Program Direct-Effect Crashes Avoided +

Roadside Program Indirect-Effect Crashes Avoided

= 0.9355 + 0.8705

= 1.806

Traffic Program Crashes Avoided (Direct and Indirect), **(A-28)**

= Traffic Program Direct-Effect Crashes Avoided +

Traffic Program Indirect-Effect Crashes Avoided

= 0.0702 + 0.1324

= 0.203

Fatal Crashes Avoided

Roadside Fatal Crashes Avoided, **(A-29)**

= Roadside Program Crashes Avoided (Direct and Indirect) X

Fatal proportion of truck crashes

= 1.806 X 0.036

= 0.065

Traffic Fatal Crashes Avoided, **(A-31)**

= Traffic Program Crashes Avoided (Direct and Indirect) X

> Fatal proportion of truck crashes

= 0.203 X 0.036

= .0073

**Injury Crashes Avoided**

Roadside Injury Crashes Avoided, **(A-30)**

= Roadside Program Crashes Avoided (Direct and Indirect) X

Injury proportion of truck crashes

= 1.806 X 0.400

= 0.7224

Traffic Injury Crashes Avoided, **(A-32)**

= Traffic Program Crashes Avoided (Direct and Indirect) X

Injury proportion of truck crashes

= 0.203 X 0.400

= 0.0812

**Lives Saved**

Roadside Lives Saved, **(A-33)**

= Roadside Fatal Crashes Avoided X Average fatalities per fatal crash

= 0.065 X 1.19

= 0.0774

Traffic Lives Saved, **(A-34)**

= Traffic Fatal Crashes Avoided X Average fatalities per fatal crash

= 0.0073 X 1.19

= 0.0087

**Injuries Avoided**

Roadside Injuries Avoided, **(A-35)**

= (Roadside Fatal Crashes Avoided X Average fatalities per injury crash)
+

(Roadside Injury Crashes Avoided X Average. injuries per injury crash)

= (0.065 X 1.26) + (0.7224 X 1.60)

= 1.2377

Traffic Injuries Avoided, **(A-36)**

= (Traffic Fatal Crashes Avoided X Average. fatalities per injury crash)
+

(Traffic Injury Crashes Avoided X Average injuries per injury crash)

= (0.0073 X 1.26) + (0.0812 X 1.60)

= 0.1391

######### APPENDIX B -- VIOLATIONS

**Table B-1. Roadside Inspection Violations**

![](media/image9.wmf){width="8.440972222222221in"
height="5.743055555555555in"}

#########  

######### Table B-1. Roadside Inspection Violations (continued)

![](media/image10.wmf){width="8.35763888888889in"
height="5.704861111111111in"}

######### Table B-1. Roadside Inspection Violations (continued)

![](media/image11.wmf){width="8.399305555555555in"
height="5.573611111111111in"}

**Table B-1. Roadside Inspection Violations (continued)**

![](media/image12.wmf){width="8.288194444444445in"
height="5.475694444444445in"}

![](media/image13.wmf){width="8.413194444444445in"
height="5.329166666666667in"}**Table B-1. Roadside Inspection Violations
(continued)**

######### Table B-1. Roadside Inspection Violations (continued)

![](media/image14.wmf){width="8.440972222222221in"
height="5.899305555555555in"}

**Table B-1. Roadside Inspection Violations (continued)**

![](media/image15.wmf){width="8.45486111111111in"
height="5.518055555555556in"}

Table B-1. Roadside Inspection Violations (continued)

######### ![](media/image16.wmf){width="8.45486111111111in" height="5.482638888888889in"}

#########  

######### Table B-1. Roadside Inspection Violations (continued)

![](media/image17.wmf){width="8.496527777777779in"
height="5.323611111111111in"}

######### Table B-1. Roadside Inspection Violations (continued)

![](media/image18.wmf){width="8.663194444444445in"
height="4.8597222222222225in"}

**Table B-1. Roadside Inspection Violations (continued)**

![](media/image19.wmf){width="8.690972222222221in"
height="4.588888888888889in"}

**Table B-1. Roadside Inspection Violations (continued)**

![](media/image20.wmf){width="8.73263888888889in"
height="2.8291666666666666in"}

**Table B-2. Traffic Enforcement Violations**

![](media/image21.wmf){width="8.899305555555555in"
height="5.626388888888889in"}

**Table B-2. Traffic Enforcement Violations (continued)**

![](media/image22.wmf){width="8.414583333333333in"
height="2.2111111111111112in"}

######### APPENDIX C -- PROGRAM BENEFITS

######### C.1. NATIONAL PROGRAM BENEFITS

![](media/image23.png){width="8.902777777777779in"
height="4.027083333333334in"}**Table C-1a. National Program Benefits,
1998**

**Table C-1b. National Program Benefits, 1999**

![](media/image24.png){width="8.930555555555555in"
height="4.106944444444444in"}

**Table C-1c. National Program Benefits, 2000**

![](media/image25.png){width="8.930555555555555in"
height="4.245833333333334in"}

######### C.2. ROADSIDE INSPECTION BENEFITS, BY STATE 

![](media/image26.png){width="8.840277777777779in"
height="5.752777777777778in"}**Table C-2a. Mean Roadside Inspection
Program Benefits by State, 1998**

######### 

######### Table C-2b. Mean Roadside Inspection Program Benefits by State, 1999

######### ![](media/image27.png){width="8.868055555555555in" height="5.8125in"}

######### 

######### 

######### Table C-2c. Mean Roadside Inspection Program Benefits by State, 2000

######### 

![](media/image28.png){width="8.868055555555555in"
height="5.548611111111111in"}

######### C.3. TRAFFIC ENFORCEMENT BENEFITS, BY STATE 

######### Table C-3a. Mean Traffic Enforcement Program Benefits by State, 1998

![](media/image29.png){width="8.971527777777778in"
height="5.342361111111111in"}

**Table C-3b. Mean Traffic Enforcement Program Benefits by State, 1999**

![](media/image30.png){width="8.922916666666667in" height="5.68125in"}

**Table C-3c. Mean Traffic Enforcement Program Benefits by State, 2000**

![](media/image31.png){width="8.874305555555555in"
height="5.920138888888889in"}

[^1]: Mean estimates. Higher and lower bound estimates were based on
    different risk assumptions, which may be found in Intervention
    Model: Roadside Inspection and Traffic Enforcement Effectiveness
    Assessment, Sept. 2002.

[^2]: Revised figures. See Section 4.5 for details.

[^3]: Except under the following circumstances: 1) A North American
    Commercial Vehicle Critical Safety Item or OOS violation is
    detected, 2) When a Level IV (Special Inspection) exercise is
    involved, 3) When a statistically-based random inspection technique
    is being employed to validate an individual jurisdiction or regional
    OOS percentage, or 4) When inspections are conducted to maintain
    CVSA inspection quality assurance. Commercial Vehicle Safety
    Alliance website,
    [http://www.cvsa.org/Inspections/CVSA_Decals/cvsa_decals.html]{.underline},
    2001.

[^4]: For a complete list of driver and vehicle violations associated
    with the roadside inspections and traffic enforcement, see Appendix
    B.

[^5]: Cycla Corporation, *Risk-based Evaluation of Commercial Motor
    Vehicle Roadside Violations: Process and Results*, July 1998. Note:
    The twenty-one traffic enforcement violations that fall under MCSAP
    were also included in the Cycla evaluation.

[^6]: See Appendix A for the explanation of how the relative weights
    from Cycla were converted into crash risk probabilities.

[^7]: Based on preliminary findings from crash causation studies
    conducted by the University of Michigan Transportation Research
    Institute. An ongoing, more comprehensive crash causation study at
    the NHTSA is expected to bolster these assumptions.

[^8]: Ibid, p. 21.

[^9]: Readers should note that the allocation of violations to programs
    actually occurs earlier in the indirect-effect calculation process.
    To simplify the presentation, however, the submodel has been
    presented in the form appearing above. This does not materially
    affect the model outline.

[^10]: To determine indirect effects, the Model looked at carriers that
    had interventions in 1998 and 1999, then noted the difference
    between the two years' data. This was done because behavioral
    changes (i.e., indirect effects) brought about by 1998 interventions
    will only be seen through the impact that they have upon a
    carrier/driver over the course of the following year.

[^11]: Source: MCMIS file, March 2001. Figures appearing in the table
    have been rounded to the nearest thousand.

[^12]: Model output figures represent the mean between totals derived
    from two sets of crash risk probabilities. An explanation of the
    probability range and its effects on the model appears in Appendix
    A.

[^13]: The U.S. Department of Transportation\'s Federal Motor Carrier
    Safety Administration (FMCSA) and National Highway Traffic Safety
    Administration (NHTSA) are conducting the Large Truck Crash
    Causation Study. The Motor Carrier Safety Improvement Act of 1999
    (MCSIA) provided for the study.

[^14]: "The MCSAP is a Federal grant program that provides financial
    assistance to States to reduce the number and severity of accidents
    ... involving commercial motor vehicles (CMVs). ... Investing grant
    monies in appropriate safety programs will increase the likelihood
    that safety defects, driver deficiencies, and unsafe motor carrier
    practices will be detected and corrected before they become
    contributing factors to accidents."
    <http://www.fmcsa.dot.gov/safetyprogs/mcsap.htm>.

[^15]: See http://www.inspector.org/37stepin.htm.

[^16]: § Sec.350.111 of the Federal Motor Carrier Safety Regulations
    defines a MCSAP traffic enforcement as follows: "Traffic enforcement
    means enforcement activities of State or local officials, including
    stopping CMVs operating on highways, streets, or roads for
    violations of State or local motor vehicle or traffic laws (e.g.,
    speeding, following too closely, reckless driving, improper lane
    change). To be eligible for funding through the grant, traffic
    enforcement must include an appropriate North American Standard
    Inspection of the CMV or driver or both prior to releasing the
    driver or CMV for resumption of operations."

[^17]: Crash causation studies are underway at the University of
    Michigan Transportation Research Institute and the NHTSA.

[^18]: Ibid.

[^19]: To avoid needless complexity, the examples have been crafted
    using risk categories 1 and 2, rather than the entire range of risk
    categories 1 through 5.

[^20]: Note: Since only two example interventions have been presented,
    one roadside-initiated (Inspection A) and the other
    traffic-initiated (Inspection B), Equation **(A-5)**'s example
    results are identical to the output of Equation **(A‑3)**.

[^21]: An area for future investigation consists of motor carriers who
    registered no improvement in average crashes avoided.

[^22]: Center for National Truck Statistics, University of Michigan
    Transportation Research Institute, *Truck and Bus Crash Factbook
    1995*, 1997.

[^23]: The Federal Highway Administration's (FHWA) Office of Motor
    Carriers (OMC) later became the Federal Motor Carrier Safety
    Administration (FMCSA).

[^24]: A *towaway* crash results in no fatalities or injuries requiring
    transport for immediate medical attention, but in one or more motor
    vehicles incurring disabling damage as a result of the crash,
    requiring the vehicle(s) to be transported away from the scene by a
    tow truck or other motor vehicle.