FMCSA Safety Program
Performance Measures 

Intervention Model:
Roadside Inspection and Traffic Enforcement
Effectiveness Assessment

September 2002

(Updated)

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

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	Page

	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		Page		

		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 BenefitsC-2
	C.2.  Roadside Inspection Benefits, by State..C-6
	C.3.  Traffic Enforcement Benefits, by StateC-10

LIST OF ILLUSTRATIONS

FIGURES

Figure	Page

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	Page

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 

1998 
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,

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

?	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.   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.

?	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.

?	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, 

?	Makes allowances for inspections with multiple violations, and

?	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. 

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

?	How the model begins with raw inspection violation data; 

?	Proceeds to the submodels, where separate algorithms are run to determine the direct and 
indirect effects; and 

?	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

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.   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.   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.  

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

Risk Category
Relative Weight
1
Violation is the potential single, immediate factor leading to a crash or 
fatalities/injuries from a given crash.
1,000
2
Violation is the potential single, eventual factor leading to a crash or 
fatalities/injuries from a given crash.
   100
3
Violation is a potential contributing factor leading to a crash or 
fatalities/injuries from a given crash.
     10
4
Violation is an unlikely potential contributing factor leading to a crash 
or fatalities/injuries from a given crash.
       1
5
Violation has little or no connection to crashes or the prevention of 
fatalities/injuries.
          0.1

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.

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.

?	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.

?	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.

?	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 results 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.

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. 

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 
Total Interventions
    2,217,000 
     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. 

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.

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,

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

?	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.   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.   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.   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.   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.   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--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. 

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

Risk Category
Roadside Inspection
Traffic Enforcement

Number of Violations
Number of Violations

OOS
Violations
Non-OOS
Violations
OOS
Violations
Non-OOS
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

Risk Category
Roadside Inspection
Traffic Enforcement

Number of Violations
Number of Violations

OOS
Violations
Non-OOS
Violations
OOS
Violations
Non-OOS
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

Risk Category
Roadside Inspection
Traffic Enforcement

Crash Reduction Probability
Crash Reduction Probability

OOS
Violations
Non-OOS
Violations
OOS
Violations
Non-OOS
Violations
1
.00833
.004167
.033
.0167
2
.000833
.0004167
.0033
.00167
3
.0000833
.00004167
.00033
.000167
4
.00000833
.000004167
.000033
.0000167
5
.000000833
.0000004167
.0000033
.00000167

Table A-2b.  Higher Bound Crash Reduction Probabilities

Risk Category
Roadside Inspection
Traffic Enforcement

Crash Reduction Probability
Crash Reduction Probability

OOS
Violations
Non-OOS
Violations
OOS
Violations
Non-OOS
Violations
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

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 

Inspection
Roadside Inspection
Traffic Enforcement

Risk Category 1
Violations
Risk Category 2
Violations
Risk Category 1
Violations
Risk Category 2
Violations

OOS
Non-
OOS
OOS
Non-
OOS
OOS
Non-
OOS
OOS
Non-
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
(vrs-rcOOS  X  Prs-rcOOS) +  (vrs-rcNON  X  Prs-rcNON)  +  					(A-1)
(vte-rcOOS  X  Pte-rcOOS) +  (vte-rcNON  X  Pte-rcNON)  =  CRRrc-init

	where

vrs-rcOOS  =	the number of roadside out-of-service violations in a given risk category 
recorded during an inspection,

vrs-rcNON  =	the number of roadside non-out-of service violations in a given risk category 
recorded during an inspection,

vte-rcOOS  =	the number of traffic out-of-service violations in a given risk category 
recorded during an inspection,

vte-rcNON  =	the number of traffic non-out-of service violations in a given risk category 
recorded during an inspection,

Prs-rcOOS  =	crash risk probability for a given roadside out-of-service risk category,

Prs-rcNON  =	crash risk probability for a given roadside non-out-of-service risk category,

Pte-rcOOS  =	crash risk probability for a given traffic out-of-service risk category,

Pte-rcNON  =	crash risk probability for a given traffic non-out-of-service risk category, and

CRRrc-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
(vrs-rcOOS  +  vrs-rcNON  +  v te-rcOOS  +  v te-rcNON)  X  CRRrc-init  =  CRRRC		(A-2)

where

CRRRC  =	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 CRRRC 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

CRRRC1  +  CRRRC2  +    +  CRRRC5  =  IA						(A-3)

where

IA =	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: 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, CRRrc-init for each risk category, based on Equation (A-1):

Higher Bound

Risk Category 1, CRRrc1-init     (2 X .0125)	=  .025 

Risk Category 2, CRRrc2-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, CRRRC1		.05		=  .025        X  2
Risk Category 2, CRRRC2	        +	.04375	=  .00625  X  7
Total Crash Risk Reduction, IA	.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: 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	            Traffic  

Risk Category 1, CRRrc1-init	(2  X  .0125)      +  (1 X .05)   =  .075	    Using (A-1)

Risk Category 2, CRRrc2-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, CRRRC1		.225  =	.075      X  3		    Using (A-2)
Risk Category 2, CRRRC2	        +	.045  =	.01125  X  4		        and (A-3)
	Total Crash Risk Reduction, IA	.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.

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  =  IRS-A1 + IRS-A2 +  + IRS-An, 	(A-4)

where

IRS-A =	crashes avoided per roadside inspection for (1, 2, , n) roadside-initiated 
inspections.

Likewise,
Traffic Enforcement-initiated crashes avoided  =  ITE-A1 + ITE-A2 +  + ITE-Am,	(A-5)

where

ITE-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.

			  VTE
	Aadjustdirect  =  ----------								(A-6)
			  VTE+RS

where

VTE  =	traffic enforcement-initiated crashes avoided from only traffic-related 
violations,

VTE+RS  =	traffic enforcement-initiated crashes avoided from all violations, and

Aadjustdirect  =	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:

RSA-direct  =  ARS-direct  +  [(1 - Aadjustdirect)  X  ATE-direct]				(A-7)

and

	TEA-direct  =  Aadjustdirect  X  ATE-direct							(A-8)

where

ARS-direct  =	the pre-allocation crashes avoided total for roadside inspections,

ATE-direct  =	the pre-allocation crashes avoided total for traffic enforcements,

RSA-direct  =	the post-allocation direct effect crashes avoided total for roadside 
inspections, and

TEA-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. 

Equation (A-5):

Higher Bound
Roadside Inspection-initiated crashes avoided  =   IRS-A1  =  0.09375
Traffic Enforcement-initiated crashes avoided  =  ITE-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, CRRrc1-init	(1  X  .05)	=  .05 
	Risk Category 2, CRRrc2-init	(2  X  .005)	=  .01

Traffic Violations Only, Equation (A-2)

Higher Bound
	Risk Category 1, CRRrc1-init	.05    X  1	=  .05
	Risk Category 2, CRRrc2-init	.01  X  2	=  .02

Traffic Violations Only, Equation (A-3)

	Higher Bound
	Risk Category 1, CRRRC1			    .05
	Risk Category 2, CRRRC2			+ .02
	Total Crash Risk Reduction, IA		   .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					Traffic Total
	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.

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  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
	 ?  IAh											(A-9)
h = 1

Carrier Interventions per Year
Carrier interventions are the number of interventions a carrier had within a given year.
	   n
 ?  Ih											(A-10)
h = 1

where

I  =	intervention, and

IA =	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  
		      ?  IAh
        h = 1
	CAavg  =  -----------									(A-11)
        n 
		      ?  Ih
        h = 1

where

CAavg   =  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
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 
	ARS  =  (CAavg  Y1  -  CAavg  Y2)   X   ?  IY1-RS						(A-12)
					        RS = 1

where

CAavg  Y1 =	carrier average crashes avoided in Y1,

CAavg  Y2 =	carrier average crashes avoided in Y2,

IY1-RS  =	Y1 roadside inspection,

ARS  =	roadside inspection crashes avoided by a given carrier in subset S? due to RS 
(1, 2, , n) roadside inspections in Y1, and

the condition CAavg  Y1  >  CAavg  Y2, or subset S?, is met.

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

                  n 
	ATE  =  (CAavg  Y1  -  CAavg)  Y2)   X   ?  IY1-TE					(A-13)
         TE = 1

where

CAavg  Y1  =	carrier average crashes avoided in Y1,

CAavg  Y2  =	carrier average crashes avoided in Y2,

IY1-TE  =	Y1 traffic enforcement,

ATE  =	traffic enforcement crashes avoided by a given carrier in subset S? due to TE 
(1, 2, , n) traffic enforcements in Y1, and

the condition CAavg  Y1  >  CAavg  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.
      m
 	AS?-RS  =  ?  ARSi 									(A-14)
    i = 1

	where

AS?-RS  =	set S roadside inspection crashes avoided for i (1,2, , m) carriers in subset S?.

	Traffic
		      m
 	AS?-TE  =  ?  ATEi 									(A-15)
    i = 1

where

AS?-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
	?  Ij
	        j = 1
	D  =  ----------										(A-16)
 		  q
		?  Ik
	        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
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?.

		        AS?-RS
	GDIRS  =  ------------									(A-17)
		            l
 		           ?  Ij
            j = 1

where

GDIRS =	general deterrence impact per roadside inspection, and

AS?-RS  =	set S roadside inspection crashes avoided for j (1, 2, , l) interventions in 
subset S?.

Traffic
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?.

		        AS?-TE
	GDITE  =  ------------									(A-18)
		             l
		           ?  Ij
            j = 1

where

GDITE =	general deterrence impact per traffic enforcement, and

AS?-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 
	RI  =  ?  Ig  X  D									(A-19)
             g = 1

where

RI  =	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
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.

AR-RS  =  RI  X  GDIRS									(A-20)

where

AR-RS  =	set R crashes avoided from roadside inspections,

RI  =	the number of set R interventions positively influenced by deterrence, and

GDIRS =	general deterrence impact per roadside inspection.

Traffic
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.

AR-TE  =  RI  X  GDITE									(A-21)

where

AR-TE  =	set R crashes avoided from traffic enforcements,

RI  =	the number of set R interventions positively influenced by deterrence, and

GDITE =	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

	ARS-indirect  =  AS?-RS  +  AR-RS								(A-22)

where

ARS-indirect  =	the pre-allocation crashes avoided total for roadside inspections,

AS?-RS  =	set S roadside inspection crashes avoided, and

AR-RS  =	set R roadside inspection crashes avoided.

Traffic

	ATE-indirect  =  AS?-TE  +  AR-TE								(A-23)

where

ATE-indirect  =	the pre-allocation crashes avoided total for traffic enforcements,

AS?-TE  =	set S traffic enforcement crashes avoided, and

AR-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
	Aadjustindirect  =  ----------								(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

Aadjustindirect =	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

RSA-indirect  =  ARS-indirect +  [(1 - Aadjustindirect)  X  ATE-indirect]				(A-25)

and

Indirect effects crashes avoided from traffic enforcements

TEA-indirect  =  Aadjustindirect  X  ATE-indirect						(A-26)

where

ARS-indirect  =	the pre-allocation crashes avoided total for roadside inspections,

ATE-indirect =	the pre-allocation crashes avoided total for traffic enforcements,

RSA-indirect =	the post-allocation indirect effect crashes avoided total for roadside 
inspections, and

TEA-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.

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
						1.00496

	Carrier A Crashes Avoided in Y2 	0.00939
						0.07092
						0.02761
					        +	0.25032
						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 done for the Federal Highway Administration's 
(FHWA) Office of Motor Carriers (OMC),  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. 

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
?	Injury crashes:	1.60 injuries per crash

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

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
	RSA  =  RSA-direct  +  RSA-indirect							(A-27)

where

RSA  =	roadside inspection crashes avoided from both direct and indirect effects,

RSA-direct  = 	the post-allocation direct-effect crashes avoided total for roadside 
inspections, and

RSA-indirect =	the post-allocation indirect-effect crashes avoided total for roadside 
inspections.

Traffic
	TEA  =  TEA-direct  +  TEA-indirect							(A-28)

	where

TEA =	traffic enforcement crashes avoided from both direct and indirect effects,

TEA-direct  = 	the post-allocation direct-effect crashes avoided total for traffic 
enforcements, and

TEA-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
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.

RSA-Fatal  =  RSA  X  CSPFatal								(A-29)

RSA-Injury =  RSA  X  CSPInjury								(A-30)

where

RSA-Fatal  =	number of fatal crashes avoided due to the roadside inspection program,

RSA-Injury =	number of injury crashes avoided due to the roadside inspection program,

RSA  =	number of roadside inspection crashes avoided,

CSPFatal  =	proportion of all crash types that are fatal crashes, and

CSPInjury =	proportion of all crash types that are injury crashes.

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

TEA-Fatal  =  TEA  X  CSPFatal								(A-31)

TEA-Injury =  TEA  X  CSPInjury								(A-32)

where

TEA-Fatal  =	number of fatal crashes avoided due to the traffic enforcement program,

TEA-Injury =	number of injury crashes avoided due to the traffic enforcement program,

TEA  =	number of traffic enforcement crashes avoided,

CSPFatal  =	proportion of all crash types that are fatal crashes, and

CSPInjury =	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
	LSRS  =  RSA-Fatal  X  FCFatal								(A-33)

	where

LSRS  =	lives saved due to the roadside inspection program,

RSA-Fatal =	number of fatal crashes avoided due to the roadside inspection program, and

FCFatal  =	average fatalities per fatal crash.

	Traffic
	LSTE  =  TEA-Fatal  X  FCFatal								(A-34)

	where,
LSTE  =	lives saved due to the traffic enforcement program, and

TEA-Fatal =	number of fatal crashes avoided due to the traffic enforcement program, and

FCFatal  =	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
	IARS  =  (RSA-Fatal  X  FCInjury)  +  (RSA-Injury  X  ICInjury)				(A-35)

	where

IARS  =	number of injuries avoided due to roadside inspections,

RSA-Fatal =	number of fatal crashes avoided due to the roadside inspection program,

RSA-Injury =	number of injury crashes avoided due to the roadside inspection program,

FCInjury  =	average injuries per fatal crash, and

ICInjury =	average injuries per fatal crash.

	
Traffic
	IATE  =  (TEA-Fatal  X  FCInjury)  +  (TEA-Injury  X  ICInjury)				(A-36)

	where

TEA-Fatal  =	number of fatal crashes avoided due to the traffic enforcement program,

TEA-Injury =	number of injury crashes avoided due to the traffic enforcement program,

FCInjury  =	average injuries per fatal crash, and

ICInjury  =	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

 

Table B-1.  Roadside Inspection Violations (continued)

Table B-1.  Roadside Inspection Violations (continued)

 

Table B-1.  Roadside Inspection Violations (continued)

 

Table B-1.  Roadside Inspection Violations (continued)

Table B-1.  Roadside Inspection Violations (continued)

 

Table B-1.  Roadside Inspection Violations (continued)

 

Table B-1.  Roadside Inspection Violations (continued)

 

Table B-1.  Roadside Inspection Violations (continued)

Table B-1.  Roadside Inspection Violations (continued)

 

Table B-1.  Roadside Inspection Violations (continued)

 

Table B-1.  Roadside Inspection Violations (continued)

 

Table B-2.  Traffic Enforcement Violations

Table B-2.  Traffic Enforcement Violations (continued)

APPENDIX C  PROGRAM BENEFITS

C.1.  NATIONAL PROGRAM BENEFITS

Table C-1a.  National Program Benefits, 1998

Table C-1b.  National Program Benefits, 1999

Table C-1c.  National Program Benefits, 2000

C.2.  ROADSIDE INSPECTION BENEFITS, BY STATE

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

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

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

C.3.  TRAFFIC ENFORCEMENT BENEFITS, BY STATE 

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

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

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

  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.
  Revised figures.  See Section 4.5 for details.
  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, 2001.
  For a complete list of driver and vehicle violations associated with the roadside inspections and traffic 
enforcement, see Appendix B.
  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.

  See Appendix A for the explanation of how the relative weights from Cycla were converted into crash risk 
probabilities.
  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.

  Ibid, p. 21.
  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.

  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.

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

  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.
  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.
  "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.

  See http://www.inspector.org/37stepin.htm.
   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."
  Crash causation studies are underway at the University of Michigan Transportation Research Institute and the 
NHTSA.

  Ibid.
  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.
  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).
  An area for future investigation consists of motor carriers who registered no improvement in average crashes 
avoided.
  Center for National Truck Statistics, University of Michigan Transportation Research Institute, Truck and Bus 
Crash Factbook 1995, 1997.

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

  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.

8

viii

1-2

2-2

3-8

4-3

A-32

B-15

C-13