**[National Fleet Safety Survey, 1996]{.underline}**

**Final Analysis Report**

**Prepared for:**

Federal Highway Administration

Office of Motor Carriers

U.S. Department of Transportation

Washington, D.C. 20590

**Prepared by:**

**Star Mountain , Inc.**

3601 Eisenhower Avenue

Alexandria, VA 22304

**STAR MOUNTAIN** project \# 4284-016

March 25, 1997

**TABLE OF CONTENTS**

EXECUTIVE SUMMARY

I. INTRODUCTION

II\. BACKGROUND

III\. FINDINGS

Distribution of Violations for All Trucks

HazMat (HM) Data

Distribution of Violations

Differences Between Facility types, Road types, and Time of Inspection

Supplemental Questions

IV\. CONCLUSION

APPENDIX I: description of level I vehicle inspection procedure

APPENDIX II: sample design and estimation procedures

Selection of States

Selection of Sites and Trucks

Theory

Estimation Methodology

State Level Estimates

HazMat-Specific OOS Rates

# 

#  

**LIST OF TABLES**

# 

**Table 1: Unweighted and VMT Weighted Out-of-Service Rates based on
Level I Inspections for All Trucks, by State 7**

**Table 2: Out-of-Service Rates for All Trucks by State, Weighted by
State VMT ad County Average Trucks Counts 9**

**Table 3: Most Frequent Violation Codes Resulting in an OOS Order
During Level I Inspections from the \"All Trucks\" Sample 11**

**Table 4: Distribution of OOS Violations per Inspections for Trucks
Placed Out-of-Service 12**

**Table 5: Out of Service Rates for Trucks Carrying HM, by State, Based
on Level I Inspections 13**

**Table 6: Most Frequently Found Federal Violation Codes Resulting in an
OOS Order During Level I Inspections for Trucks Carrying HM 15**

**Table 7: Overall OOS Rates by Facility Type, Road Type, and Time of
Day for \"All Trucks\" and \"HM\" Samples 17**

**Table 8: Raw OOS Rates by Driver Experience, Training , and Type of
Load for "All Trucks" and "HM" Samples 19**

**Table 1A: 1994 MCSAP Level I OOS Rates for States Selected into
Sample, by OOS Stratum 28**

**Table 2A: Number of Inspections Required and Achieved, by State by
Road Type for the \"All Trucks\" Sample 30**

**Table 3A: 1994 Truck VMT (millions) by State by Road Type 30**

#  

#  EXECUTIVE SUMMARY

This report describes both the methodology and findings from the 1996
National Fleet Safety Survey, conducted by the Federal Highway
Administration's (FHWA) Office of Motor Carriers (OMC), in conjunction
with the Research and Special Programs Administration (RSPA) of the
Department of Transportation (DOT). In this study, data were collected
from over 10,000 random Level I truck inspections and were used to
estimate overall, driver-specific, and vehicle-specific out-of-service
(OOS) rates for the general population of commercial vehicles on the
road and for the subpopulation of commercial vehicles carrying hazardous
materials (HM). These estimated rates represent weighted estimates,
using a weighting scheme based on State vehicle miles traveled (VMT) for
trucks.

The key findings of the study are as follows:

-   The National overall OOS rate, based on Level I inspections, is
    estimated to be 32 percent. National vehicle-specific and
    driver-specific OOS rates are estimated to be 29 and 5 percent,
    respectively. These rates are slightly lower than similar rates
    obtained from FY 1996 MCSAP data and these differences appear real
    (i.e., statistically significant).

-   For the subpopulation of trucks carrying HM, the National overall
    OOS rate, based on Level I inspections, is estimated to be 27
    percent. National vehicle-specific and driver-specific OOS rates are
    estimated to be 25 and 4 percent, respectively.

-   For the general population of trucks, the most frequently found
    OOS-related violations pertain to brakes. Approximately 49 percent
    of all OOS-related violations found in the survey were
    brake-related, the most common of these violations involving Federal
    Violation Code 396.3A1BA (brakes out of adjustment). For the
    subpopulation of trucks carrying HM, the results were similar.
    Approximately 51 percent of all OOS-related violations found were
    brake-related, and the most common of these violations, again,
    involved Federal Violation Code 396.3A1BA.

-   The study did not find appreciable differences in OOS rates between
    types of inspection facilities (fixed vs. mobile), times of
    inspection (day vs. evening), types of load, driver years of
    experience, or driver years of training. For the subpopulation of
    drivers with little experience (three years or less), however, the
    survey found that those with no training had a higher OOS rate when
    compared to those drivers receiving some form of training (51
    percent vs. 35 percent).

-   The overall OOS rates for the general population of trucks inspected
    at urban sites are slightly lower than for those inspected at rural
    sites (30 percent for urban interstate vs. 33 percent for rural
    interstate; 29 percent for urban noninterstate vs. 34 percent for
    rural noninterstate).

-   5.6 percent of all randomly sampled trucks in the survey were found
    to be carrying HM. Based on these data, the weighted national
    estimate of the percentage of operating trucks carrying HM is 7.2
    percent. Seventy-eight percent of those trucks sampled requiring HM
    registration were found to have the correct documentation onboard.
    HM carriers with appropriate registration documentation had an
    overall OOS rate of 24%, compared to 31% for those lacking such
    documentation.

#  I. INTRODUCTION 

This report presents findings from the 1996 National Fleet Safety
Survey, conducted by the Federal Highway Administration's (FHWA) Office
of Motor Carriers (OMC), in conjunction with the Research and Special
Programs Administration (RSPA) of the Department of Transportation
(DOT). In this survey, a stratified random sample of over 10,000 trucks
was used to assess the level of compliance with the Federal Motor
Carrier Safety Regulations (FMCSRs) and with the Hazardous Materials
Regulations (HMRs).

Specifically, this study estimates Out-of-Service (OOS) rates (both
driver-related and vehicle-related) for the general population of
commercial vehicles on the road and for the subpopulation of commercial
vehicles carrying hazardous materials (HM), based upon random Level I
inspections performed during June and July of 1996 in 11 states. For the
purposes of this analysis, the OOS rate is defined as the proportion of
operating commercial motor vehicles having at least one FMCSR or HMR
violation severe enough to require the vehicle to be placed
out-of-service for a prescribed period of time (in the case of a
driver-related violation) or until the vehicle defects are corrected (in
the case of a vehicle-related violation).

# 

#  II. BACKGROUND

FHWA, in conjunction with State enforcement authorities, has routinely
inspected trucks in operation on the highways since the mid-1950s. These
inspections are currently performed under FHWA's Motor Carrier Safety
Assistance Program (MCSAP). MCSAP inspections take place at permanent
and portable truck weigh stations, at special inspection pads, at port
of entry facilities and carriers' terminals, at hazardous waste
stations, at rest stations, and at other locations. Inspections are
conducted in accordance with standards developed by OMC in cooperation
with the Commercial Vehicle Safety Alliance (CVSA). These standards
allow for national uniform inspection procedures as well as provide
criteria for identifying out-of-service violations (violations of the
FMCSR or HMR severe enough to place the vehicle out-of-service). Five
different kinds of inspections are performed under MCSAP, the most
comprehensive of which is the Level I-type inspection used in this
study. Level I inspections involve a full examination of the vehicle (by
conducting a walk-around and by examining underneath the vehicle) as
well as an inspection of all driver records. A detailed description of
the vehicle component of the Level I inspection procedure is given in
Appendix I.

Trucks generally are targeted for inspection under MCSAP on a selective
basis: if a cursory auditory and visual examination of the truck reveals
potential violations, it is inspected. Otherwise, vehicles and drivers
are randomly selected. As a result, OMC's Motor Carrier Management
Information Systems (MCMIS) data, containing information on all
inspections conducted every year, cannot be considered a representative
sample of commercial motor vehicles and their drivers on the roads.

As far back as 1969, representatives of the trucking industry alleged
that OMC's (at that time, the Bureau of Motor Carrier Safety) published
data on roadside inspections were not representative of the true state
of vehicles on the road. As a result of this claim, the Bureau conducted
a controlled study to determine whether its published out-of-service
rates differed substantially from what would be obtained from a
statistical random sample of trucks. This sample was implemented during
the summer of 1971.

For the 1971 study, a sampling frame of 156 fixed inspection sites was
developed. A sample of sites was then selected from four geographic
regions across the country. Prior to sample selection, all sites on the
sampling frame were further classified within each region as "urban" or
"rural" (based on traffic volume). One rural and one urban site were
then selected into the sample from each region (using a "probability
proportional to size selection procedure" based on traffic volume). At
each selected site, time intervals were then randomly selected to
inspect trucks. The total sample consisted of 1,172 trucks. Based on the
available documentation from this study, it is not clear, however,
whether all inspections performed for this investigation were Level I
inspections, although this seems most likely.

The 1971 survey found that 28 percent of the trucks inspected were in an
"out-of-service" condition. This compares to approximately 40 percent of
trucks being ordered out-of-service from the regular inspections for
1970. Based on these results, FHWA concluded that targeting for
inspection those vehicles appearing the least well maintained did not
produce a significant bias in their published data.[^1]

Since many inspections under MCSAP are still performed selectively and
not randomly, OMC decided in 1995 to reassess the baseline
out-of-service rate (percentage of trucks operating on the highways in
an out-of-service condition) by means of a random sample.

A total of 17 States initially expressed an interest in participating in
the 1996 study. Given manpower and other constraints, OMC decided that
12 States could be accommodated by the new study. One State, Georgia,
later withdrew from the study.

For the 1996 study, an overall minimum sample size of 5,000 trucks was
decided upon. To ensure that the sample had a sufficient number of
trucks carrying hazardous materials to allow for the production of
separate OOS rate estimates for such vehicles, the random truck
selection procedure was supplemented with an oversampling procedure for
trucks with HM: States were instructed to set aside one day where only
trucks carrying HM would be sampled.

Fixed and non-fixed inspection sites were selected within each State
from each of the following road-type categories: (1) Rural Interstate
roads, (2) Rural Non-interstate roads, (3) Urban Interstate roads, and
(4) Urban Non-interstate roads. The selection of these sites was left to
each State and was not random but purposive, although states were
instructed to select as many different kinds of sites as possible,
including at least one non-fixed site per road-type category. The States
were given only the minimum number of sites to be selected within each
road-type category; the maximum number of sites to be selected in each
of these categories was not specified. Although, the required minimum
number of inspections was not always achieved in each road-type category
in all States (see Appendix II), the overall sample size achieved turned
out to be quite large, exceeding 10,000 inspections and including over
1,300 inspections of trucks carrying HM.

To ensure that the minimum number of HM-related inspections was
achieved, the States were requested to set aside one day during the data
collection period for collecting only HM-related data. This suggested
procedure was followed in nine States. In some States, the final sample
of HM-specific inspections was too sparse in particular road-type strata
to allow weighting by road type in the production of HM-specific
estimates. As a result only national estimates were weighted for HM
specific data (see Appendix II).

At each inspection site, trucks were randomly selected for Level I
inspections. Upon completing an inspection, the inspector would select
another truck by choosing the third truck passing through the site. The
volume of trucks passing each site was measured by taking a five minute
truck count prior to the beginning of the inspectors' work shift. The
survey sampling design and the estimation procedures used are discussed
in more detail in Appendix II.

#  III. FINDINGS

Table 1 presents both weighted (by VMT) and unweighted estimates of OOS
rates for each State and for the total U.S., for the overall population
of trucks. Similar information based on the weighting procedure
incorporating the truck counts from the sites (see Equation 3, Appendix
II) is presented in Table 2. For the State level estimates, the VMT
weighted estimates represent a weighted average (weighted by VMT) of the
raw OOS rates found in each road-type category. The national estimate
represents a VMT weighted average of the OOS estimates obtained for the
States (see Appendix II). The total number of inspections available for
analysis was 10,167. Two hundred eight of the inspection data records,
however, did not have the proper road-type stratum identification
necessary for the production of weighted estimates. As a result, the VMT
weighted estimates are based on a total sample size of 9,959
inspections.

As shown in Table 1, the raw unweighted OOS rate for all States combined
is 34 percent. When the data are adjusted for VMT, the estimated rate is
31 percent for the 11 States combined, and 32 percent for the Nation as
a whole. National estimates of driver and vehicle OOS rates are 5 and 29
percent, respectively. Note that the driver and vehicle OOS rate
estimates in the table do not necessarily sum to the overall weighted
OOS rate. This stems from the fact that the categories are not mutually
exclusive. That is, both a vehicle and its driver may be placed
out-of-service at the same inspection.

Table 1 indicates that, for the individual States, the estimated overall
OOS rates (weighted) range between 30 and 39 percent, with the exception
of California, whose overall OOS rate is 22 percent. State level vehicle
OOS rates (weighted) range from 21 percent (California) to 35 percent
(Maryland).

Also presented in Table 1 are driver and vehicle OOS rates based on
MCSAP Level I inspection data for Fiscal Year 1996. Comparing the Fleet
Survey OOS rate estimates with the FY 1996 MCSAP data, one notes that
the weighted National estimates for both driver and vehicle OOS rates
from the survey (5% and 29%, respectively) are lower than the FY 1996
Level I MCSAP rates (6% and 32%, respectively).

**Table 1**

**Unweighted and VMT Weighted Out-of-Service Rates**

**Based on Level I Inspections for All Trucks by State**

+------+------+-------+------+-------+-------+-------------+--------+
| *    | Ins  | Unwei | VMT  | VMT   | VMT   | MCSAP 1996  | MCSAP  |
| *Sta | pect | ghted |      | Wgt.  | Wgt.  |             | 1996   |
| te** | ions | OOS   | Weig | D     | Ve    | Lev. I      |        |
|      |      | Rate  | hted | river | hicle | Driver      | Lev. I |
|      |      |       |      | OOS   | OOS   |             | V      |
|      |      |       | OOS  | Rate  | Rate  | OOS Rate    | ehicle |
|      |      |       | Rate |       |       |             |        |
|      |      |       |      |       |       |             | OOS    |
|      |      |       |      |       |       |             | Rate   |
+------+------+-------+------+-------+-------+-------------+--------+
|      |      |       |      |       |       |             |        |
+------+------+-------+------+-------+-------+-------------+--------+
| **T  |      | \--   | .32  | .05   | .29   | .06         | .32    |
| otal |      |       |      |       |       |             |        |
| U.   |      |       |      |       |       |             |        |
| S.** |      |       |      |       |       |             |        |
+------+------+-------+------+-------+-------+-------------+--------+
| *    | 10   | .34   | .31  | .05   | .28   | \--         | \--    |
| *All | ,167 |       |      |       |       |             |        |
| 11   |      |       |      |       |       |             |        |
| Stat |      |       |      |       |       |             |        |
| es** |      |       |      |       |       |             |        |
+------+------+-------+------+-------+-------+-------------+--------+
| **   | 1272 | .23   | .22  | .02   | .21   | .03         | .26    |
| CA** |      |       |      |       |       |             |        |
+------+------+-------+------+-------+-------+-------------+--------+
| **   | 438  | .37   | .31  | .07   | .27   | .10         | .51    |
| CT** |      |       |      |       |       |             |        |
+------+------+-------+------+-------+-------+-------------+--------+
| **   | 466  | .32   | .33  | .12   | .26   | .05         | .31    |
| IL** |      |       |      |       |       |             |        |
+------+------+-------+------+-------+-------+-------------+--------+
| **   | 417  | .35   | .35  | .07   | .30   | .05         | .33    |
| KS** |      |       |      |       |       |             |        |
+------+------+-------+------+-------+-------+-------------+--------+
| **   | 944  | .39   | .37  | .04   | .35   | .05         | .36    |
| MD** |      |       |      |       |       |             |        |
+------+------+-------+------+-------+-------+-------------+--------+
| **   | 2    | .37   | .37  | .07   | .34   | .10         | .46    |
| MO** | ,565 |       |      |       |       |             |        |
+------+------+-------+------+-------+-------+-------------+--------+
| **   | 786  | .35   | .36  | .05   | .32   | .06         | .32    |
| NC** |      |       |      |       |       |             |        |
+------+------+-------+------+-------+-------+-------------+--------+
| **   | 370  | .37   | .39  | .10   | .34   | .07         | .41    |
| NM** |      |       |      |       |       |             |        |
+------+------+-------+------+-------+-------+-------------+--------+
| **   | 970  | .36   | .38  | .09   | .34   | .12         | .43    |
| OH** |      |       |      |       |       |             |        |
+------+------+-------+------+-------+-------+-------------+--------+
| **   | 944  | .31   | .29  | .04   | .27   | .05         | .32    |
| WA** |      |       |      |       |       |             |        |
+------+------+-------+------+-------+-------+-------------+--------+
| **   | 995  | .29   | .30  | .02   | .29   | .08         | .37    |
| WI** |      |       |      |       |       |             |        |
+------+------+-------+------+-------+-------+-------------+--------+

These differences between the Fleet Survey estimates and the 1996 MCSAP
rates, although small, are real (i.e., if the survey were to be
replicated, one would expect the Fleet Survey estimates to be lower
again).

That the survey estimates are slightly lower is consistent with the fact
that MCSAP inspections are not always random and frequently target
trucks that appear to have problems. This pattern is generally true for
the individual States as well, although in some States the MCSAP Driver
OOS rates are lower than those from the Fleet Survey (Illinois, Kansas,
and New Mexico). In addition to the fact that MCSAP inspections are
frequently targeted, differences between the MCSAP and Fleet Survey
estimates may also be attributable to the fact that the MCSAP OOS rate
estimates are not weighted.

Table 2 presents similar estimates based on a weighting procedure that
incorporates the truck counts obtained from the inspection sites. The
road-type category OOS rates used to produce the estimates in this table
represent a weighted average of the raw OOS rates associated with each
county sampled in the road-type category, using the sample's average
truck count for the county as the weight (The road-type category OOS
rates used to produce the estimates in Table 1 are raw rates. In both
Tables 1 and 2, the OOS rates for the road-type categories are then
averaged by each category's VMT to obtain overall estimates \-- see
Appendix II).

**Table 2**

**Out-of-Service Rates for All Trucks by State**

**Weighted by State VMT and County Average**

**Truck Counts**

+----------------+------------+--------+--------+--------+-----------+
| **State**      | No.        | We     | We     | We     | Sample    |
|                | I          | ighted | ighted | ighted | Size      |
|                | nspections |        |        |        |           |
|                |            | OOS    | Driver | V      | For       |
|                |            | Rate   |        | ehicle | Weighted  |
|                |            |        | OOS    |        |           |
|                |            |        | Rate   | OOS    | Estimates |
|                |            |        |        | Rate   |           |
+----------------+------------+--------+--------+--------+-----------+
| **Total U.S.** | \--        | .32    | .05    | .29    | 9,959     |
+----------------+------------+--------+--------+--------+-----------+
| **All States** | 9,058      | .31    | .04    | .28    | 9,939     |
+----------------+------------+--------+--------+--------+-----------+
| **CA**         | 1,250      | .20    | .01    | .19    | 1,210     |
+----------------+------------+--------+--------+--------+-----------+
| **CT**         | 425        | .33    | .07    | .28    | 437       |
+----------------+------------+--------+--------+--------+-----------+
| **IL**         | 342        | .29    | .12    | .19    | 444       |
+----------------+------------+--------+--------+--------+-----------+
| **KS**         | 287        | .36    | .06    | .31    | 417       |
+----------------+------------+--------+--------+--------+-----------+
| **MD**         | 929        | .41    | .04    | .38    | 902       |
+----------------+------------+--------+--------+--------+-----------+
| **MO**         | 2,332      | .41    | .06    | .38    | 2,561     |
+----------------+------------+--------+--------+--------+-----------+
| **NC**         | 553        | .36    | .05    | .33    | 760       |
+----------------+------------+--------+--------+--------+-----------+
| **NM**         | 352        | .34    | .05    | .31    | 358       |
+----------------+------------+--------+--------+--------+-----------+
| **OH**         | 828        | .36    | .06    | .34    | 951       |
+----------------+------------+--------+--------+--------+-----------+
| **WA**         | 844        | .33    | .03    | .31    | 935       |
+----------------+------------+--------+--------+--------+-----------+
| **WI**         | 916        | .34    | .01    | .33    | 984       |
+----------------+------------+--------+--------+--------+-----------+

The weighted OOS rate estimates in Table 2 for all States combined, as
well as the National estimates, are almost identical to those found in
Table 1. At the level of individual State estimates, however, small
discrepancies can be found between the two tables. For example, for
Illinois, the weighted vehicle OOS rate is 26 percent in Table 1 and 19
percent in Table 2. This particular discrepancy may be at least
partially attributable to the different sample sizes used in the two
approaches: for the Illinois estimates shown in Table 2, 82 inspections
had to be excluded because they were associated with counties that did
not have the required minimum number of inspections as outlined in
Appendix II. For the most part, however, the differences in the State
level estimates between the two tables are slight.

####  Distribution of Violations for All Trucks 

Table 3, below, presents the most frequently found OOS-related
violations for the "all trucks" sample. Approximately 49 percent of all
OOS-related violations found in the survey pertain to brakes (this
number cannot be derived from the table since the table does not include
all brake-related violations found in the survey). The most common of
these violations involves Federal Violation Code 396.3A1BA (brakes out
of adjustment). Although not shown in the table, only two percent of all
OOS-related violations found in the survey pertain to hours-of-service
rules (10 hour, 15 hour, or 60/70 hour rule violations) and seven
percent of all OOS-related violations pertain to hours of service in
general. (It should be noted, however, that the relatively low frequency
of OOS-related violations pertaining to hours-of-service and log sheets
may be more a reflection of the difficulty in detecting these violations
than a true measure of the extent of their occurrence.)

**Table 3**

**Most Frequently Found Federal Violation Codes**

**Resulting in an OOS Order During Level I Inspections**

**From the "All Trucks" Sample**

+----------+------------------------+---------------+------------------+
| **FED.   | [PROBLEM]{.underline}  | TOTAL OOS     | PERCENT OF       |
| VLTN**   |                        |               |                  |
|          |                        | [VIOLATIONS   | ALL OOS          |
| **[COD   |                        | ]{.underline} |                  |
| E]{.unde |                        |               | [VIOLATI         |
| rline}** |                        |               | ONS]{.underline} |
+----------+------------------------+---------------+------------------+
|          |                        |               |                  |
+----------+------------------------+---------------+------------------+
| **396    | Brakes out of          | 1455          | 26.4 %           |
| .3A1BA** | Adjustment             |               |                  |
+----------+------------------------+---------------+------------------+
| **3      | Inspection,            | 268           | 4.9 %            |
| 96.3A1** | Repair&Maint.          |               |                  |
+----------+------------------------+---------------+------------------+
| **3      | Inoperative Brakes     | 244           | 4.4 %            |
| 93.48A** |                        |               |                  |
+----------+------------------------+---------------+------------------+
| **       | Defective Turn/Hazard  | 236           | 4.0 %            |
| 393.19** | Lamp                   |               |                  |
+----------+------------------------+---------------+------------------+
| **39     | Brakes (General)       | 218           | 4.0 %            |
| 6.3A1B** |                        |               |                  |
+----------+------------------------+---------------+------------------+
| **       | Inadequate Brake       | 204           | 3.7 %            |
| 393.47** | Lining                 |               |                  |
+----------+------------------------+---------------+------------------+
| **3      | Stop Lamp Violation    | 163           | 3.0 %            |
| 93.25F** |                        |               |                  |
+----------+------------------------+---------------+------------------+
| **3      | All Suspension         | 146           | 2.6 %            |
| 93.207** | Violations             |               |                  |
+----------+------------------------+---------------+------------------+
| **396    | Damaged Brake-Hose     | 143           | 2.6 %            |
| .3A1BH** |                        |               |                  |
+----------+------------------------+---------------+------------------+
| **       | Defective Brake        | 123           | 2.2 %            |
| 393.51** | Warning Device         |               |                  |
+----------+------------------------+---------------+------------------+
| **39     | Defective Leaf Spring  | 121           | 2.2 %            |
| 3.207C** | Assembly               |               |                  |
+----------+------------------------+---------------+------------------+
| *        | Inoperable Lamp        | 109           | 2.0 %            |
| *393.9** |                        |               |                  |
+----------+------------------------+---------------+------------------+
| *        | Driver Load Secure     | 93            | 1.7 %            |
| *392.9** |                        |               |                  |
+----------+------------------------+---------------+------------------+
| **3      | Improper Load          | 90            | 1.6 %            |
| 93.100** | Securement             |               |                  |
+----------+------------------------+---------------+------------------+
| **3      | Driver Duty Status not | 88            | 1.6 %            |
| 95.8F1** | Current                |               |                  |
+----------+------------------------+---------------+------------------+
| **396    | Brake Reserve Pressure | 88            | 1.6 %            |
| .3A1BL** | Loss                   |               |                  |
+----------+------------------------+---------------+------------------+

Table 4 shows the distribution for the number of OOS violations given
for trucks placed out-of-service from the survey. The table shows that,
for the 3625 trucks ordered out-of-service, 59 percent had only one OOS
violation, 23 percent had two OOS violations, and nine percent had three
OOS violations. Nine percent of trucks ordered out-of-service had four
or more OOS violations and roughly one percent had ten or more such
violations.

**Table 4**

**Distribution of OOS Violations per Inspection**

**for Trucks Placed Out-of-Service**

+-------------------+-----------+-------------+----------+-----------+
| **Total OOS       | [Freq     | [Percent]{  | Cu       | C         |
| Violations**      | uency]{.u | .underline} | mulative | umulative |
|                   | nderline} |             |          | [Pe       |
| **[per            |           |             | [Freque  | rcent]{.u |
| Inspecti          |           |             | ncy]{.un | nderline} |
| on]{.underline}** |           |             | derline} |           |
+-------------------+-----------+-------------+----------+-----------+
|                   |           |             |          |           |
+-------------------+-----------+-------------+----------+-----------+
| **1**             | 2134      | 58.9        | 2134     | 58.9      |
+-------------------+-----------+-------------+----------+-----------+
| **2**             | 834       | 23.0        | 2968     | 81.9      |
+-------------------+-----------+-------------+----------+-----------+
| **3**             | 331       | 9.1         | 3299     | 91.0      |
+-------------------+-----------+-------------+----------+-----------+
| **4**             | 166       | 4.6         | 3465     | 95.6      |
+-------------------+-----------+-------------+----------+-----------+
| **5**             | 72        | 2.0         | 3537     | 97.6      |
+-------------------+-----------+-------------+----------+-----------+
| **6**             | 31        | 0.9         | 3568     | 98.4      |
+-------------------+-----------+-------------+----------+-----------+
| **7**             | 14        | 0.4         | 3582     | 99.1      |
+-------------------+-----------+-------------+----------+-----------+
| **8**             | 9         | 0.2         | 3591     | 99.1      |
+-------------------+-----------+-------------+----------+-----------+
| **9**             | 12        | 0.3         | 3603     | 99.4      |
+-------------------+-----------+-------------+----------+-----------+
| **10+**           | 22        | 0.7         | 3625     | 100.0     |
+-------------------+-----------+-------------+----------+-----------+

####  HazMat (HM) Data

5.6 percent of all randomly sampled trucks in the survey were found to
be carrying HM. Based on these data, the weighted national estimate of
the percentage of operating trucks carrying HM is 7.2 percent. Table 5
gives unweighted State level estimates and weighted National estimates
of OOS rates for trucks carrying HM. As discussed in Appendix II,
because of the small number of inspections involving HM in several
road-type strata, weighting was only deemed appropriate for National
estimates. The table indicates that 28 percent (unweighted estimate) of
the Level I inspections performed on trucks carrying HM resulted in an
OOS order (i.e., the truck being placed out-of-service). Twenty-six
percent of these inspections resulted in a vehicle-related OOS order and
four percent resulted in a driver-related OOS order.

**Table 5**

**Out-of-Service Rates \*\***

**For Trucks Carrying HM, by State**

**Based on Level I Inspections**

+---------------------+------------------+---------+---------+---------+
| **[S                | [Number of       | Overall | Driver  | Vehicle |
| tate]{.underline}** | Inspecti         |         |         |         |
|                     | ons]{.underline} | [OOS    | [OOS    | [OOS    |
|                     |                  | Rat     | Rat     | Rat     |
|                     |                  | e]{.und | e]{.und | e]{.und |
|                     |                  | erline} | erline} | erline} |
+---------------------+------------------+---------+---------+---------+
|                     |                  |         |         |         |
+---------------------+------------------+---------+---------+---------+
| **Total U.S.        | 1,320            | .27     | .04     | .25     |
| (weighted)**        |                  |         |         |         |
+---------------------+------------------+---------+---------+---------+
| **All 11            | 1,320            | .28     | .04     | .26     |
| S                   |                  |         |         |         |
| tates(unweighted)** |                  |         |         |         |
+---------------------+------------------+---------+---------+---------+
| **CA**              | 36               | .14     | .06     | .08     |
+---------------------+------------------+---------+---------+---------+
| **CT**              | 81               | .37     | .05     | .36     |
+---------------------+------------------+---------+---------+---------+
| **IL**              | 62               | .31     | .03     | .29     |
+---------------------+------------------+---------+---------+---------+
| **KS**              | 83               | .29     | .02     | .28     |
+---------------------+------------------+---------+---------+---------+
| **MD**              | 69               | .32     | .01     | .32     |
+---------------------+------------------+---------+---------+---------+
| **MO**              | 278              | .30     | .05     | .27     |
+---------------------+------------------+---------+---------+---------+
| **NC**              | 117              | .26     | .02     | .24     |
+---------------------+------------------+---------+---------+---------+
| **NM**              | 73               | .29     | .04     | .27     |
+---------------------+------------------+---------+---------+---------+
| **OH**              | 233              | .21     | .03     | .21     |
+---------------------+------------------+---------+---------+---------+
| **WA**              | 118              | .25     | .06     | .21     |
+---------------------+------------------+---------+---------+---------+
| **WI**              | 170              | .31     | .03     | .31     |
+---------------------+------------------+---------+---------+---------+

\*\* unweighted for State estimates, weighted for National estimate.

Comparing these findings to the data from the "all truck" sample (Table
1), one notes that for all States combined, both the raw and the
weighted OOS rates are slightly lower for the "HM" sample. The
unweighted overall OOS rate for these trucks is 28 percent (compared to
34 percent for the "all trucks" sample) and the weighted National
estimate is 27 percent (compared to 32 percent in the "all trucks"
sample). Measuring the statistical significance of the difference
between the estimates is difficult, since the actual variances of the
estimates are not known. Considering the weighted estimates, however
(where there is a smaller difference between the results of the two
samples), and treating the estimates as though derived from a simple
random sample, the difference is statistically significant at the
alpha=.05 level. This would suggest that the differences, although
small, are real (i.e., they actually exist in the population at large)
and are not merely a product of random sampling.

Comparing the raw State level overall OOS rates in Table 5 with the
unweighted State level rates in Table 1, one notes that, with the
exception of Wisconsin, the raw rates for trucks carrying HM are
consistently lower (although only slightly in some cases) than those
obtained from the "all truck" sample. The most dramatic difference
occurs in the case of Ohio, where the raw unadjusted overall OOS rate is
.21 for trucks carrying HM, compared to .36 for the "all trucks" sample.
It should be pointed out, however, that in several States, the sample
sizes are rather low.

As discussed in Appendix II, the weighted National estimates from the
"HM"

sample were obtained by weighting the raw State level OOS rates by State
VMT. Table 5 shows that these weighted nationally representative
estimates differ very little from the raw estimates.

#### 

####  Distribution of Violations

Table 6 shows the most frequently found OOS-related violations for
trucks carrying HM. As can be seen from the table, the distribution is
very similar to what was found with the "all trucks" sample. Again,
Federal Violation Code 396.3A1BA is the most common OOS-related
violation (constituting roughly 25 percent of such violations).
Approximately 51 percent of all OOS-related violations found for trucks
carrying HM pertained to brakes (again, number cannot be calculated from
data shown in table). Only 11 percent of the OOS-related violations in
the HM sample were HM-specific violations (Federal Violation Codes 171
through 180).

**Table 6**

**Most Frequently Found Federal Violation Codes**

**Resulting in an OOS Order During Level I Inspections**

**Found For Trucks Carrying HM**

+-------------+-----------------------+--------------+----------------+
| **FED.      | [PROBLEM]{.underline} | TOTAL OOS    | PERCENT OF     |
| [VL         |                       |              |                |
| TN.CODE]{.u |                       | [VIOLATIONS] | ALL OOS        |
| nderline}** |                       | {.underline} |                |
|             |                       |              | [VIOLATION     |
|             |                       |              | S]{.underline} |
+-------------+-----------------------+--------------+----------------+
|             |                       |              |                |
+-------------+-----------------------+--------------+----------------+
| **          | Brakes out of         | 143          | 24.6%          |
| 396.3A1BA** | Adjustment            |              |                |
+-------------+-----------------------+--------------+----------------+
| *           | Brakes (General)      | 27           | 4.6%           |
| *396.3A1B** |                       |              |                |
+-------------+-----------------------+--------------+----------------+
| **393.19**  | Defective Turn/Hazard | 24           | 4.1%           |
|             | Lamp                  |              |                |
+-------------+-----------------------+--------------+----------------+
| **393.48A** | Inoperative Brakes    | 24           | 4.1%           |
+-------------+-----------------------+--------------+----------------+
| **          | Damaged               | 24           | 4.1%           |
| 396.3A1BH** | Brake-Hose/Tube       |              |                |
+-------------+-----------------------+--------------+----------------+
| **396.3A1** | Inspection,           | 21           | 3.6%           |
|             | Repair,&Maint.        |              |                |
+-------------+-----------------------+--------------+----------------+
| **393.47**  | Inadequate Brake      | 20           | 3.4%           |
|             | Lining                |              |                |
+-------------+-----------------------+--------------+----------------+
| **393.25F** | Stop Lamp Violation   | 19           | 3.3%           |
+-------------+-----------------------+--------------+----------------+
| *           | Defective Leaf Spring | 16           | 2.7%           |
| *393.207C** | Assembly              |              |                |
+-------------+-----------------------+--------------+----------------+
| **393.207** | All Suspension        | 13           | 2.2%           |
|             | Violations            |              |                |
+-------------+-----------------------+--------------+----------------+
| *           | Defective Axle        | 13           | 2.2%           |
| *393.207A** | Positioning Parts     |              |                |
+-------------+-----------------------+--------------+----------------+
| **392.9**   | Driver Load Secure    | 12           | 2.1%           |
+-------------+-----------------------+--------------+----------------+
| **393.45**  | Brake Tubing Aid Hose | 11           | 1.9%           |
|             | Adequacy              |              |                |
+-------------+-----------------------+--------------+----------------+
| **          | Brake Reserve         | 11           | 1.9%           |
| 396.3A1BL** | Pressure Loss         |              |                |
+-------------+-----------------------+--------------+----------------+
| **393.51**  | Defective Brake       | 9            | 1.5%           |
|             | Warning Device        |              |                |
+-------------+-----------------------+--------------+----------------+

####  

####  Differences Between Facility types, Road types, and Time of Inspection

Table 7, below, presents OOS rates and corresponding sample sizes for
both the "all trucks" and the "HM" sample, broken down by facility type
(fixed site or roadside inspection site), road type (urban interstate,
urban non-interstate, rural interstate, rural non-interstate), and time
of inspection (daytime vs. evening). For the OOS rates in the table
broken-out by type of inspection facility and time of inspection, the
rates represent unweighted estimates, since the sample design cannot
accommodate the production of accurate weighted estimates at this level
of detail (the sample sizes become too small in particular road-type
strata). These unweighted estimates are nonetheless useful for
exploratory purposes. Furthermore, the analyses already presented have
shown that the differences between weighted and unweighted estimates are
frequently not substantial (see Table 1). For the estimates broken-out
by road-type, the estimates are weighted.

The table shows few appreciable differences between the raw OOS rates
within most of the breakouts listed. The table does show, however,
slightly higher OOS rates for the rural road-type categories in the case
of the "all trucks" sample (i.e., urban interstate vs. rural interstate,
and urban non-interstate vs. rural non-interstate). Although these
differences appear real (if the estimates were treated as having been
obtained from a simple random sample, the differences would be
statistically significant at the alpha=.05 level), the table does not
show this same pattern with the HM data.

**Table 7**

**Overall OOS Rates by**

**Facility Type, Road Type, and Time of Day**

**for "All Trucks" and "HM" Samples**

  -----------------------------------------------------------------------
  **[All Trucks]{.underline} [(inspections)]{.underline} [HM]{.underline}
  [(inspections)]{.underline}**

  **[Category]{.underline}**

  **[Facility Type:\*]{.underline}**

  **Fixed Site .33 (7322) .28 (942)**

  **Roadside .35 (2845) .26 (399)**

  **[Road Type: \*\*]{.underline}**

  **Urban Interstate .30 (1850) .27 (181)**

  **Urban Non-Interstate .29 (856) .27 (114)**

  **Rural Interstate .33 (4671) .27 (678)**

  **Rural Non-Interstate .34 (2582) .27 (347)**

  **[Time of Day:\*]{.underline}**

  **6am to 5:59pm .34 (9167) .27 (1245)**

  **6pm to 5:59am .31 (1000) .32 (96)**
  -----------------------------------------------------------------------

**\* numbers are unweighted.**

**\*\*numbers are weighted.**

####  Supplemental Questions 

In the National Fleet Survey, the five following supplemental questions
were added to the inspector's data collection form:

\(1\) Who loaded the materials into the vehicle?

\(2\) How much experience have you had driving a commercial vehicle?

\(3\) How much experience have you had transporting hazardous materials?

\(4\) What type of entry level commercial motor vehicle training have
you received?

\(5\) Do you have a copy of the RSPA registration number onboard? (for
HM only)?

Table 8 presents raw unweighted OOS rates broken-out by categories
suggested by these questions (as in the case with Table 7, the level of
detail required by the table is too fine to allow for the production of
reliable weighted estimates). The table shows that slightly less than
half of the trucks inspected in the "all trucks" sample have drivers
with 12 or more years of experience. For drivers of trucks carrying HM,
more than half (58%) of those sampled have at least 12 years of general
experience, and a little less than half (45%) have at least eight years
of experience specific to transporting HM.

For the "all trucks" sample, the table does not show much difference in
overall OOS rates among the various general experience categories (none
of the differences is statistically significant at the alpha=.05 level).
When these same drivers in the "all trucks" sample are classified
according to years of experience transporting HM, however, the
differences become somewhat more pronounced. For example, the overall
raw OOS rate for drivers from the "all truck" sample with zero to three
years of general experience is .34, compared to an OOS rate of .28 for
drivers with 15 or more years of experience transporting HM. This same
pattern is found for both the driver and vehicle OOS rates in addition
to the overall OOS rate. Considering only trucks carrying HM, the table
again shows a decrease in OOS rates as driver experience increases. Note
that these patterns do not necessarily point to a cause and effect
relationship between increased driver experience and lower OOS rates.
For example, it may be that more experienced drivers are assigned newer
vehicles which are more likely to pass an inspection.

**Table 8**

**Raw OOS Rates by**

**Driver Experience, Training and Type of Load**

**for "All Trucks" and "HM" Samples**

**[All Trucks]{.underline} [HM Only]{.underline}**

**Ovrl. Ovrl. \# Drv. Veh. Ovrl Ovrl. \# Drv. Veh.**

**[OOS]{.underline} [Inspcts.]{.underline} [OOS]{.underline}
[OOS]{.underline} [OOS]{.underline} [Inspcts.]{.underline}
[OOS]{.underline} [OOS]{.underline}**

**[Driver Experience:]{.underline}**

**0 to 3 yrs .35 (1963) .08 .31 .35 (177) .06 .33**

**4 to 7 yrs. .35 (1738) .07 .31 .28 (184) .04 .25**

**8 to 11 yrs. .33 (1490) .05 .30 .28 (202) \`.03 .27**

**12 to 15 yrs. .32 (1054) .06 .29 .24 (156) .03 .22**

**15 + yrs. .33 (3900) .05 .29 .26 (616) .03 .25**

**[Driver HM Experience:]{.underline}**

**None .36 (4604) .07 .32 .38 (127) .09 .34**

**Less than 3 yrs. .34 (2258) .06 .31 .31 (332) .04 .29**

**4 to 7 yrs. .33 (1167) .06 .29 .25 (261) .04 .23**

**8 to 11 yrs. .29 (732) .04 .27 .23 (196) .02 .22**

**12 to 15 yrs. .29 (370) .04 .26 .27 (124) .03 .25**

**15 + yrs. .28 (784) .04 .26 .25 (287) .03 .24**

**[Driver Training:]{.underline}**

**Training .34 (9713) .06 .30 .27 (1291) .04 .26**

**No Training .35 (403) .06 .31 .36 (39) .03 .33**

**[Type of Load:]{.underline}**

**Driver Loaded .32 (1872) .06 .28 .25 (456) .02 .24**

**Shipper Loaded .34 (5429) .07 .30 .28 (607) .04 .26**

**Freight For. Loaded .34 (607) .06 .30 .31 (87) .05 .29**

**Other Loaded .31 (635) .04 .29 .27 (84) .01 .27**

**[RSPA Registration]{.underline}**

**Required and Onboard NA NA NA NA .24 (807) .02 .22**

**Req. and Not Onboard NA NA NA NA .31 (228) .04 .30**

For the "all trucks" sample, Table 8 suggests that driver training has
little impact on OOS rates. These results, however, may stem from the
fact that the majority of drivers encountered in the survey had several
years of general experience (the table indicates that 81 percent of
drivers in the "all trucks" sample had four or more years of general
experience). When only those drivers are considered having three years
or less of experience, the raw OOS rate is 51 percent for those without
any training versus 35 percent for those with some form of training (not
shown in table). Although the sample size for the subpopulation of
drivers having both three years or less of experience and no training is
rather small (65 drivers), this difference is statistically significant
at the alpha=.05 level (treating the survey as a simple random sample).
Most of this increase is attributable to driver-related OOS violations:
the subsample of the 65 drivers with three years or less of experience
and no training have a raw driver-related OOS rate of 17 percent (not
shown in table). In the case of trucks carrying HM, the sample size for
trucks driven by individuals with no training is too small to make
meaningful comparisons with trucks driven by trained drivers.

When the raw OOS rates are broken-out by type of load, Table 8 suggests
that, for the "all trucks" sample, the load category has little effect.
When only examining trucks carrying HM, however, one does note small
differences in rates among the load categories. In particular, driver
loaded trucks tend to have slightly lower raw OOS rates (.25), and
freight forwarder loaded trucks slightly higher rates (.31). Reasons for
such a difference, however, cannot be determined from the data.

Carriers are required to be registered with RSPA if they ship or
transport radioactive, explosive, or extremely toxic (by inhalation)
materials; if they ship or transport HM in bulk packages with a capacity
greater than 13,248 liters (for liquids or gases) or 13.24 cubic meters
(for solids); or if they ship or transport HM in non-bulk packages
greater than 2,268 kilograms. Of those inspected vehicles carrying HM
and requiring RSPA HM registration, 78 percent (807 out of 1035) had
registration documentation onboard. As shown in Table 8, both driver and
vehicle OOS rates were lower for the subsample of trucks requiring RSPA
registration and having it onboard, when compared to trucks requiring
such registration and not having it onboard. Because of the small sample
sizes for these subpopulations, however, these differences can only be
shown to be statistically significant in the case of the vehicle OOS
rate.

#  IV. CONCLUSION

Based on the data collected and the estimation procedures used, the
National OOS rate (overall) is estimated to be 32 percent for the
general population of trucks on the highways. This estimate can be
interpreted as indicating that if the general population of trucks on
the highways were subjected to a Level I inspection, 32 percent of them
would have at least one Federal code violation severe enough to place
them out-of-service. The National driver and vehicle OOS rates are
estimated to be five and 29 percent, respectively. When considering only
trucks carrying HM, the overall OOS rate is estimated to be 27 percent.
Driver and vehicle OOS rates for this same population are estimated to
be four and 25 percent, respectively. For both the general population of
trucks, as well as the subpopulation of trucks carrying HM,
approximately half of the OOS-related violations found in the survey
were brake-related.

Differences in OOS rates according to facility type, road type,
time-of-day, driver experience, driver training, and type of load
carried were also examined. With the exception of road-type category,
weighted estimates, however, were not generated, as the overall sample
size of the study cannot support the production of reliable weighted
estimates at these levels of detail.

#  APPENDIX I

**DESCRIPTION OF LEVEL I VEHICLE INSPECTION PROCEDURE**

#   {#section-7 .unnumbered}

#  APPENDIX II

**SAMPLE DESIGN AND ESTIMATION PROCEDURES**

# 

For the 1996 study, a stratified random sampling plan was developed for
collecting data on Level I inspections. Individual States constituted
the primary sampling units. At the second stage of selection, a
stratified sample of locations (inspection sites) was selected within
each State. Finally, at the third stage of selection, individual trucks
were sampled at the inspection sites.

An overall minimum sample size of 5,000 trucks was decided upon. In the
case of a simple random sample, such a sample size would ensure that the
estimated OOS rate from the sample would be within one percentage point
of the true value, at the alpha=.05 level of confidence. The alpha=.05
confidence level indicates that if the study were to be replicated, one
would expect, on average, the estimated out-of-service rates from the
sample replicates to be within one percent of the true value in 95 out
of 100 samples. To ensure that the sample had a sufficient number of
trucks carrying hazardous materials to allow for the production of
separate OOS rate estimates for such vehicles, the random truck
selection procedure was supplemented with an oversampling procedure for
trucks with HM: States were instructed to set aside one day where only
trucks carrying HM would be sampled.

Since the actual sample design used in the study is a stratified sample
rather than a simple random sample, the accuracy of the sample estimates
may be somewhat higher than what is specified above. The sampling design
is discussed in more detail below.

####  Selection of States

All States were first classified into one of four primary sampling
strata based on their past overall Level I out-of-service rates
(25-31.9%, 32-38.9%, 39-45.9%, and 46% and above), using 1994 MCMIS
Level I interstate inspection data (these strata are subsequently
referred to in this document as out-of-service strata). The States were
then notified about the study and were asked about their interest in
participating.

A total of 17 States initially expressed an interest in participating in
the study. Given manpower and other constraints, OMC decided that 12
States could be accommodated by the study. These States were then
purposely (nonrandomly) selected in such a manner that all of the four
strata, defined above, were represented in the sample, and that
participating States were geographically dispersed. One State, Georgia,
later withdrew from the study because of manpower shortages resulting
from holding the Olympic Games. Table 1A, below, lists the remaining 11
States selected in the sample, the out-of-service strata to which they
belong, and the OOS rates in these States from the 1994 Level I
interstate MCMIS inspection data.

**Table 1A**

**1994 Interstate Inspections Level I OOS Rates for**

**States Selected into Sample,**

**By OOS Stratum**

  -----------------------------------------------------------------------
  **94 MCMIS 94 MCMIS 94 MCMIS MCMIS**

  **Selected Overall Lev. I Driver Lev. I Vehicle Lev. I Sample**

  **Stratum State OOS Rate OOS Rate OOS Rate Size**

  **25%-31.9% CA .26 .04 .24 63,691**

  **25%-31.9% IL .28 .05 .25 10,395**

  **25%-31.9% NC .32 .04 .29 10,871**

  **32%-38.9% KS .36 .05 .33 8,510**

  **32%-38.9% WA .36 .04 .34 22,528**

  **32%-38.9% MD .38 .04 .36 16,710**

  **38%-45.9% WI .40 .10 .34 10,067**

  **39%-45.9% NM .44 .04 .43 4,858**

  **46% + OH .46 .13 .40 15,149**

  **46% + MO .56 .09 .52 32,565**

  **46% + CT .57 .07 .55 4,976**
  -----------------------------------------------------------------------

rates have been rounded to two decimal places.

\*from MCMIS.

The 5000 sample units (trucking inspections) were allocated to the 11
States following loosely the rule of optimal allocation. This rule from
sampling theory suggests that the overall sample be allocated to the
sampling strata in a manner proportional to the size and variability
found in each sampling stratum. That is, sampling strata having more
population units or more variability among the units for the
characteristic in question receive proportionally more sample units. In
the case of this study, however, both the population variability for
trucks being out-of-service as well as the number of trucks operating
(i.e., the population units) in each stratum were unknown. Hence, the
population variability was assumed equal in all States and strata, and
the number of trucks operating in each State was assumed correlated to
truck Vehicle Miles of Travel. Optimal allocation was then loosely
applied by simply allocating more truck inspections to the States having
more VMT. (It should be noted that, strictly speaking, the States are
primary sampling units and not sampling strata. The end result is the
same, however. The sample units are allocated more heavily to States
with more population units.) Further adjustments to the sample
allocation had to be made based on the manpower capabilities of
individual States.

####  Selection of Sites and Trucks

In the second stage of selection, fixed and non-fixed inspection sites
were selected within each State from each of the following road-type
categories: (1) Rural Interstate roads, (2) Rural Non-interstate roads,
(3) Urban Interstate roads, and (4) Urban Non-interstate roads. The
selection of these sites was left to each State and was not random but
purposive, although states were instructed to select as many different
kinds of sites as possible, including at least one non-fixed site per
road-type category. The States were given only the minimum number of
sites to be selected within each road-type stratum; the maximum number
of sites to be selected in each of these strata was not specified. As in
the case of allocating the overall sample to the States described above,
more truck inspections were allocated to the road-type strata having
more truck VMT.

The number of inspections requested of each State as well as the actual
number of inspections performed is given in Table 2A by road-type
category. One notes from Table 2A that, although the total number of
inspections actually performed in each State usually far exceeds the
State's requested minimum, the requested minimums for the road-type

**Table 2A**

**Number of Inspections Required and Achieved**

**by State by Road Type for the "All Trucks" Sample**

  -----------------------------------------------------------------------
  **Rural Urban**

  **Interstate Other Interstate Other**

  **Req. (Ach.) Req. (Ach.) Req. (Ach.) Req. (Ach.)**

  **[State]{.underline}**

  CA 180 (**175**) 180 (**77**) 100 (731) 100 (227)

  CT 170 (180) 140 (174) 56 (**34**) 40 (49)

  IL 150 (209) 180 (164) 85 (**17**) 85 (**54**)

  KS 110 (112) 150 (221) 40 (**31**) 50 (53)

  MD 140 (441) 140 (**134**) 80 (225) 90 (102)

  MO 210(2005) 150 (512) 140 (**20**) 150 (**24**)

  NM 140 (197) 110 (**62**) 70 (98) 70 (**1**)

  NC 110 (216) 150 (201) 110 (192) 100 (151)

  OH 140 (512) 140 (267) 100 (106) 100 (**66**)

  WA 140 (219) 180 (435) 70 (171) 80 (110)

  WI 100 (405) 120 (335) 50 (225) 70 (**19**)
  -----------------------------------------------------------------------

**Table 3A**

**1994 Truck VMT (millions) by State by Road Type**

**(source:FWHA)**

  -----------------------------------------------------------------------
  **Rural Urban**

  **Interstate Other Interstate Other**

  **[State]{.underline}**

  **CA 2,309 3,664 2,443 11,502**

  **CT 164 173 484 385**

  **IL 1,957 1,002 2,747 1,610**

  **KS 473 786 183 317**

  **MD 565 621 764 963**

  **MO 1,621 1,850 878 1,193**

  **NM 213 656 160 268**

  **NC 1,370 4,184 808 3,212**

  **OH 1,852 2,704 2,152 1,839**

  **WA 517 1,095 704 1,616**

  **WI 426 1,945 338 1,135**
  -----------------------------------------------------------------------

strata were frequently not met. Notwithstanding these difficulties, the
overall sample size achieved turned out to be quite large, exceeding
10,000 inspections and including over 1,300 inspections of trucks
carrying HM. Table 3A gives 1994 truck VMT totals for each of the
road-type categories.

To ensure that the minimum number of HM-related inspections was
achieved, the States were requested to set aside one day during the data
collection period for collecting only HM-related data. This suggested
procedure was followed in nine States. In some States, the final sample
of HM-specific inspections was too sparse in particular road-type strata
to allow weighting by road type in the production of HM-specific
estimates. As a result only national estimates were weighted for HM
specific data (see below).

At each inspection site, trucks were randomly selected for Level I
inspections. Upon completing an inspection, the inspector would select
another truck by choosing the third truck passing through the site. The
volume of trucks passing each site was measured by taking a five minute
truck count prior to the beginning of the inspectors' work shift. Both
weighted and unweighted estimates of OOS rates were produced in this
study. Estimates pertaining to the general population of trucks on the
road were generated using all sample inspection data, with the exception
of data collected from "HM only" sampling days. Separate estimates for
trucks carrying HM were generated from data from "HM only" sampling days
as well as from HM-related data contained in the general sample. Because
the number of HM-related inspections was extremely low in several
road-type strata for some States, separate weighting procedures had to
be developed for estimates of HM-specific OOS rates.

For the weighted estimates, several approaches were considered for
weighting the data, involving the use of standard statistical techniques
with some modifications. These modifications became necessary since many
of the population parameters required by standard estimation formulas
were unknown and could not be estimated. This section begins by
examining the feasibility and appropriateness of applying various
statistical estimation techniques to the data in this study. This is
followed by a discussion of the estimation procedures actually
implemented.

####  Theory

Based on standard statistical estimation procedures, the general formula
for estimating a percentage from a single stage stratified sample is
given by,

**(1)** **P =**$$ $\sum_{}^{}{ph \ast Nh}$ **/** $\sum_{}^{}{Nh}$ ,

where **p~h~** is the percentage of sample units in the sampling stratum
having the characteristic of interest, and **N~h~** is the number of
units existing in the stratum population. In this study the **p~h~**
values represent the percentage of trucks estimated to be out-of-service
in stratum **h**. The challenge in using this approach for this study
stems primarily from the fact that the values for **N~h~** (the total
number of trucks operating on the highways in sampling stratum **h)**
are unknown. Hence, to use this formula in this study, proxies must be
substituted, which are correlated with these **N~h~** values. In this
study, truck VMT is used as the proxy.

It should be pointed out, however, that Equation (1) applies to a
single-stage sample design, whereas the National Fleet Safety Survey is
actually a multi-stage design, involving the selection of States, and
within States, the selection of inspection sites and trucks.
Traditionally, in the case of a multi-stage design, the percentage of
units in the population having a particular characteristic is obtained
by using a ratio estimator. Using the ratio estimator, the estimated
out-of-service rate would take the form,

**(2)** **P =**$$
$\sum_{}^{}{\text{Wgt}1 \ast \text{Wgt}2 \ast \text{.}\text{.}\text{.}\text{Wgt}n \ast Xi}$
**/**
$\sum_{}^{}{\text{Wgt}1 \ast \text{Wgt}2 \ast \text{.}\text{.}\text{.}\text{Wgt}n \ast Yi}$
**,**

where, **Wgt~1~** through **Wgt~n~** represent the sampling weight
components corresponding to each stage of sample selection, **X~i~**
represents the total number of sampled trucks found in an out-of-service
condition at a sample site, and **Y~i~** represents the total number of
sampled trucks at the site. Each sampling weight component, **Wgt**,
corresponds to the inverse of the probability of selection for the unit
at that particular sampling stage.

For this study, however, the ratio estimator as given by Equation (2)
becomes problematic because of the difficulty in developing appropriate
sampling weights for some of the sampling stages, particularly the stage
associated with the selection of inspection sites. These sites were
selected purposively and do not have selection probabilities associated
with them from which sampling weight components could be derived. Had
the sample been limited to fixed inspection sites, the sites could have
been quite easily selected by means of a probability sample. In the case
of the non-fixed sites (corresponding to roadside inspections), however,
such a procedure becomes considerably more complex: all road segments in
each sampling stratum would have to have been defined, enumerated, and
assigned a selection probability prior to sampling. Since these
procedures were not performed, Equation (1), with some modifications,
becomes a more practical option for estimation.

####  Estimation Methodology

Representative weighted sample estimates of OOS rates for all trucks
were obtained from Equation (1) by using the formula iteratively,
beginning with the strata associated with the final sampling stage
(road-type strata), and then working backward. Thus all the sample data
in each State were initially collapsed to the road-type stratum level
and the percentage of inspections resulting in out-of-service violations
(**p~h\ ~**in Equation 1) was calculated for each road-type stratum in
the State. Then, substituting the total 1994 truck VMT for the total
number of trucks operating in each road-type stratum (**N~h~**), a
weighted estimate of the OOS rate was obtained for each out-of-service
level stratum (from which the States were selected) by the formula,

**(3)** **P~oos~ =**$$$\sum_{s}^{}{}$ $\sum_{h}^{}{ph \ast \text{VMT}h}$
**/** $\sum_{s}^{}{}$$\sum_{h}^{}{\text{VMT}h}$ ,

where **P~oos~** is the estimated out-of-service rate for a particular
out-of-service level stratum (one of four), and **p~h~** and
**VMT~h~**VMTh are, respectively, the raw out-of-service rate and total
truck VMT for road-type stratum **h** in State **s**. The inner
summation in the formula is over all four road-type strata within a
given State **s** belonging to the out-of-service stratum, and the outer
summation is over all States belonging to the out-of-service stratum.

The road-type stratum's out-of-service rate, **p~h\ ~**, in Equation (3)
was calculated directly from the raw sample data in each road-type
stratum. In addition, these percentages were alternatively calculated
using a weighted average based on average county truck counts obtained
from the sample. With the alternative approach, **p~h~** was calculated
by the formula,

**(3a)** **p~h~ =**$$ $\sum_{c}^{}{Xc}$ **/** $\sum_{c}^{}{Yc}$ ,

where **Y~c~** is the average truck volume (averaged over all inspection
sites) for a given county, based on the five minute sample truck counts;
**X~c~** is this same average truck volume multiplied by the raw OOS
rate for the county obtained from the sample; and the summations in the
numerator and denominator are over all counties contained within
road-type stratum **h**. **X~c~** represents the sample estimate of the
number of trucks contained in the truck count, **Y~c~** , that are in an
out-of-service condition. Originally, the intention had been to
calculate the parameters **X** and **Y** at each inspection site rather
than at the county level. However, due to lack of consistency in the
labeling of site identifiers in the survey data base, it was not
possible to identify uniquely all inspection sites (for example, a
location along Route 50 in Illinois might have been labeled as RT50, or
I50, or R50, etc.). Hence, the micro data were aggregated to the county
level, and individual counties found in the sample were treated as
inspection locations. Even after the data were aggregated to the county
level, it was found that some counties still had too small a number of
inspections to produce a reasonable estimate of **X~c~**, above. Thus,
to ensure that **X~c~** was based on a reasonable sample size, counties
with fewer than ten total inspections were excluded from the
calculations (this resulted in the exclusion of 250 counties comprising
a total of 553 inspections). For counties in Illinois and Wisconsin, the
required minimum number of inspections was relaxed from ten to six. This
became necessary when it was found that keeping the required minimum at
ten in these States resulted in several road-type strata not being
represented in the sample.

Once OOS rate estimates were obtained for each out-of-service Level
stratum from Equation (3), a weighted estimate of the National OOS rate
was obtained by, again, applying Equation (1) to the out-of-service
strata. Thus,

**(4)** **P~ntnl~ =**$$
$\sum_{\text{oos}}^{}{p\text{oos} \ast \text{VMT}\text{oos}}$ /
$\sum_{\text{oos}}^{}{\text{VMT}\text{oos}}$ ,

where **p~oos~** is the estimated out-of-service rate for a given
out-of-service rate stratum (obtained from Equation 3) and **VMT~oos~**
is the total truck VMT in the same stratum.

####  State Level Estimates

Weighted representative estimates of out-of-service rates for individual
States were obtained using a similar approach as shown by Equation (5),
below.

**(5)** **P~s~ =**$$ $\sum_{h}^{}{ph \ast \text{VMT}h}$ **/**
$\sum_{h}^{}{\text{VMT}h}$ .

This formula is the same as Equation (3), except that the summation here
is only over the four road-type strata in one particular State (hence,
the outer summation sign in Equation 3 is missing).

####  HazMat-Specific OOS Rates

As mentioned above, the number of HM-related inspections was often very
low for particular road-type strata in several States. If the weighting
procedures outlined above had been implemented to estimate an OOS rate
specific to trucks carrying HM, some values of **p~h\ ~**(the percentage
of trucks in the road-type stratum found to be in an out-of-service
condition), used in Equation (3) would have been based on a sample size
too small to ensure any reasonable accuracy. Hence, for State level
estimates, HM-specific OOS rates were generated from the raw survey data
without applying any weighting procedures.

For a National estimate of a HM-specific OOS rate, however, the
unweighted OOS rates for each State were weighted by VMT using the
following procedure. First, a weighted estimate of the OOS rate in each
OOS level stratum was obtained using the formula,

**(3b)** **P~oos~ =**$$ $\sum_{s}^{}{ps \ast \text{VMT}s}$ **/**
$\sum_{s}^{}{\text{VMT}s}$ ,

where **p~s~** is the raw OOS rate for State **s**. The values of
**P~oos~** obtained from Equation (3b) were then plugged into Equation
(4) to obtain a National level estimate.

[^1]: "Report of Controlled Sampling Survey from Roadside Inspections on
    Units of Property Motor Carriers," Bureau of Motor Carrier Safety,
    FHWA, December 1971.