NEW JERSEY MOTOR VEHICLE INSPECTION PROGRAM 

PIF EFFECTIVENESS STUDY 

Final Report 

Prepared for: 

New Jersey Department of Environmental Protection 

Bureau of Motor Vehicle Inspection & Maintenance 

401 East State Street 

Trenton, NJ 08625 

Prepared by: 

MACTEC Engineering and Consulting, Inc. 

21740 Beaumeade Circle, Suite 150 

Ashburn, VA 20147 

703.729.1416 

June 23, 2010 
NJ Motor Vehicle Inspection Program PIF Effectiveness Study June 23, 2010 

MACTEC Engineering and Consulting, Inc., Contract No: A61906 Final Report   

TABLE OF CONTENTS 

1.0 INTRODUCTION ............................................................................................................................... 1 

2.0 OTHER STATES’ PRACTICES .......................................................................................................... 3 

3.0 ANALYTICAL APPROACH ............................................................................................................... 4 

4.0 ANALYTICAL RESULTS .................................................................................................................. 7 

4.1 Failure Rates for OBD Tests ....................................................................................................... 7 

4.2 Proposed Discount Factors ......................................................................................................... 8 

4.3 Clean Scanning / Clean Piping Analysis .................................................................................. 10 

5.0 RECOMMENDATIONS .................................................................................................................. 13 

5.1 Proposed Discount Factors ....................................................................................................... 13 

5.2 Overall Network Effectiveness ................................................................................................. 13 

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NJ Motor Vehicle Inspection Program PIF Effectiveness Study June 23, 2010 

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1.0 INTRODUCTION 

In its original regulations on implementing an enhanced motor vehicle inspection program, the 

USEPA considered inspections performed at a decentralized test-and-repair garage to be only 

50% as effective as those performed at a centralized test-only facility. Under pressure from 

states, the USEPA allowed inspection programs to claim higher degrees of decentralized testing 

effectiveness in their modeling as long as the claim was backed by sound reasoning. In New 

Jersey’s hybrid vehicle inspection network, Centralized Inspection Facilities (CIFs) are test-only facilities 

run by the Parsons Group as a State contractor. Private Inspection Facilities (PIFs) are comprised of 

privately-owned test and repair facilities licensed by the State. The New Jersey Department of 

Environmental Protection (the Department or DEP) proposed an effectiveness of 80% for its 

decentralized PIFs and has been using this factor in modeling for many years. 

When enhanced inspection was implemented in New Jersey, all vehicles received a tailpipe 

exhaust emissions test as their primary test. A tailpipe test may be subject to considerable fraud 

because it is easy to probe a known clean vehicle (“clean piping”). While the Motor Vehicle 

Commission (MVC) aggressively pursues and prosecutes fraudulent inspection, a Sierra 

Research study in 2005, concluded that a significant fraction of tailpipe tests were either outright 

fraudulent or suspicious. 

With a large percentage of the motor vehicle fleet (model year 1996 and newer) now subject to 

On Board Diagnostics (OBD) testing as their primary emissions test, the landscape has changed. 

Newer OBD vehicles (2005+) have VINs embedded in the OBD data and even older OBD 

vehicles have a distinct “fingerprint” of OBD data that narrows probable year/make/model/ 

engine family. As such, fraudulently substituting a known good vehicle for the vehicle under 

test (“clean scanning”) has become no less difficult, but far more easily detected. The MVC and 

DEP now have access to triggers and alerts whenever suspected fraudulent OBD testing occurs, 

virtually in real time. 

The Department considers 80% PIF effectiveness to likely be very conservative in light of the 

program and technology changes described above. In May 2010, the Department therefore 

authorized MACTEC to assess improvements in effectiveness of the decentralized program. The 

purpose of this study is to determine a reasonable effectiveness fraction that may be supported by 

data and good technical reasoning. 

The data currently available for this study includes about ten years of enhanced inspection 

program data, plus limited roadside testing data. We evaluated the utility and relevance of the 

roadside data because it is not based on random selection of vehicles subject to inspection. It 

was determined that the use of this data could not improve the statistical confidence of the larger 

dataset. 

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MACTEC Engineering and Consulting, Inc., Contract No: A61906 Final Report   

MACTEC analyzed the effectiveness of the decentralized PIF network relative to the CIF 

(centralized) network. The relative effectiveness of PIFs is based on data collected from PIFs and 

CIFs in 2009. 

The final results of our evaluation are being submitted by June 30, 2010, to coincide with 

program planning efforts that may be affected by the study outcome. 

 

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2.0 OTHER STATES’ PRACTICES

Since the decentralized portion of the New Jersey inspection program is not unlike decentralized 

testing in other States, MACTEC was directed to investigate relevant programs to determine if 

effectiveness rates in current use are similar enough to indicate a standard for industry practice. 

MACTEC was able to determine that emission inspection programs in Massachusetts, 

Connecticut, New York, Georgia and Utah claim in their SIPs that their decentralized OBD 

inspections are as effective as centralized programs. None of these States use the decentralized 

program effectiveness prompt of the MOBILE6.2 model when modeling the emission benefits 

for their OBD inspections. 

Although many states claim that their decentralized tailpipe inspections are 100% effective, 

based on the supporting data included with this report, it seems more reasonable to keep New 

Jersey’s PIF effectiveness factor for tailpipe tests at 80%. 
NJ Motor Vehicle Inspection Program PIF Effectiveness Study June 23, 2010 

MACTEC Engineering and Consulting, Inc., Contract No: A61906 Final Report   

3.0 ANALYTICAL APPROACH

The following is a description of the analysis employed to re-calculate the PIF effectiveness for 

MOBILE6 modeling. It is broken down into the following categories: 

• Previous program (CIF and PIF) data analysis 

• Current program (CIF and PIF) data analysis 

• MOBILE6.2 analysis 

This analysis defines the effectiveness of the decentralized (PIF) network relative to the 

centralized (CIF) network. CIFs are test-only facilities run by the Parsons Group as a State 

contractor. PIFs are comprised of privately-owned test and repair facilities licensed by the State. 

Previous Program Data Analysis Protocol 

An indicator of PIF vs. CIF effectiveness is the incidence of clean scanning or clean piping. 

Clean piping and clean scanning refer to the practice of substituting a passing vehicle for the 

vehicle being tested. Clean piping occurs when an inspector probes the tailpipe of a passing 

vehicle instead of the vehicle being tested. Clean scanning occurs when an inspector substitutes a 

fault-free vehicle for the vehicle that is being inspected. Previous program data from CIFs and 

PIFs was analyzed to determine rates of suspected fraud in PIFs and CIFs. From this, the 

effectiveness of PIF inspections is calculated relative to CIF inspections. This analysis focused 

on data collected during the last two years of the old program. The following describes the 

possible indicators of fraud that we calculated. 

Suspected Clean Scanning 

Two parameters are calculated to determine if stations may have clean scanned a vehicle: 

1. Mismatch between entered VIN and OBD VIN 

2. Questionable retests – mismatch between initial test monitors supported and retest 

monitors supported 

Mismatch between entered VIN and OBD VIN -- If the vehicle has an electronic VIN 

available through the vehicle’s OBD II system, clean scanning cases can be identified by 

comparing the entered VIN with the VIN provided by vehicle’s OBD II system. In order to 

perform this analysis, the data was manipulated to remove cases where mismatches between the 

OBDII VIN and the registered VIN may be in error. These manipulations are presented below: 

• Records where the OBDII VIN and the registered VIN had less than 17 characters were 

removed. 

• Records where the last digit of the OBDII VIN and the registered VIN was alpha (not 

numeric) were removed. 

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NJ Motor Vehicle Inspection Program PIF Effectiveness Study June 23, 2010 

MACTEC Engineering and Consulting, Inc., Contract No: A61906 Final Report   

• Mismatches are based on the last 4 digits of the OBD VIN and the registered VIN. 

The percent of tests with VIN mismatches equals the following: 

(# of valid mismatches between OBD VIN/entered VIN)/(# of records with valid OBD VINs) 

Questionable Retests – Mismatch Between Initial Test Monitors Supported and Retest 

Monitors Supported -- The readiness monitors supported by the vehicle’s OBDII system 

provide a rough signature for the vehicle. The following analysis was conducted on OBDII 

inspection data to identify cases where readiness monitors supported on an initial failing test do 

not match readiness monitors supported on passing retests: 

1. Using data from the last two years, test records for the initial failing test were identified 

(termed Fail Initial OBD) 

2. Records for vehicles that pass retests were identified (termed Pass OBD Retest). 

3. Data records for the Fail Initial OBD and Pass OBD Retest groups were linked via VIN. 

4. Records where the monitor profile changes were identified. Did not count cases where 

readiness status changes from ready to not ready or vice versa. 

The percent of suspect retests equals the following: 

(# of monitor mismatches)/(# OBD retests) 

Suspected Clean Piping 

Two parameters were calculated to determine if a station may have clean piped a vehicle: 

1. Overall tailpipe failure rate 

2. Questionable retests – less than 1⁄2 hour between initial failing test and passing retest. 

Failure rate analysis – Initial test failure rates for the tailpipe test were calculated by model year 

and network (CIF vs. PIF). Then, using the statewide distribution of model years for the tailpipe 

test, normalized failure rates for tailpipe tests were calculated for CIFs and PIFs. Rates for CIFs 

were compared to rates for PIFs. The relative failure rate is calculated as follows: 

(Normalized PIF failure rate)/(Normalized CIF failure rate) 

Questionable Retests – Less than 1⁄2 Hour between Initial Failing Tailpipe Test and Passing 

Retest

A short time (less than 1⁄2 hour) between an initial failing test and a passing retest could indicate 

that the vehicle was clean-piped on the retest. The following procedure was used to identify 

vehicles that passed a retest less than 1⁄2 hour after they failed: 

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NJ Motor Vehicle Inspection Program PIF Effectiveness Study June 23, 2010 

MACTEC Engineering and Consulting, Inc., Contract No: A61906 Final Report   

1. Using data from the last two years, test records for the initial failing test were identified 

(termed Fail Initial Tailpipe) 

2. Records for vehicles that pass retests were identified (termed Pass Tailpipe Retest). 

3. Data records for the Fail Initial Tailpipe and Pass Tailpipe Retest groups were linked via 

VIN. 

4. Time between passing retests and failed initial test was calculated. 

5. Records where passing retests occurred less than 1⁄2 hour after the failing test were 

identified. 

The percent of suspect retests equals the following: 

(# of passing retests performed less than 1⁄2 hour after failing test)/(# of tailpipe retests) 

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NJ Motor Vehicle Inspection Program PIF Effectiveness Study June 23, 2010 

MACTEC Engineering and Consulting, Inc., Contract No: A61906 Final Report   

4.0 ANALYTICAL RESULTS 

This section provides comparative information on failure rates for OBD and tailpipe tests and 

then addresses the results from the clean piping and clean scanning analysis.

4.1 Failure Rates for OBD Tests 

Table 1 and Figure 1 compare OBD failure rates for CIFs with those for PIFs. The normalized 

fail rate for the PIFs is 98.5% of the fail rate for CIFs. As shown on Figure 1, PIFs have a higher 

normalized fail rate for readiness, while CIFs have a higher normalized fail rate for MILCommanded On. This makes sense, considering that PIFs are much more likely than CIFs to 

attempt to correct a MIL situation before inspecting the vehicle. This practice would lower MILon rates while raising not ready rates. 

Table 1 – OBD Fail Rate: CIFs vs. PIFs

Model Year Weighting 

Fail Rate 

PIF CIF 

1996 3% 14% 21% 

1997 8% 14% 16% 

1998 5% 14% 16% 

1999 9% 11% 12% 

2000 8% 11% 11% 

2001 11% 14% 13% 

2002 9% 10% 9% 

2003 14% 7% 6% 

2004 8% 6% 4% 

2005 12% 4% 3% 

2006 8% 3% 2% 

2007 2% 2% 1% 

2008 1% 0% 1% 

2009 1% 0% 1% 

Normalized Fail Rate 8.8% 8.9% 

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NJ Motor Vehicle Inspection Program PIF Effectiveness Study June 23, 2010 

MACTEC Engineering and Consulting, Inc., Contract No: A61906 Final Report   

Figure 1- Readiness and MIL-On Fail Rate: CIFs vs. PIFs 

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

MIL-On Readiness Overall

Normalized OBDII Fail Rates: CIFs vs. PIFs

CIF

PIF

4.2 Failure Rates for Tailpipe Tests 

Table 2 compares tailpipe failure rates for CIFs with those for PIFs. The normalized fail rate for the PIFs 

is 55% of the fail rate for CIFs. The lower normalized fail rate in PIFs can be partially explained by the 

common practice of repairing vehicles before their initial inspections. 

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NJ Motor Vehicle Inspection Program PIF Effectiveness Study June 23, 2010 

MACTEC Engineering and Consulting, Inc., Contract No: A61906 Final Report   

Table 2 – Tailpipe Fail Rate: CIFs vs. PIFs 

Model 

Year Weighting 

Tailpipe Failure Rate 

PIFs CIFs 

Pre-81 1.85% 12.0% 1.9% 

1981 0.19% 13.2% 0.2% 

1982 0.25% 14.2% 0.3% 

1983 0.37% 12.4% 0.4% 

1984 0.75% 12.5% 0.8% 

1985 1.10% 10.9% 1.1% 

1986 1.91% 12.0% 1.9% 

1987 2.52% 10.4% 2.5% 

1988 3.44% 9.1% 3.4% 

1989 3.99% 9.0% 4.0% 

1990 5.38% 9.3% 5.4% 

1991 5.72% 9.6% 5.7% 

1992 8.41% 10.4% 8.4% 

1993 10.74% 8.6% 10.7% 

1994 15.24% 8.0% 15.2% 

1995 18.62% 6.2% 18.6% 

1996 0.73% 5.1% 0.7% 

1997 1.10% 4.1% 1.1% 

1998 1.15% 3.1% 1.2% 

1999 1.27% 3.2% 1.3% 

2000 1.57% 1.9% 1.6% 

2001 1.66% 0.9% 1.7% 

2002 1.63% 1.0% 1.6% 

2003 1.93% 0.6% 1.9% 

2004 2.68% 0.2% 2.7% 

2005 3.05% 0.2% 3.0% 

2006 1.32% 0.3% 1.3% 

2007 0.69% 0.4% 0.7% 

2008 0.53% 0.3% 0.5% 

2009 0.15% 0.5% 0.2% 

2010 0.02% 0.0% 0.0% 

Normalized Fail Rate 7.2% 13.1% 

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NJ Motor Vehicle Inspection Program PIF Effectiveness Study June 23, 2010 

MACTEC Engineering and Consulting, Inc., Contract No: A61906 Final Report   

4.3 Clean Scanning / Clean Piping Analysis 

An indicator of PIF vs. CIF effectiveness is the incidence of clean scanning or clean piping. 

Clean piping and clean scanning refer to the practice of substituting a passing vehicle for the 

vehicle being tested. Clean piping occurs when an inspector probes the tailpipe of a passing 

vehicle instead of the vehicle being tested. Clean scanning occurs when an inspector substitutes a 

fault-free vehicle for the vehicle that is being inspected. Previous program data from CIFs and PIFs 

were analyzed to determine rates of suspected fraud in PIFs and CIFs. From this, the effectiveness of PIF 

inspections was calculated relative to CIF inspections. This analysis focused on data collected during the 

last year of the old program. 

Suspected Clean Scanning in OBD Inspections 

Two parameters were calculated to determine if stations may have clean scanned a vehicle: 

1. Mismatch between entered VIN and OBD VIN 

2. Questionable retests – mismatch between initial test monitors supported and retest monitors 

supported 

Mismatch between entered VIN and OBD VIN -- If the vehicle has an electronic VIN available through 

the vehicle’s OBD system, clean scanning cases can be identified by comparing entered VIN with the 

VIN provided by vehicle’s OBD system. In order to perform this analysis, the data were manipulated to 

remove cases where mismatches between the OBD VIN and registered VIN may be in error. These 

manipulations are presented below: 

• Records where the OBD VIN and registered VIN had less than 17 characters were removed. 

• Records where the last digits of the OBD VIN and registered VIN were alpha (not numeric) were 

removed. 

• Mismatches were then manually reviewed to identify additional cases where the OBD VIN was 

invalid. 

Table 3 presents the number and percent of OBD VIN mismatches by network type. PIFs had a higher 

mismatch rate than CIFs, but the overall rate was low for both networks. 

Table 3 – OBD VIN Mismatches: CIFs vs. PIFs 

Network # of Valid OBD VINs # of Valid Mismatches % with Valid Mismatch % Matching 

CIF 318,488 559 0.18% 99.82% 

PIF 27,278 207 0.76% 99.24% 

The correct inspection rate at PIFs is 99.4% of the correct inspection rate at CIFs, based on OBD VIN. 

Questionable Retests – Mismatch Between Initial Test Monitors Supported and Retest Monitors 

Supported -- The readiness monitors supported by the vehicle’s OBD system provide a rough signature 

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MACTEC Engineering and Consulting, Inc., Contract No: A61906 Final Report   

for the vehicle. The following analysis was conducted on OBD inspection data to identify cases where 

readiness monitors supported on an initial failing test do not match readiness monitors supported on 

passing retests: 

1. Using data from the last year, test records for the initial failing test were identified (termed Fail 

Initial OBD) 

2. Records for vehicles that pass retests were identified (termed Pass OBD Retest). 

3. Data records for the Fail Initial OBD and Pass OBD Retest groups were linked via VIN. 

4. Records where the monitor profile changes were identified. 

Table 4 presents the number and percent of monitor mismatches during retests by network type. Like 

OBD VIN mismatches, PIFs had a higher retest monitor mismatch rate than CIFs, but the overall rate was 

low for both networks. 

Table 4 – Retest Monitor Mismatches: CIFs vs. PIFs 

Network # Re-Inspections 

# with 

Monitor 

Mismatches 

% with 

Monitor 

Mismatches 

% without 

Monitor 

Mismatches 

CIF 24,995 68 0.27% 99.73% 

PIF 19,460 103 0.53% 99.47% 

The correct re-inspection rate at PIFs is 99.7% of the correct re-inspection rate at CIFs, based on the 

percentage of retests with readiness monitor mismatches. 

Triggers Implemented with New Program 

The trigger incidence statistics reported above are based on data from 2009 where the State investigated 

mismatches in the following: 

• OBD VIN 

• PID Count/PCM Module ID 

Using the expanded OBD data collected by new equipment in the PIFs, the State has implemented the 

following triggers, in addition to OBD VIN and PID Count/PCM Module ID mismatches: 

• Communication Protocol mismatches 

• Monitors supported mismatches 

• Review of expanded PCM Module ID 

• Review of CAL ID and CVN 

• Extended data review: 

o Time since diagnostic trouble codes were cleared. 

o Distance traveled with MIL on. 

The new triggers will further reduce the already low rates of fraudulent tests. 

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MACTEC Engineering and Consulting, Inc., Contract No: A61906 Final Report   

Suspected Clean Piping in Tailpipe Inspections 

Due to time constraints for this task we were only able to analyze one parameter to determine if a station 

may have clean piped a vehicle: Questionable Retests – Less than 1⁄2 Hour between Initial Failing 

Tailpipe Test and Passing Retest 

The following procedure was used to identify vehicles that passed a retest less than 1⁄2 hour after they 

failed the initial test: 

1. Using data from the last two years, test records for the initial failing test were identified (termed 

Fail Initial Tailpipe) 

2. Records for vehicles that passed retests were identified (termed Pass Tailpipe Retest). 

3. Data records for the Fail Initial Tailpipe and Pass Tailpipe Retest groups were linked via VIN. 

4. The time between passing retests and failed initial test was calculated. 

5. Records where passing retests occurred less than 1⁄2 hour after the failing test were identified. 

A short time (less than 1⁄2 hour) between an initial failing test and a passing retest could indicate that the 

vehicle was clean-piped on the retest. However, in many cases tailpipe failures can be repaired in less 

than 1⁄2 hour. For example, a simple tune-up to correct high hydrocarbon emissions can conceivably be 

performed on some vehicles in less than 1⁄2 hour. Therefore, the percentage of tests identified by this 

trigger represents the upper limit of fraudulent tests. 

Table 5 presents the number and percent of tailpipe retests that occurred less than 1⁄2 hour after the initial 

failing test. Even if there were no fraudulent retests, the percentage of passing retests within 1⁄2 hour of the 

initial failing test would be expected to be much lower in CIFs than in PIFs. As shown in Table 5, a 

higher percentage of passing retests occur within 1⁄2 hour of the initial failing test in PIFs than in CIFs. 

Overall, based on this trigger, the non-suspect re-inspection rate at PIFs is 78% of the rate at CIFs. 

Table 5 – Passing Reinspections That Occur Within 1⁄2 Hour of Initial Failing Tests: CIFs vs. PIFs 

Network # Re-Inspections 

# Within 1/2 

Hour 

Suspect ReInspection Rate Non-Suspect ReInspection Rate 

CIF 19,791 1,212 6% 94% 

PIF 53,578 14,360 27% 73% 

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NJ Motor Vehicle Inspection Program PIF Effectiveness Study June 23, 2010 

MACTEC Engineering and Consulting, Inc., Contract No: A61906 Final Report   

5.0 RECOMMENDATIONS 

The recommendations presented in this section relate first to the PIF effectiveness factors for 

OBD and tailpipe inspections and then for the overall network. 

5.1 Proposed Discount Factors 

Recommendations on PIF effectiveness for OBD inspections and for tailpipe inspections are 

provided below.

PIF Effectiveness for OBD Inspections 

MACTEC recommends that the State use a 100% effectiveness factor for OBD tests at PIFs, 

instead of the 80% factor that is currently used. Our justification for increasing the effectiveness 

of OBD inspections at PIFs is: 

• Fail rates for OBD inspections in PIFs are nearly identical to those in CIFs. 

• Based on an analysis of triggers for OBD tests performed in 2009, over 99% of 

inspections in PIFs have no indications of fraud.

• New Jersey has implemented several additional OBD triggers in the new program, which 

will further reduce the incidence of fraud.

PIF Effectiveness for Tailpipe Inspections 

MACTEC recommends that the State continue to use an 80% effectiveness factor for tailpipe 

emissions tests at PIFs. The data analyzed in this task do not justify a higher or lower 

effectiveness factor. The fail rate in PIFs is 55% of the fail rate in CIFs, which can be partially 

explained by the common practice of repairing vehicles in PIFs before their initial inspections. 

The rate of non-suspect tests in PIFs is 78% of the rate in CIFs, based on the percentage of 

passing retests that occur within 1⁄2 hour of the initial failing tests. Considering that the 

percentage of tests identified by this trigger represents the upper limit of fraudulent tests, the 

current PIF effectiveness factor of 80% is a conservative estimate. Further study would likely 

result in a higher PIF effectiveness factor. 

5.2 Overall Network Effectiveness 

Table 6 and Figure 2 show estimates of the overall network effectiveness based on the following 

data and assumptions: 

• PIF effectiveness is as follows: 

o OBD tests: 100% 

o Tailpipe tests: 80% 

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• 30% of the vehicles are tested in PIFs. Based on historical data, the percent tested in PIFs 

is more like 20%, so this assumption results in a conservative, i.e., low, effectiveness 

factor for the overall network. 

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Table 6 – Estimates of the I/M Program Effectiveness of the Network 

Year 

PIF Testing 

OBD % 

PIF Testing 

TP % 

PIF I/M 

Effectiveness 

CIF I/M 

Effectiveness 

MOBILE6.2 

I/M 

Effectiveness 

2005 54.0% 46.0% 90.8% 100% 97.2% 

2006 53.0% 47.0% 90.6% 100% 97.2% 

2007 57.5% 42.5% 91.5% 100% 97.4% 

2008 64.5% 35.5% 92.9% 100% 97.9% 

2009 67.8% 32.2% 93.6% 100% 98.1% 

2010* 71.7% 28.3% 94.3% 100% 98.3% 

2011* 75.6% 24.4% 95.1% 100% 98.5% 

2012* 79.5% 20.5% 95.9% 100% 98.8% 

Figure 2 – Projected I/M Program Effectiveness 

86%

88%

90%

92%

94%

96%

98%

100%

2005 2006 2007 2008 2009 2010* 2011* 2012*

I/M Effectiveness by Year

PIF I/M Effectiveness

Overall I/M Effectiveness