CHAPTER
5. CVISN SAFETY
BENEFITS
In 1998[1],
5,374 people were killed and approximately 127,000 were injured in crashes
involving approximately 412,000 large commercial motor vehicles (CMVs). The FMCSA has set as one of its primary
objectives the reduction of CMV-related fatalities and injuries by
50 percent by 2010. Although
new research, such as the Large Truck Crash Causation project (FMCSA 2001), will
help FMCSA better understand the causes of these crashes, vehicle safety defects
and driver violations of the Federal Motor Carrier Safety Regulations (FMCSRs)
are known to contribute to a portion of them (VNTSC
1999a).
The most important benefit expected from the deployment of CVISN
technologies, especially electronic screening and safety information exchange,
is a reduction in CMV‑related crashes through improved enforcement of the
FMCSRs. The principal hypothesis to
be tested is that CVISN technologies will help enforcement staff focus
inspection resources on high‑risk carriers. This will result in more out-of-service
(OOS) orders for the same number of inspections—thereby removing from service
additional trucks and drivers that would have caused crashes because of vehicle
defects and driver violations of safety regulations. A second hypothesis is that the
increased attention on high-risk carriers will encourage motor carriers to
improve their compliance with safety regulations. This indirect benefit is the number of
crashes that would have been caused by violations in safety regulations, but are
avoided due to improved compliance.
As outlined in the CVISN MDI Summary Evaluation Plan (Battelle 1998), the
safety benefits analysis addresses the following four
questions:
·
What is the impact of CVISN
on the numbers of crashes, injuries, and fatalities involving large
CMVs?
·
What is the impact of CVISN
on rates of driver and carrier compliance with the FMCSR?
·
To what extent does CVISN
help roadside safety enforcement officials identify high‑risk commercial
vehicles and motor carriers?
·
To what extent does CVISN
help roadside safety enforcement officials identify OOS
violators?
The CVISN safety benefits analysis was performed using a probability
model that predicts the number of crashes avoided under various scenarios. Each scenario is defined by specific
assumptions concerning the future deployment of CVISN. The probability model relates the number
of crashes avoided to several input parameters including the probability that a
CMV has an out‑of‑service (OOS) condition, the number of inspections performed,
historical rates at which OOS orders were issued, national crash/injury/fatality
rates involving large trucks, and probabilities that certain OOS conditions will
contribute to a crash. Estimates of
these inputs were obtained from the literature or from data collected in several
special studies conducted in states that had previously deployed—or were in the
process of deploying—CVISN safety information exchange and electronic screening
technologies. States that
participated in these studies were Connecticut, Kentucky, and
Oregon.
Section 5.1 contains an overview of our findings. Five scenarios are presented to
illustrate the safety benefits of CVISN under different deployment options and
assumptions concerning the potential outcomes. The technical approach discussion in
Section 5.2 describes the probability model and summarizes the design of the
special studies that were undertaken to obtain outcome measures used in the
model. Section 5.3 shows the
calculation of safety benefits for each scenario. Supporting analyses are presented in
Section 5.4.
Limitation of
Findings
The analysis contained in this chapter uses a probability model to
predict the number of truck-related crashes that would be avoided nationwide as
CVISN deployment expands. Although
the model can be justified by basic principles of probability, its application
relies on a variety of input parameters used to estimate impacts and benefits of
CVISN. Some of the parameters were
estimated using results from the open literature on crashes and highway
statistics, and others were estimated with data collected in special studies
involving participating CVISN states.
Both types of estimates are subject to errors of unknown
magnitude.
Some of the literature results were derived from a related FMCSA program
(VNTSC 1999a), which was reviewed by an expert panel (Nicholson 1998). The panel expressed concern that the
estimates of crash causation probabilities were based on limited data. However, no alternative approach was
recommended. Currently, FMCSA is in
the process of developing a new data collection program that has the potential
to fill this information gap.
Estimates of CVISN impacts and benefits obtained from the special CVISN
studies should also be used with caution.
There were few if any opportunities to replicate the studies in different
states in order to determine the statistical uncertainty of the estimates. In some cases the data limitations were
the result of CVISN deployment delays or reduced levels of deployment of
specific technologies.
Although additional data are needed to support these results, the safety
analysis presented in this chapter helps to illustrate how the deployment of
CVISN can affect highway safety.
The analysis can be easily modified as new data become
available.
5.1
Overview of
Results
This section contains an overview of the results of our safety benefits
analysis. Results were obtained by
combining analyses of selected roadside enforcement data with literature results
to project the benefits of these technologies under five CVISN roadside
enforcement (RE) deployment scenarios that incorporate the anticipated impacts,
and a baseline scenario representing current enforcement practices. The deployment scenarios are defined as
follows:
RE-0: Baseline—Pre-CVISN. Enforcement officers (inspectors) select
CMVs for inspection using personal experience and judgment, but without the aid
CVISN technologies.
RE-1: ISS with Manual
Pre-screening. Inspectors are equipped with laptop
computers containing Aspen and ISS.
CMVs are pre-screened based on weigh-in-motion (WIM) and/or visual
screening on a sorter ramp. Of the
CMVs directed to the fixed scale, officers use ISS to select vehicles for
inspection.
RE-2: ISS with Electronic
Screening. State deploys electronic screening with
safety snapshots at all major inspection sites. Motor carriers classified as “low-risk,”
based on SafeStat scores, enroll in the electronic screening program. Trucks from the low-risk carriers
(comprising approximately 52 percent of trucks on the road) are equipped with
transponders and allowed to bypass inspection sites. Inspectors use ISS in the manner
described in RE-1 to select vehicles for inspections from the remaining 48
percent of trucks in the high, medium, or unknown/ insufficient data risk
categories.
RE-3: ISS with Electronic
Screening and a Reduction in OOS Conditions Due to Improved FMCSR Compliance by
Motor Carriers. Motor carriers respond to targeted
enforcement by improving compliance with safety regulations. Specifically, we assume that the total
number of vehicle and driver OOS conditions will decrease by 25 percent due to
improved compliance. Enforcement is
conducted as in RE-2. (25 percent
is an assumed value to illustrate potential impacts of improved compliance on
crash reductions. The sensitivity
of this assumption is assessed by also performing the analysis with an assumed
reduction of 10 percent – referred to as scenario RE-3*. At this time, there is no statistical
evidence that targeted enforcement will have such effects on safety violation
rates.)
Deployment RE-1 represents a current application of ISS for vehicle
selection (e.g., Connecticut’s Greenwich and Union weigh stations). Scenarios RE-2 and RE‑3 (or RE-3*)
represent “feasible” situations that could occur as CVISN deployment
expands. Benefit/cost analyses for
these three scenarios are presented in Chapter 8. However, to illustrate the limits of the
direct and indirect benefits of CVISN roadside enforcement technologies, we
consider two additional hypothetical scenarios:
RE-4: 100 Percent Inspection Selection
Efficiency. State deploys electronic screening with
safety snapshots at all major inspection sites, and all trucks are equipped with
transponders. Safety analysis and
screening algorithms have progressed to the point that OOS violations can be
identified with near certainty.
Therefore, all inspections result in OOS orders. Although not expected to occur, this
scenario is used to illustrate the direct benefits of maximizing the efficiency
of roadside enforcement operations.
RE-5: 100 Percent Compliance with Safety
Regulations. In response to targeted enforcement,
violations of vehicle and driver safety regulations are eliminated. Although not expected to occur, this
scenario represents the maximum possible benefit (direct and indirect) of
improved enforcement.
Except as described in scenarios RE-3 and RE-5, the rates of compliance
with vehicle and driver safety regulations are based on estimates obtained in
the FMCSA’s National Fleet Safety Survey, or NFSS (Star 1997). The NFSS collected data from over 10,000
random Level I (driver and vehicle) inspections and estimated that 29
percent of all commercial vehicles and 5 percent of commercial vehicle
drivers were operating with OOS conditions. For all of these scenarios, it is
assumed that the numbers and types of inspections performed annually are
constant and equal to the numbers reported by FMCSA’s Motor Carrier Safety
Assistance Program (MCSAP) Quarterly Report Information System for Fiscal Year
1998 (FMCSA 1999)—the last year for which complete crash statistics are
available. Additional information
from the literature, such as annual crash/injury/fatality rates, numbers of
inspections performed and OOS orders issued, and crash causation statistics are
discussed in Sections 5.2 and 5.3, along with the methods and analyses used
to determine the crash reduction benefits of CVISN.
The impacts of CVISN technologies on roadside enforcement operations were
evaluated through special studies conducted in participating states. However, because CVISN is still in the
early stages of deployment, especially in the area of electronic screening with
safety snapshots, opportunities to evaluate these impacts directly were
limited. The following results,
obtained from CVISN pilot states, provide useful insight into these effects;
however, the degree to which these results are statistically representative of
future deployments could not be determined:
1. A
study of roadside inspection selection strategies at four Connecticut inspection
sites (two using ISS and two without ISS) demonstrated that using ISS, in
combination with manual prescreening, to select commercial vehicles for
inspection increases OOS orders by approximately 2 percent for the same number
of inspections—a 2 percent increase in inspection
efficiency.
2.
Analysis of this same inspection selection strategy under the added
assumption that “low-risk” carriers would be permitted to bypass the inspection
sites demonstrates that electronic screening, with full participation by all
low‑risk carriers, could increase inspection efficiency by more than 11
percent.
3. A
two-year study of the changes in safety compliance rates in Oregon, conducted
during the deployment of roadside screening and safety information exchange
technologies, failed to demonstrate that CVISN roadside deployment will increase
compliance with safety regulations.
However, in designing the study it was anticipated that advanced safety
screening technologies would be deployed during the second year; but deployment
of these systems was delayed, which made it difficult to observe the expected
impact on safety compliance. As
discussed in scenario RE-3, the 25 percent reduction (or 10 percent for scenario
RE-3*) in safety violations is assumed for illustration
purposes.
These estimated and assumed effects of CVISN deployment, along with
results from the literature, were applied to a crash avoidance model (described
in Section 5.2) to predict the numbers of truck-related crashes and associated
injuries and fatalities that would be avoided under each of the above roadside
enforcement scenarios. Table 5-1
summarizes the major results of this analysis. Supporting details are provided in
Section 5.3.
Table 5-1. Estimated
Safety Benefits of CVISN Under Selected Deployment Scenarios and
Assumptions
|
Scenario |
Description |
Numbers of Safety Events
Avoided1 |
Additional2 Safety
Events Avoided (CVISN Benefit) | ||||
|
Crashes |
Injuries |
Fatalities |
Crashes |
Injuries |
Fatalities | ||
|
|
Random
Selection |
3,765 |
1,160 |
49 |
|
|
|
|
RE-0 |
Baseline
(pre-CVISN) |
4,423 |
1,362 |
57 |
|
|
|
|
RE-1 |
ISS
with manual prescreening |
4,507 |
1,388 |
59 |
84
|
26 |
2 |
|
RE-2 |
RE-1 plus electronic bypass of
low-risk carriers |
5,012 |
1,544 |
65 |
589 |
181 |
8 |
|
RE-3 |
RE-2 plus 25% reduction in
safety violations |
14,368 |
4,425 |
187 |
9,945 |
3,063 |
130 |
|
RE-3* |
Same as RE-3 except with a 10%
reduction in safety violations |
8,755 |
2,697 |
114 |
4,332 |
1,335 |
57 |
|
RE-4 |
100% inspection selection
efficiency |
10,561 |
3,253 |
137 |
6,138 |
1,891 |
80 |
|
RE-5 |
100% compliance with safety
regulations |
42,436 |
13,070 |
552 |
38,013 |
11,708 |
494 |
1 In 1998, approximately
412,000 large trucks were involved in crashes resulting in 127,000 injuries and
5,374 fatalities. The estimated number of crashes avoided is based the
assumption that crashes are avoided when vehicles and drivers with safety
violations are placed out-of-service.
2 Compared to baseline scenario
(RE-0)
According to the model,
current roadside enforcement strategies (RE-0) are responsible for avoiding
4,423 truck-related crashes, which represents slightly more than 1 percent of
the 412,000 truck-related crashes that occur annually, based on 1998 crash
statistics (FMCSA 2000b).
Assuming that the numbers of injuries and fatalities are proportional to
the number of crashes, it is estimated that current roadside enforcement
activities are responsible for preventing 1,365 injuries and 57
deaths.
For reference, the numbers of crashes, injuries, and fatalities that
would be avoided if vehicles were randomly selected for inspection
(Random Selection) were also calculated and shown in Table 5-1. The differences between these numbers
and the baseline numbers can be used to estimate the benefits of current
inspection selection strategies, which include the training, knowledge, and
experience that the inspectors bring to the job.
The safety benefits of CVISN
are obtained by subtracting the numbers of crashes, injuries, and fatalities
avoided under the baseline scenario from the corresponding numbers under
scenarios RE-1 to RE-5. For
example, if ISS were used to select vehicles for inspection following manual
pre-screening on sorter lanes, as currently performed at two sites in
Connecticut, an additional 84 crashes, 26 injuries, and two fatalities could be
avoided. If electronic screening is
added, and all low-risk carriers enroll and are permitted to bypass inspections,
enforcement staff could focus inspections on more high-risk carriers. The increased numbers of OOS orders for
the same number of inspections would help avoid 589 additional truck‑related
crashes as well as 181 injuries and 8 fatalities. Although there is no direct evidence
concerning the degree to which safety compliance improves with enhanced
enforcement, scenarios RE-3 and RE-3* demonstrate the substantial safety
benefits that would occur if CVISN‑enhanced enforcement strategies helped to
encourage improved compliance with safety regulations.
Scenarios RE-4 and RE-5 are presented to illustrate the potential benefit
of CVISN under limiting conditions at full deployment. Clearly it is not realistic to expect
roadside enforcement to achieve 100 percent efficiency in selecting
vehicles with OOS conditions.
However, it is conceivable that advances in safety analysis, through
programs such as the Large Truck Crash Causation Study, combined with enhanced
data reporting capabilities offered by CVISN, would help to identify carriers
that might pose exceptionally high risks.
The additional 6,138 crashes avoided under scenario RE-4 represent the
maximum benefit of this enhanced enforcement capability. Of course, the maximum number of crashes
that could be avoided with any improvement related to safety compliance is
42,436, of which only 38,013 would be attributable to the indirect benefit of
CVISN or any other program that helps to increase compliance with safety
regulations. The resulting
reduction in injuries (11,708) and fatalities (494) represent 9 percent of the
numbers that occurred in 1998.
Thus, the analysis of this limiting condition demonstrates the maximum
degree to which CVISN can contribute to FMCSA’s goals of reducing the number of
injuries and fatalities from truck‑related crashes by 50 percent by the
year 2010.
The model used to calculate the number of crashes avoided is illustrated
in Figure 5‑1. The direct
effect of improved inspection efficiency (OOS orders
per 100 inspections) is represented by the contour lines, which
determine the number of crashes avoided for a given level of compliance. As inspection efficiency increases, the
number of crashes avoided increases up to a limit. Recall that scenario RE-4 produces the
maximum number of crashes avoided without increasing safety compliance. Note that the lower (realistic) limit on
inspection efficiency corresponds to random selection (percent OOS orders equals
the violation rate). The successive
lines, from bottom to top, represent the indirect impact of reducing the safety
violation rates by 25 percent, 50 percent, 75 percent, and 100 percent. The results (total crashes avoided) for
the scenarios described above are marked in Figure 5-1.
Figure 5-1: Number of Crashes Avoided versus Inspection
Selection Efficiency at Selected Levels of Reduction in Vehicle/Driver Safety
Regulation Violation Rates (Showing Approximate Locations of Estimates for
Selected Scenarios)
5.2
Technical
Approach
The CVISN safety benefits estimation methodology is based on a
probability model that relates the improvement in safety—as measured by the
numbers of crashes, injuries, and fatalities avoided—to the number of OOS orders
issued and other CMV safety parameters such as crash rates, violation rates, and
crash causation statistics. Data
were collected from published sources as well as new, special studies designed
to provide input to the model.
The four study questions presented on page 5-1 provide the basis for the
safety benefits estimation methodology.
While the first of these questions addresses the heart of the safety
benefits of CVISN, the particular CVISN technologies that are included in the
MDI achieve these safety benefits only through improvements in the enforcement
of vehicle and driver compliance with safety regulations. The remaining three questions, which
provide the main focus for the safety evaluation, examine the relationship
between CVISN deployment and its impact on enforcement practices. Results from the literature and new
studies were used to address the latter three questions. Once those questions were answered, a
statistical model was used to relate their answers to the question of the effect
of CVISN on crashes, injuries, and fatalities.
The second and third questions indicate that CVISN technologies might be
expected to help improve compliance with safety regulations in two ways, both
resulting from increased effectiveness of roadside inspection operations. The direct, but smaller, impact
is the removal of unsafe drivers and vehicles from the highways. The third question addresses the direct
effect by examining the impact that electronic screening and safety information
exchange technologies will have on the inspectors’ ability to select commercial
vehicles for inspection in the most efficient manner. The indirect effect, which is
expected to be much larger, occurs when drivers and carriers modify their
behavior in response to the improved, more targeted inspections. The second question addresses this
indirect effect. The hypothesis to
be tested is that carriers will expend resources to ensure that their vehicles
stay in compliance. Carriers with
good safety records (low-risk carriers) would expect to have a small probability
of being inspected. High‑risk
carriers will try to improve their safety rating to avoid increased
inspections.
The fourth question focuses on a different aspect of safety enforcement,
that of identifying violators of OOS orders. Safer Data Mailbox (SDM) is an
electronic database of inspection records that has been designed to provide
safety enforcement agencies with a national database of inspection
information. Queries to this
database provide information about any inspection of a vehicle within the past
45-day period. The extent to which
it is used by inspectors in the field will indicate the extent to which OOS
violators can be identified.
The following sections describe (1) the sources of data obtained from the
literature and from special studies that were conducted to quantify the impacts
of CVISN on roadside safety enforcement, and (2) the crash avoidance model used
to estimate CVISN safety benefits.
Data
Sources
Table 5-2 lists some key safety statistics obtained from the published
literature. Most of these data are
used in the crash avoidance analysis; others are provided for reference. According to FMCSA, 7.2 million large
trucks (>10,000 pounds gross vehicle weight) travel approximately
196 billion miles in the U.S. each year. In 1998, the last year for which
complete statistics are available, 412,000 trucks were involved in crashes,
resulting in approximately 127,000 injuries and 5,374 deaths. The corresponding rates per vehicle mile
traveled are derived from these values.
Other relevant statistics provided in Table 5-2 include the number
of commercial vehicle inspections performed in 1998 and the actual percentages
of OOS orders issued (25.5 percent of vehicles and 8.1 percent of
drivers). In 1996 FMCSA sponsored
the National Fleet Safety Survey (NFSS), in which 10,000 trucks were selected at
random for inspection in order to estimate the percentages of trucks and drivers
that operate with OOS conditions (i.e., violation rates). These estimates differ from the actual
OOS rates because inspectors choose vehicles for inspection based on vehicle
appearance and apply their knowledge and experience. The estimated violation rates reported
by the NFSS were 29 percent for vehicles and 5 percent for drivers
(Star 1997).
In order to determine the
impact of removing OOS violators from the roadway on the number of crashes, it
is necessary to estimate certain probabilities associated with crash
causation. Specifically, the
probabilities that a crash involving a large truck was caused by vehicle and
driver OOS conditions are needed.
FMCSA recognizes that information on the causes of large truck crashes is
lacking and, therefore, recently initiated the Large Truck Crash Causation Study
(2001), which will soon begin collecting the necessary data. In the meantime, this analysis uses the
best available information, which FMCSA used to evaluate the safety benefits of
roadside inspections (VNTSC 1999a).
These data issues are discussed more fully in the next section along with
the explanation of the crash avoidance model.
Table
5-2. Relevant Safety and
Safety Enforcement Statistics on Large Trucks
|
Statistic
Description |
Value |
Source
1 |
|
Number
of large trucks |
7.2
million |
Safety
Action Plan (FMCSA 2000a) |
|
Large
truck annual vehicle miles traveled (VMT) |
196
billion |
Safety
Action Plan (FMCSA 2000a) |
|
Number
of registered interstate motor carriers |
500,000 |
Safety
Action Plan (FMCSA 2000a) |
|
Number
of large trucks participating in electronic screening
(2000) |
150,000 |
What
Have We Learned (FHWA 2000) |
|
Large
trucks involved in crashes (1998) Injuries
from large truck crashes (1998) Fatalities
from large truck crashes (1998) |
412,000 127,000 5,374 |
1998
Crash Profile (FMCSA 2000b) |
|
Large
trucks involved in property damage-only crashes Large
trucks involved in injury-only crashes Large
trucks involved in fatal crashes |
318,000 89,000 4,935 |
1998
Crash Profile (FMCSA 2000b) |
|
Large
truck crash rate (truck crashes/100 million VMT) = 412,000 truck crashes/196 billion
VMT |
210.2 |
Derived |
|
Large
CMV injury crash rate (crashes/100 million VMT) =89,000 truck crashes/196 billion
VMT |
45.4 |
Derived |
|
Large
CMV fatal crash rate (crashes/100 million VMT) =4,935 truck crashes/196 billion
VMT |
2.5 |
Derived |
|
Commercial
vehicle (non-bus) inspections performed (1998) Commercial
vehicle (non-bus) driver inspections (1998) Total
CV (non-bus) inspections (driver and vehicle)
(1998) |
1,562,739 2,089,846 |