National ResearchCouncil Canada

Conseil nationalde recherches Canada

Centre for Surface
Transportation Technology

Centre de technologie destransports de surface

Test Report
Effects of Pavement Structure on Vehicle
Fuel Consumption – Phase III
G.W. Taylor, P. Eng.
J.D. Patten, P. Eng.
Centre for Surface Transportation Technology (CSTT)
National Research Council of Canada (NRC)
2320 Lester Road
Ottawa, Ontario K1V 1S2
CanadaPhone (613) 998-9639
Fax(613) 957-0831
Prepared for:Cement Association of Canada; andNatural Resources Canada Action Plan 2000 on Climate Change

January 27, 2006
Project 54-HV775
Technical Report CSTT-HVC-TR-068

CONTROLLEDUNCLASSIFIEDThis document contains confidential information that is proprietary to NRC’s Centre for Surface
Transportation Technology. No part of its contents may be used, copied, disclosed or conveyed to any party
in any manner whatsoever without prior written permission from NRC’s Centre for Surface Transportation
Technology.

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CSTT-HVC-TR-068

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CSTT-HVC-TR-068

CONTROLLEDUNCLASSIFIEDCONTROLÉNON CLASIFIÉE

Effects of Pavement Structure on Vehicle Fuel Consumption – Phase III

G.W. Taylor, P. Eng.
J.D. Patten, P. Eng.

Centre for Surface
Transportation Technology

Centre de technologie
des transports de surface

Technical Report

Rapport technique

CSTT-HVC-TR-068

CSTT-HVC-TR-068

January 27, 2006
J. Coleman
General manager/
Gestionnaire principal

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CSTT-HVC-TR-068

This report reflects the views of its authors, and not necessarily those of the Cement
Association of Canada or Natural Resources Canada.

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CSTT-HVC-TR-068

ABSTRACT
A highway tractor with a van semi-trailer and a passenger car were driven over concrete,asphalt and composite paved roads to detect if fuel savings could be attributed to any of thethree pavement surfaces. The tests were conducted in winter, spring, summer cool, summer hot
and fall weather conditions and at two road speeds: 60 km/h and 100 km/h. Additionally, thetrailer was loaded to three different weights to establish if loading was a contributing factor tofuel consumption differences among pavement types. All testing was performed on open
highways in Ontario and Quebec. The acquired data were then analysed using multiple
regression which formed the basis for a set of predictive mathematical models. A number ofconclusions regarding the relationship between pavement type and fuel efficiency were drawn
from these models.

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EXECUTIVE SUMMARY
The Pavement Fuel Efficiency Study, Phase III was contracted by the Cement Association of
Canada (CAC) and Natural Resources Canada (NRCan) under the Government of Canada
Action Plan 2000 on Climate Change (Minerals and Metals Programme), in the fall of 2002, to
the National Research Council’s (NRC) Centre for Surface Transportation Technology (CSTT).
CSTT provided an independent third-party evaluation to quantify the potential fuel consumption
differences when vehicles are driven over three distinct types of pavements: asphalt, concreteand composite (asphalt top-coat over concrete).
CSTT developed comprehensive performance tests that were conducted between fall 2002 andfall 2003 to quantify these potential fuel consumption differences for a highway tractor pulling aloaded van semi trailer. Additionally, limited data were also collected for a passenger car. The
passenger car was tested in one loading condition, winter and summer weather conditions over
all three pavement types. The highway tractor and tridem van semi-trailer were tested in threeloading conditions, at five distinct seasonal conditions (winter, summer day [hot], summer night[cool], fall and spring) and over all three pavement types.
The results of the study pertain to the test routes that were selected for this programme and notnecessarily all grades of concrete, asphalt and composite pavements. Additionally, there was
no attempt to quantify the effects of periodic surface irregularities such as potholes, lowfriction/high friction transient areas, tining and changes in elastomeric properties. The study
was focussed on fuel burn characteristics and did not consider such pavement properties aslight reflection, sound reflection, resistance to hydroplaning, maintainability or ease/cost of
construction.
In addition to the dynamic fuel consumption data, a series of static surveys were conducted tocharacterize the road surfaces. An IRI survey was used to gather information on International
Roughness Index, an index that measures irregularities on the surface of the road (lower valuesof IRI equate to smoother roads). A precision GPS survey was used to gather information onroad curvature and grade. Finally, a Falling Weight Deflectometer survey was conducted toquantify the strength of the road bed at selected locations. The data from these surveys weremerged with the fuel consumption data to form ‘Meta’ files. These ‘Meta’ files were then used togenerate models for all the various conditions, allowing statistical multiple regression formulae
to be generated.
CSTT’s conclusions, stemming from the tractor and van semi-trailer fuel consumption testingand subsequent statistical models, are summarized below. Unless noted otherwise, all valuesof absolute fuel consumption differences are mean values and all percentage differences aremean percentage differences:•

At 100 km/h, on smooth roads, fuel consumption reductions were realised on allconcrete roads when compared to asphalt. The savings ranged from 0.4 L/100 km to
0.7 L/100 km (0.8% to 1.8%) when compared to asphalt roads. These savings wererealised for both empty and fully loaded vehicle conditions for four of the five seasons.
All these differences were found to be statistically significant at the 95% level. The
savings during the fifth season, Summer Night, were 0.25 L/100 km (0.4%), however,these data were found to be not statistically significant.

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CSTT-HVC-TR-068•

When comparing concrete roads to composite roads at 100 km/h, the results showedthat fuel consumption savings ranged from 0.2 L/100 km to 1.5 L/100 km (0.8% to 3.1%)
in favour of concrete. However, under Summer day conditions, less fuel was consumedon the composite roads, as compared to concrete. The value of these savings wasroughly 0.5 L/100 km (1.5%). All composite to concrete comparisons were found to bestatistically significant except the Spring data, which was not statistically significant.

•

The fuel savings for the empty trailer at 60 km/h when comparing concrete to asphalt
roads ranged from 0.4 L/100km to 0.5 L/100km (1.7% to 3.9%) in favour of concrete andwere all statistically significant in four of the five seasons. The fuel savings for theSummer Night data were 0.1 L/100 km (0.5%) but they were not statistically significant.

•

The fuel savings for the full trailer at 60 km/h when comparing concrete to asphalt roads
ranged from 0.2L/100km to 0.4 L/100km (1.3% to 3.0%) in favour of concrete and were
all statistically significant in four of the five seasons. The fuel savings for the SummerNight data were 0.1 L/100 km (0.5%) but they were not statistically significant.

•

The fuel savings for the empty trailer at 60 km/h in four of the five seasons whencomparing concrete to composite roads ranged from 1.1 L/100km to 1.9 L/100km (2.0%
to 6.0%), in favour of concrete. However, the summer day data indicated a savings infavour of composite, when compared to concrete, of 0.2 L/100 km (3.0%). All of thesesavings were statistically significant.

•

The fuel savings for the full trailer at 60 km/h in four of the five seasons when comparing
concrete to composite roads ranged from 0.6 L/100km to 1.4 L/100km (1.9% to 4.1%) in
favour of concrete. However, the summer day data indicated a savings in favour ofcomposite, when compared to concrete, of 0.2 L/100 km (2.4%). All of these savings
were statistically significant except the Spring data.

Coastdown tests were conducted on the fully loaded tractor and van semi-trailer combination toisolate the differences in rolling resistance between the three pavement surfaces. The results ofthe coastdown testing did not indicate any significant differences between any of the threesurfaces with respect to rolling resistance, from 30 km/h to 10 km/h.
CSTT’s primary conclusions stemming from the passenger car testing and subsequentstatistical models are summarized below:•

Due to the limited number of data points and seasonal conditions, the results from thepassenger car testing were less conclusive than the tractor and trailer testing.

•

Of the four seasonal car models presented below, three were statistically significant and
one was not (asphalt versus concrete in summer).

•In winter testing, the passenger car consumed 0.3 L/100 km more (2.9%) on asphalt
than on concrete. These savings were all statistically significant.

•In winter testing, the car consumed 0.2 L/100 km less fuel (2.3%) on composite
pavement when compared to concrete. These savings were all statistically significant.

CSTT-HVC-TR-068•

In summer testing, the passenger car consumed 0.1 L/100 km (1.5%) more fuel oncomposite roads when compared to concrete. These savings were all statisticallysignificant.

•In summer testing, the passenger car consumed 0.05 L/100 km (0.3%) less fuel onasphalt roads when compared to concrete. However, these savings were notstatistically significant.

ix

CSTT performed a comparison between this Phase and the previous Phase II rework project.
Since each project generated a data set and a model it stood to reason that each of the data
could be plugged into each of the models. The results of this cross-comparison are listed below:•

Different mathematical models were developed for the Phase II and Phase III studies.
The data from both studies (Phase II and Phase III) were analyzed and compared using
both models for the data collected at 25 deg C. For the Phase II data (tanker semitrailer), these analyses showed statistically significant fuel savings when operating on
concrete pavement compared to asphalt pavement of 1.9 L/100 km, ranging from 4.3%
to 9.2%, depending on model used, IRI range, vehicle speed and weight. It is important
to note that these higher percentage differences between the two data sets were likely
affected by the different types of road surfaces and not the models.

•

When similarly comparing concrete pavement and composite pavement, the savings
ranged from 0.8 L/100 km to 1.2 L/100 km (1.9% to 5.8%) in favour of concrete onsmooth roads and were statistically significant.

•

The comparison using the two models for the Phase III (van semi-trailer) data at 25 deg
C showed statistically significant fuel savings when operating on concrete pavementcompared to asphalt pavement ranging from 0.5 L /100 km to 0.8 L/100 km (1.1% to5.2%), depending on model used, IRI range, vehicle speed and weight.

•

The comparison using the two models for Phase III data (van semi-trailer) showed thatthe fuel consumption differences between composite and concrete pavements onrougher roads were not statistically different. However, the fuel consumption savings forconcrete pavements, when compared to composite, on smoother roads ranged between
0.3 L/100 km and 0.7 L/100 km (0.6% and 4.8%) and were all statistically significant.

•

The predicted fuel savings on concrete, when compared to asphalt and composite, arevery similar when Phase III data (van semi-trailer) is inserted into each of the models.
Similarly, the predicted fuel savings on concrete, when compared to asphalt andcomposite, are very similar when Phase II data (tanker semi-trailer) is inserted into eachof the models. However, the predicted fuel savings when comparing Phase II data to
Phase III data are not similar. CSTT therefore concludes that the differences between
Phase II and Phase III results stem primarily from the collected data themselves (i.e. theprevailing road conditions) and not the mathematical models.

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CSTT-HVC-TR-068

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Report prepared by

Reviewed and approved by_______
Gordon Taylor, P. Eng.
Consulting Engineer

Jeff Patten, P. Eng.
Manager, Test and Evaluation Engineering
Road Vehicles and Military Systems
NRC/CSTT

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TABLE OF CONTENTS
1
1.1
1.2
1.3
1.4
1.5
2
2.1
2.2
2.3
2.4
2.5
3
3.1
3.2
4
4.1
4.2
4.3
4.4
4.5
4.6
5
5.1
5.2
5.3
5.4
6
6.1
6.2
6.3
7
7.1
7.2
7.3
8
8.1
8.2
8.3
8.4
9

INTRODUCTION
References
Task Objective
Previous Work
Overview for Phase III
Limitations
EQUIPMENT, PROCEDURES & TEST PARAMETERS
Equipment
Vehicle tests performed by NRC/CSTT
Heavy Haul Tractor with Semi Trailer
Passenger Vehicle
Tests and Surveys Performed by Third Party Contractors
TEST SITES
Test Site Descriptions
Operational Routes
PHASE III TRACTOR TRAILER RESULTS
Error Analysis and Test Repeatability
Test Data Processing
Pavement Flexibility and Strength Tests
Statistical Analysis
Analysis of Point Estimates
Phase III Truck Model Findings
COAST DOWN TESTSCoastdown Procedure
Test Sites
Test Results
Coastdown Findings
PASSENGER CAR TESTSTest Method
Data Analysis
Car Data Point Estimates
COMPARISON BETWEEN PHASES II AND III

1
1
1
1
2
2
3
3
3
3
9
11
12
12
17
18
18
19
20
20
23
41
43
43
44
47
50
51
51
51
52
56

Purpose56
Possibility of poor data collection in either of both of the phases
56
Comparative Analysis of the Tanker semi-trailer (Phase II) and Van Semi-trailer (Phase III) Data56
SUMMARY OF FINDINGS
2003 Van Semi-trailer Tests
Coastdown Tests
Passenger Vehicle Tests
Phase II to Phase III Model Comparison

69
69
70
70
70

CONCLUSIONS

73

10 FOLLOW ON WORK

75

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11 REFERENCES

CSTT-HVC-TR-068
76

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LIST OF TABLES
Table 2-1 Summary of Test Sites and Initial Test Conditions .......................................................8
Table 3-1 Test Site Lengths and Characteristics, Sorted by Test Site........................................14
Table 4-1 Truck Winter Model....................................................................................................21
Table 4-2 Truck Spring Model.....................................................................................................22
Table 4-3 Truck Summer Day Model ..........................................................................................22
Table 4-4 Truck Summer Night Model ........................................................................................22
Table 4-5 Truck Fall Model .........................................................................................................23
Table 4-6 Truck All-season Model ..............................................................................................23
Table 4-7 Full, Winter Point Estimates, 100km/h,IRI =1.0,pavement temps= –20,–15, & –10o C.............................................................................................................................................26
Table 4-8 Full, Spring Point Estimates (100km/h,IRI =1.0,pavement temps = 10,15,& 20o C) ...26
Table 4-9 Full, Summer Day Point Estimates, 100km/h, IRI =1.0, pavement temps = 25, 30 &35o C ....................................................................................................................................27
Table 4-10 Full, Summer Night Point Estimates, 100km/h, IRI =1.0, pavement temps = 20, 25 &30o C ....................................................................................................................................27
Table 4-11 Full, Fall Point Estimates, 100km/h, IRI =1.0, pavement temps = -5, 0 & +5o C .......28
Table 4-12 Full, All data Model Point Estimates (Speed=100, IRI =1.0).....................................28
Table 4-13 Empty,Winter Point Estimates,100km/h,IRI =1.0,pavement temp= -20,-15,& -10o C29
Table 4-14 Empty, Spring Point Estimates,100km/h,IRI =1.0,pavement temps =10,15, & 20o C29
Table 4-15 Empty, Summer Day Point Estimates, 100km/h, IRI =1.0, pavement temps = 25, 30,& 35o C .................................................................................................................................30
Table 4-16 Empty, Summer Night Point Estimates, 100km/h, IRI =1.0, pavement temps = 20,
25, & 30o C ...........................................................................................................................30
Table 4-17 Empty, Fall Point Estimates, 100km/h, IRI =1.0, pavement temps = -5, 0 & +5o C...31
Table 4-18 Empty, All data Model Point Estimates (Speed=100, IRI =1.0) ................................31
Table 4-19 Empty, All Data Model Point Estimates (IRI = 1.0, Speed = 60 km/h) ......................32
Table 5-1 Coastdown Test Conditions........................................................................................48
Table 5-2 Regression Equation Summary ..................................................................................49
Table 6-1 Car Winter Model Coefficients ....................................................................................52
Table 6-2 Summer Car Model Coefficients.................................................................................52
Table 6-3 Car Winter Point Estimates, 100 km/h........................................................................53
Table 6-4 Car Summer Point Estimates, 100 km/h.....................................................................53
Table 7-1 Phase II Model with Phase III data, IRI Less Than 1.2...............................................58
Table 7-2 Phase II Model with Phase III data, IRI between 1.2 And 1.6.....................................59
Table 7-3 Phase III Model with Phase III data, IRI Less Than 1.2..............................................59
Table 7-4 Phase III Model with Phase III data, IRI between 1.2 And 1.6....................................60
Table 7-5 Phase II Model with Phase II data, IRI Less Than 1.2................................................60
Table 7-6 Phase II Model with Phase II data, IRI Between 1.2 And 1.6 .....................................61
Table 7-7 Phase III Model with Phase II data, IRI Less Than 1.2...............................................61
Table 7-8 Phase III Model with Phase II data, IRI between 1.2 And 1.6.....................................62
Table 7-9 Comparison of Phase III data (van) in Phase III and Phase II Models, at 25 deg C...65
Table 7-10 Comparison of Phase II Data (tanker) in Phase II and Phase III Models, at 25 deg C.............................................................................................................................................66

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LIST OF FIGURES
Figure 2-1 Side View of Tractor Trailer .........................................................................................5
Figure 2-2 Trailer Payload giving 49,400 kg GVW........................................................................5
Figure 2-3 Front Three Quarter View of Pontiac Grand Prix.......................................................10
Figure 4-1 Repeatability of Instantaneous Fuel Consumption Over a Test Course...................19
Figure 4-2 Truck Model Estimates – Concrete ...........................................................................33
Figure 4-3 Truck Model Estimates, Full Load At 100 km/h (43,660 kg) ......................................33
Figure 4-4 Asphalt-Concrete Percentage Change Estimates at 100 km/h .................................34
Figure 4-5 Composite-Concrete Percentage Change Estimates at 100 km/h ............................35
Figure 4-6 Asphalt 95% Confidence Bounds - Empty, 100 km/h ................................................35
Figure 4-7 Asphalt 95% Confidence Bounds – Full Load at 100 km/h .......................................36
Figure 4-8 Composite, 95% Confidence Bounds - Empty, 100 km/h..........................................36
Figure 4-9 Composite, 95% Confidence Bounds, Full, 100 km/h ...............................................37
Figure 4-10 Asphalt Percent Change At 60 km/h (All loads) ......................................................37
Figure 4-11 Composite Percent Change At 60 km/h (All loads) .................................................38
Figure 4-12 Asphalt Percent Difference- 95% Confidence Bounds, empty, 60 km/h..................38
Figure 4-13 Asphalt, Percent Difference- 95% Confidence Bounds, full, 60 km/h......................39
Figure 4-14 Comp. Percent Difference- 95% Confidence Bounds, empty, 60 km/h ...................39
Figure 4-15 Comp. Percent Difference- 95% Confidence Bounds, full, 60 km/h ........................40
Figure 5-1 Site 1 West Coastdown Test Area.............................................................................44
Figure 5-2 Site 1 East Test Areas...............................................................................................45
Figure 5-3 Site 11 East Coastdown Test Sites ...........................................................................45
Figure 5-4 Site 2 East Coastdown Test Areas............................................................................46
Figure 5-5 Site 5 West Coastdown Test Areas...........................................................................46
Figure 5-6 Site 5 East Coastdown Test Areas............................................................................47
Figure 5-7 Coastdown Speed vs. Time at Site 11 East - Asphalt ...............................................48
Figure 5-8 Coastdown Average Decceleration Rates .................................................................49
Figure 6-1 Car Point Estimates (-10 to 30 C)..............................................................................54
Figure 6-2 Car Percentage Change Estimates vs. Concrete, 100 km/h .....................................54
Figure 7-1 Van Model Comparisons at 100 km/h and IRI=1.0....................................................67
Figure 7-2 Van Model Comparisons at 100 km/h and IRI=1.5....................................................67
Figure 7-3 Tanker Model Comparions at 100 km/h and IRI=1.0.................................................68
Figure 7-4 Tanker Model Comparisons at 100 km/h and IRI=1.5 ...............................................68

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ACKNOWLEDGEMENTS
The authors wish to thank the following CSTT personnel who contributed to the test
programme:•

Erik Oxelgren set up the data acquisition system and acquired fuel burn data andmonitored the testing for every tractor trailer test loop. The vast majority of the tests
were conducted between 6 PM and 4 AM. Mr. Oxelgren’s dedication to the project isgreatly appreciated;•

Rebecca Mann performed data acquisition on several of the passenger car runs; and•Cristian Tabra, E.I.T assisted with passenger car testing.

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CSTT-HVC-TR-068

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CSTT-HVC-TR-068

1 INTRODUCTION
1.1

References
A. Statement of Work between Cement Association of Canada and NRC/CSTT, dated
8/2002.
B. Interdepartmental agreement between NRC/CSTT and Action Plan 2000 on Climate
Change, Minerals and Metals (NRCan), signed 3/21/2003, amended 6/17/2003 and4/4/2004.

1.2

Task Objective

The Centre for Surface Transportation Technology (CSTT) at NRC was tasked jointly by The
Cement Association of Canada (CAC) and Natural Resources Canada (NRCan) Action Plan
2000 on Climate Change (Minerals and Metals Programme) to perform a third phase of testing
to investigate the effects of pavement structure on fuel consumption rate.

1.3

Previous Work

Prior to the commencement of this Phase III testing, the National Research Council and the
Cement Association of Canada had engaged in two previous pavement fuel efficiency studies.
Each of the previous phases identified areas of concern and possible improvement for futurestudies. Phase I was a single season test designed as an initial body of work to determine if
fuel efficiency differences between concrete and asphalt pavements could be detected, using
engine management software. Phase II was a multi-season test involving a variety of different
truck and trailer combinations and a relatively wide spectrum of road roughness and was a morein depth study than Phase I. After Phase II was delivered it was determined that a morethorough statistical model would allow for better interpretation of the results. Therefore, aPhase II “rework” project was initiated to build a more robust statistical model using the Phase II
data (tanker semi-trailer only).
The lessons learned in Phases I and II were reviewed, allowing a more focussed andrepresentative study to be conducted, called Phase III. This technical report deals primarily withthe execution, analysis and results of Phase III testing. However, where appropriate, previous
phases have been presented, discussed and compared to Phase III work. The following table
outlines the time-lines of each of the phases:
Phase
I
II
II Rework
III

Data Collection
Summer 1998
April 1999 to January 2000
April 1999 to January 2000
Sept 2002 to Sept 2003

Report Acceptance
November 1998
August 2000
July 2002
December 2005

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1.4

CSTT-HVC-TR-068
Overview for Phase III

The third phase of testing was complementary to the previous two phases [1], [2] of testing andemployed many of the recommendations found in the Phase II rework final report (July 2002).
Whereas previous studies used tractor and trailer combinations that were not as common to
Canadian roads, this study utilized the most popular tractor and trailer combination currently
found on Canadian roads: a tandem drive tractor pulling a van semi trailer. Section 7 outlines
the principal differences regarding methodology and conclusions between Phases II and III.
Although the main objective of this Phase III study was to quantify the fuel efficiency of a heavy
haul tractor-trailer combination, a passenger car component was added to the test programme,
to broaden the spectrum of analysis. The results from truck and passenger car testing arepresented separately, in subsequent sections of this report.
1.5

Limitations

The roads used to quantify the fuel consumption differences were selected by the appropriate
provincial authorities and, at the time of testing, were in good condition and represented current
construction techniques and as such the study was not intended as a comparison between all
grades of concrete or asphalt. Additionally, the resulting mathematical models were notdesigned to account for any localized transient effects such as variations in surface wear,
concrete tining, surface friction or cross sectional pavement irregularities such as potholes, ruts
or bumps. Variations in chemical or physical properties (e.g. elastomeric) of the pavements
were also not considered.

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3

2 EQUIPMENT, PROCEDURES & TEST PARAMETERS
2.1

Equipment

The following equipment was used:
i.
ii.
iii.
iv.
v.
vi.
vii.
viii.
ix.
x.
xi.
xii.
xiii.
xiv.
xv.
xvi.
2.2

“International” tandem drive axle, tractor unit.
Wabash, 53 foot, tridem axle, van semi trailer.
2002 Pontiac Grand Prix, VIN 1G2WK52J72F224037
Data acquisition computers and peripherals
Hand held temperature probe
AutoTAP hardware and software
Payload in the form of lead boxes and concrete filled drums c/w tie downs
Wind speed anemometer
Single roller dynamometer
Laptop computers
Power inverter
Barometer
Autologger
Cummins “Insite” Version 5.4 software and adapter
Flashing lights
Rotating beacon

Vehicle tests performed by NRC/CSTTThe fuel efficiency data were collected using two separate vehicles:••

Heavy haul tractor with semi trailer; andPassenger car.

The test apparatus and methods for each configuration are presented separately below.
2.3

Heavy Haul Tractor with Semi Trailer

2.3.1 Apparatus
Previous NRC pavement fuel efficiency studies used truck and trailer combinations thatwere less common to Canadian roads. Although data collected in previous studies
were relevant to the road surfaces found in Canada, they did not properly represent the
majority of tractor/trailer combinations found on Canadian roads. In order to alleviate
this problem, the most commonly used combination was selected for this phase of
testing: a single axle- steer with tandem-drive-tractor pulling a 53-foot dry goods vansemi trailer.
Although the tandem-axle van semi trailer remains the most popular axle arrangement in
Ontario and Quebec, a 12 foot spread tridem (three axle) trailer arrangement was

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CSTT-HVC-TR-068
selected for two reasons: tridem grouping provides 12 tires in contact with the road, incomparison with eight tires for a tandem, thus providing more statistical sensitivity torolling resistance for a study of this nature; and the tridem grouping is becoming
increasingly more popular as operators attempt to ship heavier loads using the samelength of trailer.
A manufactured, and variable, load was created from lead boxes weighing from 500 to5000 lbs each, as well as barrels filled with concrete, each weighing 1000 lbs. This
allowed the test team to load the trailer to any gross weight stipulated by the test
parameters. The concrete barrels were tightly secured to the van trailer using cargo
straps and were prevented from moving in any direction. A railway system was screwed
to the floor of the trailer to prevent the lead boxes from moving laterally or longitudinally,
however, the boxes were able to move vertically, with the motion of the trailer, by
roughly one inch (2.5 cm).
With the exception of the manufactured load of lead and concrete, both the tractor andtrailer were tested in a completely ‘stock’ condition and represented a typical
Ontario/Quebec configuration. None of the tires were replaced during the tests and nomajor tractor power-train components were replaced other than fluids and filters. The
tires used for testing (manufactured by General Tire) were neither ‘low rolling resistance’or ‘high rolling resistance’ and represented a typical set of tires used in Canada. The test
truck, trailer and load can be seen at Figures 2-1 and 2-2.

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Figure 2-1 Side View of Tractor Trailer

Figure 2-2 Trailer Payload giving 49,400 kg GVW

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CSTT-HVC-TR-068
Due to logistical reasons, beyond the control of CSTT/NRC, the tractor was not
equipped with a roof mounted air deflector. Although reducing the effects of
aerodynamic drag would have been ideal, the net effect on the mathematical models canbe considered minimal since the identical tractor and trailer were used throughout allphases of testing. When considering how a tractor’s power (leaving the drive-shaft) is
consumed while cruising on the highway, three factors should be considered:
aerodynamic drag, rolling resistance and electrical/HVAC accessories. Horizontal trailer
aerodynamic drag can typically be modelled as a quadratic curve (y = ax2 + b) whereas
rolling resistance is a linear form such as y = mx + b. The contribution of electrical andclimate control accessories are normally modelled as a constant. The contributions ofvertical aerodynamic effects have been ignored.
Since this study focussed on fuel burn as it relates to pavement type it would have been
ideal to isolate the contribution from rolling resistance and remove all other sources (i.e.
remove the possibility that high speed aero drag may reduce the sensitivity of the rolling
resistance data). However, when measuring total actual fuel burn over a specified
distance it is impossible to distinguish the fuel burn’s various sources. An analysis ofthat nature could only be developed using theoretical estimates or computer simulations.
The following example (using industry accepted figures [3], [4], [5]) demonstrates how
the model could have been affected by the lack of air deflector at higher speeds:
Without an air deflector, at 100 km/h, the power delivered by a typical tractor isconsumed as follows (excluding internal losses in the engine itself):
82 kW
60 kW
18 kW
160 kW

Aerodynamic Drag
Rolling resistanceElectrical Accessories/AC
Total

51%
38%
11%
100%

Under similar conditions, the power drain on a tractor fitted with an air deflector would
be:
70 kW
60 kW
18 kW
148 kW

Aerodynamic Drag
Rolling resistanceElectrical Accessories/AC
Total

47%
41%
12%
100%

Therefore, the sensitivity of analysing the high speed fuel efficiency data, based on
rolling resistance figures without an air deflector, would likely be less than 4% (i.e. 4%
applied to the difference in fuel economies between the pavement types, which itself isalready a small number). The low speed data would not be affected since aerodynamic
drag is insignificant at city speeds.

7

CSTT-HVC-TR-068

2.3.2 Test Conditions
The tractor semi-trailer was tested in five separate temperature ranges as follows:
SeasonWinterSpringSummer Hot
Summer Cool
Fall

Ambient Temperature Range< -10 oC
> - 5 oC and < +10 oC> +29 oC
> +10 oC and < +25 oC> - 5 oC and < +10 oC

Note: Although the ambient temperature range for fall and spring wereidentical, roads can behave differently in different seasons due tovariations in roadbed strength.

The tractor semi-trailer was tested using three separate loading conditions as follows:
Load Condition
Empty
Practical Full Load

GVW
16,000 kg
43,660 kg

Maximum Legal Load in Ontario/Quebec

49,400 kg

Note: Weights recorded on MTQ and MTO scales.

The load conditions were selected for the following reasons:
Empty: This represented a situation when a trailer has delivered its load and isreturning to its point of origin.
Nominal Full Load: This represented a typical situation where a trailer is loaded
to its maximum nominal weight with allowances for variations in payload location,snow and rain loading, extra passengers and fuel. This load situation, although
not at the legal limit, is the practical limit used by most operators. It is very
difficult to configure a vehicle at maximum legal gross weight while
simultaneously satisfying individual load restrictions on each axle group.
Maximum Legal Load: This represented the maximum permissible loading as
stipulated by the Ministries of Transportation in Quebec and Ontario. Although
this load is legal, it is not practical for normal revenue service as it requires
delicate balancing of gross weight and individual axle loads.
Table 2-1 summarizes the test sites and the acceptable test conditions and parameters used toacquired the data.

8

CSTT-HVC-TR-068
Table 2-1 Summary of Test Sites and Initial Test Conditions
Variable

Number of Conditions

Locations or Test Conditions
Concrete- Highway 440E/W, Laval, QC- Highway 13N, Laval, QC- Highway 40E/W, Vaudreuil, QC- Highway 417 E , East of
Casselman, ON- Highway 115W, Peterborough,ON

Pavement Structural
Types

3

Asphalt- Highway 40E, Rigaud, QC- Highway 25 Laval, QC- Highway 40W/E near Vaudreuil, QC- Highway 417E, Carp, ON- Highway 115E, Peterborough, ON- Highway 417E, Casselman, ON- Highway 115W, Peterborough,ON
Composite(Asphalt/Concrete)- Highway 440 W Laval, QC- Highway 401W, Morrisburg, ON

Pavement
Roughness

Variable

Vehicle Types

1

Load

3

Speed

3

Seasons5

Temperatures
Ranges4

Ambient Wind
Grade
Road Conditions

Variable
Variable

IRI < 23 axle tractor with tridem (3 axle) van
semi-trailer.
Empty – 16,000 kg
Typical Full – 43,660 kg
Maximum Legal – 49,400 kg
100, 80, 60 km/h
Spring, Summer Night, Summer Day,
Fall and WinterLess than –10 o C ,-5 to 10 o C
10 to 25 o C
Greater than 25 o C
Less than 10 km/h averageLess than 0.5%
Bare and dry

2.3.3 Test Procedure
For each loading and weather condition the following test method was observed.
i. Tire pressures were recorded for all tires
ii. Air was added (or removed) from those tires found to be outside the test
specification.

CSTT-HVC-TR-068

9iii. The data acquisition system was connected to the inline adapter plug on
the on-board Cummins engine management system.
iv. The test vehicle was driven to a test site at highway speed. All of the testsites were hundreds of kilometres from CSTT which allowed thepowertrain, wheel bearings and tires to become sufficiently warm.
v. Several kilometres before the start of a test site, the data acquisition
system was readied and zeroed and the tractor’s cruise control was
engaged at 100 km/h and the wind speed anemometer was erected in thevertical attitude. The use of cruise control minimized the effects oftransient vehicular accelerations and decelerations.
vi. As the tractor passed the established kilometre marker post, signifying
the beginning of a test site, the data acquisition system was engaged andrecording commenced.
vii. The tractor and trailer cruised over the test site in the right hand lane,
minimizing steer input.
viii. Any deviations of more than 2 km/h from the desired speed constituted afailed test and steps (v) through (vii) were repeated until a steady state
speed result was achieved.
ix. Steps (iv) through (viii) were repeated at 80 km/h and 60 km/h.
x. The test team then ferried the vehicle to the next test site and repeatedsteps (iii) through (ix) until the completion of that ‘test loop’.
xi. Steps (i) through (x) were then repeated on a separate day of testing inorder to capture data at all the ‘test loops’.
xii. All data were then saved for review and analysis. If a test run wasdetermined to be outside of the pre-determined test limits (e.g. windspeed greater than 10 km/h), steps (i) through (ix) were repeated.

2.4

Passenger Vehicle

2.4.1 Apparatus
To complement the tractor and trailer testing, a series of passenger car tests were conducted.
The test car was a 2002 Pontiac Grand Prix four-door sedan equipped with a 3.1 L V6 engine,
four speed automatic transmission and Good-Year Eagle L/S tires. The vehicle was received
off-lease with 42,000 km, ensuring that the engine was “broken in”. The tires were inspected
and found to be in excellent, and similar, condition. See Figure 2-3.

10

CSTT-HVC-TR-068

Figure 2-3 Front Three Quarter View of Pontiac Grand Prix

2.4.2 Test Conditions
The passenger car was tested in two (2) separate temperature ranges as follows:
SeasonWinterSummer Cool

Ambient Temperature Range< -10 oC>+15 oC and < +25 oC

These temperatures were selected to give a broad range of data, while respecting thelimited time allotted for car testing.

The passenger car was tested using one load condition as follows:
Load Condition
Curb weight, 2 occupants and baggage

Weight1756 kg

2.4.3 Test Methodology
The test methodology used for the passenger car was similar to that of the tractor semi-trailer
except that the software used to acquire these data was specifically written for General Motors
vehicles, and hence acquired a different set of engine parameters.

CSTT-HVC-TR-068

2.5

11

Tests and Surveys Performed by Third Party Contractors

Throughout the project, a variety of surveys were conducted by firms other than the CSTT/NRC.
Each of the surveys provided critical static road data to complement the acquired fuel flow data
used in the mathematical analysis. The surveys were as follows:
Precision GPS: A precision GPS survey was conducted to continuously define the roadcurvature and elevation/grade over the test sections. The sample rate was one measurement
every second at a speed of 80 km/h. Since curvature and elevation do not vary by season, theGPS survey was conducted once, at the beginning of the project.
International Roughness Index (IRI): An IRI survey was conducted to define the roughness of
the road surface over the test sections. The sample rate was one measurement every 50m foreach wheel path and the units of measurement were m/km. It is well documented that IRI
values can vary between seasons, therefore it was deemed essential to collect data seasonally.
Each of the seasonal multiple regression models included only the IRI data collected for thatseason. For a full description of IRI refer to: http://www.umtri.umich.edu/erd/roughness/iri.html.
Falling Weight Deflectometer (FWD): An FWD survey was conducted to define the strength of
the roadbed at various discrete locations on the test sections. The FWD consists of a flat platethat is pushed into the road with a known force. The force with which the road ‘pushes back’ isthen recorded and gives a measure of road strength. Road strength varies significantly withseasons therefore the FWD testing was conducted seasonally. For a full description of FWD
refer to http://www.dynatest.com/hardware/fwd_hwd.htm.

12

CSTT-HVC-TR-068

3 TEST SITES
3.1

Test Site Descriptions

The test sites were developed jointly by NRC/CSTT and the client steering committee. The
objective was to find multiple road sites that were constructed in concrete, asphalt andcomposite (concrete base with asphalt top layer), were nominally flat and straight, had IRI
values less than 2.0 and were within a daily operational radius of NRC/CSTT offices in Ottawa.
The steering committee suggested a series of sites and site evaluation trips were made to all
the proposed sites during the last two weeks of August 2002. Each site was assessed for its
topography, operational feasibility, total test loop distances and estimated test times. After theseassessments, and some further site modifications, the following sites were selected for inclusionin the program:
Site 1: 417E/W (West of Kanata) Asphalt
This section ran between Panmure Rd. (kilometre post (kp) 163) and March Rd. exit (kp 144). It
had significant grades at the westerly end of the section but this deficiency was offset by aseries of flat areas in the easterly end. Both directions were included in the test section.
Site 2: 417E ConcreteThis section started at kp 44.5 and ended at kp 9 and represented a newly constructed sectionof highway. Because of the extremely long distance between loop-around points in this section,
it was recommended that only one direction be used and that the speed test be done
sequentially (100, 80 and 60) through the section.
Site 3: 40E/W Rigaud Asphalt
This section started at kp 13 and ended at kp 8 on Highway 40 in Quebec just before the
Hudson off-ramp. The easterly direction was originally specified but the westerly direction wasadded as it was part of the loop back.
Site 4: 40E/W Hudson-Vaudreuil Concrete/Asphalt
This was a site that had been used in the Phase II testing (tanker trailers). The section starts at
kp 26, ends at kp 37, and has both asphalt and concrete pavements. The center section
contains an overpass and does not meet grade criteria (data taken on the overpass was omitted
from the analysis).
Site 5: 401E/W Morrisburg Composite
This section was on Highway 401 starting at kp 758 and ending at kp 738 near Morrisburg,
Ontario. This is a long section and thus sequential speed tests were performed over 7 km length
sections. Originally only the westerly direction was to be used but the easterly section wasresurfaced just prior to the testing program’s start and was thus included in the study.

Site 6: 13N/S Laval ConcreteThis section started at kp 10 and ended at kp 19.5 on Highway 13 (Quebec) with both directions
used. Loop backs were made at interchanges 20 and 8.
Site 7: 440/25 E/W Laval Concrete, Asphalt
This site was included in the previous study and contains both a concrete portion (Highway 440)
and an asphalt portion (Highway 25). The start and end points were at kp 18 and kp 15.5 on
Highway 25, and kp 18 and kp 31 on Highway 440. Testing was done in both directions.

CSTT-HVC-TR-068

13

Site 8: 40W Kirkland Asphalt
This section between kp 49 and kp 40 (Exit 40) was not included in the originally suggested test
sections but is quite flat and is on a route coming back from Site 6/7. The site was included withtesting at a constant speed (100 km/h).
Site 9: Hwy 115 NE/SW Concrete and Asphalt
This section of Highway 115 is constructed with alternating sections of concrete and asphalt
pavements. Some portions of the route have excessive grade, however, there are level sections
available for both pavement types. Loop arounds were done at interchanges for Highway 7A
and County Rd 32. Note that there are no kilometre posts on this highway and thus roadside
physical start and end queues were used. The north-easterly direction start was at the “no U
turn” sign and ended at the Highway 7A exit – “Fowler's Corners” exit sign. The south-westerly
start was opposite the Highway 7A exit – “Fowler's Corners” exit sign on the NE pavement.
Sites 10 and 11: 417E Asphalt
Sites 10 and 11 were added as they were sections of highway that were driven over to get toSite 2 on Highway 417. Site 11 was a portion of the test site used in the previous study near the
Casselman test site (kp 64 to kp 52). Site 10 is a very long section of flat asphalt from kp 103
through to kp 64. Both sections were tested in a single direction (east) at a single highway
speed of 100 km/h. Note that no IRI data were collected for these sections but they were judged
to be in good repair with estimated IRI values between 1 and 2.
The test site locations and lengths are summarized in Table 3-1 and indicate that a total of
268.3 linear km of road was used in the study. Given the number of test loads, speeds andtemperature conditions included in the study, a total of 8,954 km of test surfaces were driven
over to collect 736 data files.
Tables 3-1 through 3-4 illustrate the IRI, grade and curvature data for all of the test sites.
However, it should be noted that the data presented represents every metre of the test sites but
some of this data were rejected from the analysis as it was outside of the test parameters.

14

CSTT-HVC-TR-068

Table 3-1 Test Site Lengths and Characteristics, Sorted by Test Site
Site
No.

Province

1E
1W
2E
3E
3W
4E
4E
4W
4W
5E
5W
6N
6S
7N
7E
7S
7W
8W
9NE
9NE
9SW
9SW
10E
11E
Total

Ontario Highway 417
6.4
Ontario Highway 417
7.6
Ontario Highway 417
38.9
Quebec Highway 40
2.9
Quebec Highway 40
3.5
Quebec Highway 40
7.5
Quebec Highway 40
3.4
Quebec Highway 40
7.4
Quebec Highway 40
3.4
Ontario Highway 401
19.8
Ontario Highway 401
19.8
Quebec Highway 13
9.3
Quebec Highway 13
9.4
Quebec Highway 25
2.7
Quebec Highway 440
13.0
Quebec Highway 25
2.5
Quebec Highway 440
13.3
Quebec Highway 40
11.1
Ontario Highway 115
7.3
Ontario Highway 115
7.5
Ontario Highway 115
3.1
Ontario Highway 115
14.4
Ontario Highway 417
39.9
Ontario Highway 417
14.0
Total = 268.3km 115.9km 112.8 km 39.6 km•••

Location

Asphalt(km)

AverageConcrete Composite
Average IRI*
%Grade(km)(km)

International Roughness Index - summer values
Grades greater than 0.5% were rejected in the mathematical models
10E and 11E were sections from previous studies and no IRI data were collected

0.9
0.8
1.1
1.1
1.2
1.8
1.3
1.8
1.1
0.6
0.9
1.3
1.2
1.4
1.4
1.3
1.4
1.3
1.0
1.7
1.2
1.5
n/a
n/a

-0.44
0.45
0.01
0.16
-0.11
-0.35
-0.35
0.38
0.38
-0.01
-0.01
0.02
-0.01
-0.01
-0.01
0.03
0.03
-0.06
-0.92
-0.92
0.92
0.92
-0.05
0.02

Average Maximum Minimum%Curve % Grade % Grade

-0.03
-0.01
-0.04
0.18
-0.16
-0.08
-0.08
0.10
0.10
0.01
-0.01
0.01
0.00
-0.07
-0.07
-0.04
-0.04
0.08
0.01
0.01
-0.04
-0.04
-0.02
-0.05

+3.2
+1.8
+3.2
+1.0
+0.9
+1.0
+1.0
+0.9
+0.9
+0.8
+0.9
+2.3
+1.8
+2.0
+2.0
+3.1
+3.1
+0.9
+3.3
+3.3
+2.6
+2.6
n/a
n/a

-1.9
-2.1
-1.9
-0.9
-1.2
-0.9
-0.9
-1.2
-1.2
-1.1
-1.1
-2.1
-0.9
-2.9
-2.9
+2.1
+2.1
-1.2
-2.3
-2.3
-3.4
-3.4
n/a
n/a

15

CSTT-HVC-TR-068

Table 3-2 Test Site Lengths and Characteristics, Sorted by Pavement Type
Site
No.

Province

7S
7N
3E
9SW
3W
9NE
4W
4E
1W
8W
11E
10E

QuebecQuebecQuebecOntario
QuebecOntario
QuebecQuebecOntario
QuebecOntario
Ontario

Location

Highway 25
Highway 25
Highway 40
Highway 115
Highway 40
Highway 115
Highway 40
Highway 40
Highway 417
Highway 40
Highway 417
Highway 417
WeightedAsphalt
Average
4E
Quebec Highway 40
4W Quebec Highway 40
9NE Ontario Highway 115
6N
Quebec Highway 13
6S
Quebec Highway 13
7E
Quebec Highway 440
7W Quebec Highway 440
9SW Ontario Highway 115
2E
Ontario Highway 417
Weighted
Concrete
Average
5E
Ontario Highway 401
5W Ontario Highway 401
WeightedComp.
Average•••

Average
Asphalt Concrete Composite
Average IRI*
%Grade
(km)
(km)
(km)

2.5
2.7
2.9
3.1
3.5
7.3
7.4
7.5
7.6
11.1
14.0
39.9

3.4
3.4
7.5
9.3
9.4
13.0
13.3
14.4
38.9

19.8
19.8

International Roughness Index - summer values
Grades greater than 0.5% were rejected in the mathematical models
10E and 11E were sections from previous studies and no IRI data were collected

Average Maximum Minimum%Curve % Grade % Grade

1.3
1.4
1.1
1.2
1.2
1.0
1.8
1.8
0.8
1.3
n/a
n/a

0.03
-0.01
0.16
0.92
-0.11
-0.92
0.38
-0.35
0.45
-0.06
0.02
-0.05

-0.04
-0.07
0.18
-0.04
-0.16
0.01
0.10
-0.08
-0.01
0.08
-0.05
-0.02

3.1
2.0
1.0
2.6
0.9
3.3
0.9
1.0
1.8
0.9
n/a
n/a

2.1
-2.9
-0.9
-3.4
-1.2
-2.3
-1.2
-0.9
-2.1
-1.2
n/a
n/a

1.3

-0.02

-0.008

1.6

-1.5

1.3
1.1
1.7
1.3
1.2
1.4
1.4
1.5
1.1

-0.35
0.38
-0.92
0.02
-0.01
-0.01
0.03
0.92
0.01

-0.08
0.10
0.01
0.01
0.00
-0.07
-0.04
-0.04
-0.04

1.0
0.9
3.3
2.3
1.8
2.0
3.1
2.6
3.2

-0.9
-1.2
-2.3
-2.1
-0.9
-2.9
2.1
-3.4
-1.9

1.3

0.06

-0.03

2.7

-1.6

0.6
0.9

-0.01
-0.01

0.01
-0.01

0.8
0.9

-1.1
-1.1

0.8

-0.01

0.0

0.9

-1.1

16

CSTT-HVC-TR-068

Table 3-3 IRI Seasonal Means and Standard Deviations, Sorted by Test Site
SectionDescription

1E
1W
2E
3E
3W
4E
4E
4W
4W
5E
5W
6N
6S
7E
7W
7N
7S
8W
9NE
9NE
9SW
9SW

417 Eastbound Kanata (Asphalt)
417 Westbound Kanata (Asphalt)
417 Eastbound Casselman (Con.)
40 Eastbound Rigaud (Asphalt)
40 Westbound Rigaud (Asphalt)
40 E Hudson/Vaudreuil (Conc.)
40 E Hudson/Vaudreuil (Asphalt)
40 W Hudson/Vaudreuil (Conc.)
40 W Hudson/Vaudreuil (Asphalt)
401 Eastbound Prescott (Comp)401 Westbound Prescott (Comp)
13 Northbound Laval (Concrete.)
13 Southbound Laval (Concrete)
440 Eastbound Laval (Concrete)
440 Westbound Laval (Concrete)
25 Northbound Laval (Asphalt)
25 Southbound Laval (Asphalt)
40 Westbound Kirkland (Asphalt)
115 N Peterborough (Asphalt)
115 N Peterborough (Concrete)
115 S Peterborough (Asphalt)
115 S Peterborough (Concrete)

Length
(m)
7663
7696
35574
3504
3518
3400
7500
3400
7500
20072
20063
9516
9566
13050
13037
2506
2498
8159
7300
7500
3100
14400

Fall
2002
(m/km)
0.97
0.82
1.23
1.05
1.22
1.61
1.26
1.71
1.29
0.55
0.90
1.30
1.22
1.25
1.23
1.28
1.36
1.30
1.03
1.73
1.46
1.07

Winter2003(m/km)
1.10
1.12
1.29
1.09
2.07
1.76
1.20
1.92
1.22
0.94
1.09
1.40
1.32
1.31
1.28
1.60
1.81
1.54
1.20
1.80
1.55
1.58

Spring2003(m/km)
1.02
0.86
1.13
1.08
1.32
1.68
1.20
1.85
1.13
0.58
1.03
1.27
1.17
1.34
1.37
1.39
1.39
1.27
1.04
1.82
1.44
1.22

Summer2003(m/km)
1.05
0.87
1.13
1.07
1.36
1.77
1.33
1.78
1.14
0.58
1.09
1.27
1.17
1.35
1.36
1.37
1.42
1.27
0.98
1.69
1.48
1.18

Mean
(m/km)
1.04
0.92
1.20
1.07
1.49
1.69
1.26
1.82
1.28
0.66
1.03
1.31
1.22
1.31
1.31
1.41
1.50
1.35
1.04
1.76
1.49
1.28

Std
Dev
(m/km)
0.05
0.14
0.08
0.02
0.39
0.06
0.06
0.09
0.08
0.19
0.09
0.06
0.07
0.05
0.07
0.14
0.21
0.13
0.09
0.06
0.05
0.22

17

CSTT-HVC-TR-068

Table 3-4 IRI Seasonal Means and Standard Deviations, Sorted by Pavement Type

SectionDescription

7S
7N
9SW
3E
3W
9NE
4E
4W
1E
1W
8W

25 Southbound Laval (Asphalt)
25 Northbound Laval (Asphalt)
115 S Peterborough (Asphalt)
40 Eastbound Rigaud (Asphalt)
40 Westbound Rigaud (Asphalt)
115 N Peterborough (Asphalt)
40 E Hudson/Vaudreuil (Asphalt)
40 W Hudson/Vaudreuil (Asphalt)
417 Eastbound Kanata (Asphalt)
417 Westbound Kanata (Asphalt)
40 Westbound Kirkland (Asphalt)
Weighted Average Asphalt
40 E Hudson/Vaudreuil (Conc.)
40 W Hudson/Vaudreuil (Conc.)
115 N Peterborough (Concrete)
13 Northbound Laval (Concrete.)
13 Southbound Laval (Concrete)
440 Westbound Laval (Concrete)
440 Eastbound Laval (Concrete)
115 S Peterborough (Concrete)
417 Eastbound Casselman (Con.)
Weighted Average Concrete
401 Westbound Prescott (Comp)401 Eastbound Prescott (Comp)
Weighted Average Composite

4E
4W
9NE
6N
6S
7W
7E
9SW
2E
5W
5E

3.2

Length
(m)
2498
2506
3100
3504
3518
7300
7500
7500
7663
7696
8159
3400
3400
7500
9516
9566
13037
13050
14400
35574
20063
20072

Fall
2002
(m/km)
1.36
1.28
1.46
1.05
1.22
1.03
1.26
1.29
0.97
0.82
1.30
1.15
1.61
1.71
1.73
1.30
1.22
1.23
1.25
1.07
1.23
1.28
0.90
0.55
0.73

Winter2003(m/km)
1.81
1.60
1.55
1.09
2.07
1.20
1.20
1.22
1.10
1.12
1.54
1.33
1.76
1.92
1.80
1.40
1.32
1.28
1.31
1.58
1.29
1.41
1.09
0.94
1.02

Spring2003(m/km)
1.39
1.39
1.44
1.08
1.32
1.04
1.20
1.13
1.02
0.86
1.27
1.14
1.68
1.85
1.82
1.27
1.17
1.37
1.34
1.22
1.13
1.30
1.03
0.58
0.81

Summer2003(m/km)
1.42
1.37
1.48
1.07
1.36
0.98
1.33
1.14
1.05
0.87
1.27
1.16
1.77
1.78
1.69
1.27
1.17
1.36
1.35
1.18
1.13
1.28
1.09
0.58
0.84

Mean
(m/km)
1.50
1.41
1.49
1.07
1.49
1.04
1.26
1.28
1.04
0.92
1.35
1.21
1.69
1.82
1.76
1.31
1.22
1.31
1.31
1.28
1.20
1.32
1.03
0.66
0.85

Operational Routes

Because of the large geographical area that was covered by the test sites and the operational
starting base of the vehicle equipment supplier in Prescott, the sites were grouped into thefollowing three routes (test loops), which allowed for the completion of testing in one working
day for each:
Route 1 - Prescott to Montreal - collecting data on Site 5E (401E), Site 6 (Highway 13), Site 7(Highway 440/25), Site 8 (Highway 40W) and Site 5W (Highway 401W).
Route 2 - Prescott to Kanata – collecting data at Site 1 (Highway 417 at Carp) then through to417 E concrete, 40 Rigaud, Hudson/Vaudreuil and then back to Prescott via Highways 40, 450,
20 and 401.
Route 3 - Prescott to Peterborough via 401/115 and back the same way.

Std
Dev
(m/km)
0.21
0.14
0.05
0.02
0.39
0.09
0.06
0.08
0.05
0.14
0.13
0.11
0.06
0.09
0.06
0.06
0.07
0.07
0.05
0.22
0.08
0.09
0.09
0.19
0.14

18

CSTT-HVC-TR-068

4 PHASE III TRACTOR TRAILER RESULTS
As described in Section 3.1, there were a total of 736 individual test data files collected. The
following section summarizes the results of the data analysis.
4.1

Error Analysis and Test Repeatability

The published error rates for the instruments used to collect the data were as follows:
Anemometer, Wind Speed: +/- 1%
Anemometer, Wind Direction: +/- 5%
Cummins Data Acquisition System: +/- 1%
GPS accuracy: +/- 2 cm to local station, +/- 50 cm to global co-ordinate system.
IRI accuracy: +/- 0.5%
To ensure an appropriate level of test repeatability, the physical test site locations and test
conditions were carefully defined and controlled throughout the test program. In addition, theoperation of the truck and the accuracy and repeatability of the fuel use measurement was
tracked throughout the project. The principal control measure for season to season changes infuel flow measurement accuracy was through the comparison of the fuel used on a test day trip
set between the fill-up at the depot and the engine fuel use information. Since testing was
performed on a seasonal basis, there was no need for a diesel fuel temperature/volumetricadjustment. Short ‘loop-around’ road courses were utilized, as much as possible, to minimize
the effects of tail and head winds. A wind speed anemometer was used in order to compensate
for slight variations in head and tail wind. The anemometer was located well ahead of the
tractor’s pressure zone (as per SAE standards) in order to detect the true ambient wind speed
and direction. These data were collected in the same time domain as the fuel burn data,
allowing the data analysts to remove the wind’s effects from the vehicle’s fuel consumption.
In addition, periodic repeatability trials were performed in which the vehicle was driven twiceover the same section of road, at the same speed, load and climatic conditions. Sample datafrom one such test are presented at Figure 4-1 and indicate high repeatability, for that particulartest, with a 0.02% difference in the average fuel consumption rates between the two runs.

19

CSTT-HVC-TR-068

60

Instantaneous Fuel Consumption (L/100 km)

50

40

30

20

10

0
4.0

4.5

5.0

5.5

6.0

6.5

Test Speed (km) and Trial Number
80-1

7.0

7.5

8.0

Distance (km)

80-2

8.5

9.0

9.5

10.0

10.5

11.0

Test Site - Highway 115 NE
Empty Trailer - Test Weight 16,600 kg

Figure 4-1 Repeatability of Instantaneous Fuel Consumption Over a Test Course
4.2

Test Data Processing

The test data were contained in three separate data files:♦ Truck data – which contained the engine data, and included date and time, vehicle speed,engine RPM, fuel flow, throttle position;♦ Wind data – data from the front boom mounted anemometer♦ Test log file – Notes which include test data on vehicle weight, pavement temperatures,
ambient temperatures, and general notes and observations.
In addition, the test site data were contained in three files:♦ GPS topography data
♦ A seasonally specific IRI data set.
♦ A seasonally specific FWD data set.
All these files were merged into one unified data set. The truck and wind data were time based
data sets (a reading at defined time intervals), which located the physical site data in space(distance along road). The merging process involved converting the vehicle’s time-based data tospace-based data by estimating distance travelled between sampling time intervals (speed/time)and locating the start distance for the test. The GPS and IRI data were merged in space andtime respectively based on the truck data file’s distance and time for the start of the test. Test

20

CSTT-HVC-TR-068

load and temperatures were added to each test record along with the pavement structure data.
The final “metafile” contained all the test data in a unified frame of reference. Finally all the test
data, by season, was formed into one data file (without formulae) and analysed using thesoftware package Minitab.
4.3

Pavement Flexibility and Strength Tests

As presented in section 2.5, the strength of the road was measured using a Falling WeightDeflectometer test. The results of those tests may be found in Appendix B. However, it should
be noted that the effect of road strength was not an important variable in the mathematicalanalysis as the road beds used in this study were all of similar construction and quality. This is
not to say that road strength is not a factor in fuel efficiency, but rather, the roads used in thisstudy were all constructed using similar techniques and therefore these data became
insignificant for the purposes of the Phase III model.
4.4

Statistical Analysis

Multiple regression was used to investigate the effects of pavement structure on fuelconsumption rate. Data filtering was employed on the total data set to remove spurious data
and also to constrain the data within designated speed zones (removing speed transition data)and pavement roughness ranges. Post-test analysis revealed that the roads were significantly
smoother than previous studies, therefore the initial maximum value of IRI equalling 2.0 was
reduced to a maximum value of 1.6 for the Phase III data. Fuel consumption data collected onpavements with an IRI greater than 1.6 were thus not considered as part of the Phase III
analysis. Pavement structure was represented in the model by two indicator values, Pvash andPvcomp:••the first took on a value of one (1) for asphalt and zero (0) otherwise,
the other took a value of one (1) for composite and zero (0) otherwise.

Thus, concrete pavement was defined as the base category or structure. The analysis
developed a model which estimated fuel consumption rate (L/100km) as a function of pavement
structure, vehicle load, air or pavement temperature, vehicle speed, wind speed, IRI, grade, andvarious interactions among these variables. It was determined that the following single equation
form could be applied to all the seasonal subsets and to the combined data set.
FuelCon = Constant + Pvash * (1=asphalt) + Pvcomp * (1=composite) + IRI coeff * IRI + Gradecoefficient * Grade + Load coeff * Load + Pavement temperature coefficient *Pavetemp + Speed coefficient * Speed + AirSpdSq coefficient * AirSpdSq
Where
FuelCon = fuel consumption rate in L/100km
IRI = International Road Roughness Index
Grade = Road grade in percent
Load = Total vehicle mass in kilograms
Pavetemp = Pavement or ambient temperature in degrees Celsius
Speed = Vehicle road speed in km/h
AirSpdSq = Absolute air speed (road speed plus relative wind speed) squared.
This equational form provided the best explanatory power of a number evaluated and contained
all the variables measured in the study.

21

CSTT-HVC-TR-068

The following statistical terms are defined as:
tratio: This is the test statistic from which is computed the Pvalue. T ratio is dimension-lessand for the purposes of this study was defined as: Coef/SE Coef
Pvalue: Probability value, or the smallest value of significance (e.g. 5%) that would lead tothe rejection of a hypothesis. It provides an objective assessment of the validity of ahypothesis. If the Pvalue is less than a chosen level of significance, the hypothesis isrejected. Otherwise, the hypothesis is accepted.
Tables 4-1 through 4-6 provide the individual season model results for the truck and trailer andinclude the sample size, variable coefficients, statistical measures of variable significance,
statistical measures of variable error and overall correlation. All the seasonal models had strong
coefficients of determination (R2) values ranging between 65% and 79%. Given the large
number of records in the results database, these are very good modelling results. As an
example, the p-value reported in the last column of row 2 in Table 4-1 shows a value of 0.002.
This indicates that in only 0.2% of the time would a value larger than 0.4327 occur (Pvash).
Table 4-1 Truck Winter Model
Predictor
Coef SE Coef
Constant-17.61310.6102
Pvash
0.43270.1363
Pvcomp0.73680.2065IRI
0.64870.2016Grade
11.25390.1474Load
0.000118 1.95E-06
Pavetemp-0.20758
0.01425
Speed
0.345150.01069
AirSpdSq
0.001794 7.02E-05
10,049 observations R2 = 78.9%

tratio-28.87
3.18
3.57
3.22
76.35
60.46
-14.57
32.28
25.55

Pvalue
0
0.002
0
0.001
0
0
0
0
0

22

CSTT-HVC-TR-068

Table 4-2 Truck Spring Model
Predictor
Constant
Pvash
Pvcomp
IRI
Grade
Load
Pavetemp
Speed
AirSpdSq

Coef
SE Coef
tratio-15.2602
0.5269-28.96
0.68197
0.096327.08
0.36260.18791.93
0.89780.15455.81
16.80420.1169143.76
7.76E-05 2.26E-06
34.28-0.00556 0.02621-0.21
0.370659 0.008395
44.15
0.001414 5.42E-05
26.12
34,572 observations R2 = 65.5%

Pvalue
0
0
0.054
0
0
0
0.832
0
0

Table 4-3 Truck Summer Day Model
Predictor
Coef SE Coef
Constant-17.456
1.199
Pvash
0.61180.1439Pvcomp-0.5495
0.2088IRI
0.47580.2257Grade
15.6395
0.1645Load
8.74E-05
2.1E-06
Pavetemp
0.20555
0.02844
Speed
0.27921 0.008845
AirSpdSq
0.001437 5.43E-05
12,802 observations R2 = 68.1%

tratio-14.56
4.25
-2.63
2.11
95.08
41.68
7.23
31.57
26.44

Pvalue
0
0
0.009
0.035
0
0
0
0
0

Table 4-4 Truck Summer Night Model
Predictor
Coef SE Coef
Constant-2.8886
0.772
Pvash
0.16620.1296
Pvcomp0.84410.2118IRI
0.24120.2092Grade
15.4992
0.1475Load
1.98E-05
7E-07
Pavetemp-0.15004
0.01932
Speed
0.30356
0.01247
AirSpdSq
0.001372 8.33E-05
14,266 observations R2 = 69.9%

tratio
-3.74
1.28
3.99
1.15
105.1
28.45
-7.76
24.34
16.47

Pvalue
0
0.2
0
0.249
0
0
0
0
0

23

CSTT-HVC-TR-068

Table 4-5 Truck Fall Model
Predictor
Coef SE Coef
Constant-14.3128
0.6039Pvash
0.39450.1397
Pvcomp1.19590.2186IRI
1.32430.2148Grade12.4083
0.1581
Load
0.000121 2.76E-06
Pavetemp
0.08106
0.0335Speed
0.305450.01122
AirSpdSq
0.00171 7.59E-05
9,460 observations R2 = 76.3%

tratio
-23.7
2.82
5.47
6.17
78.51
43.59
2.42
27.22
22.55

Pvalue
0
0.005
0
0
0
0
0.016
0
0

Table 4-6 Truck All-season Model
Predictor
Coef SE Coef
Constant-8.6187
0.2487Pvash
0.55406
0.05838
Pvcomp0.141430.09747
IRI
0.5741
0.09187Grade15.17160.0694Load
4.39E-05
6.1E-07
Pavetemp-0.07563
0.00169
Speed
0.337456 0.004679
AirSpdSq
0.001444 3.02E-05
81,150 observations R2 = 67.1%

4.5

tratio-34.66
9.49
1.45
6.25
218.5571.52-44.7572.12
47.74

Pvalue
0
0
0.147
0
0
0
0
0
0

Analysis of Point Estimates

In order to quantify and compare the savings in fuel consumption on concrete relative to asphalt
or composite pavements, point estimates and confidence bounds were determined for expectedfuel consumption on each surface for a range of temperatures in each seasonal model.
Calculations were performed using the following assumptions:♦ an IRI of 1.0 to reflect a relatively smooth surface;♦ percent grade of 0;♦ speed of 100 km/h;♦ loads of 16,000 (“empty”), 43,660 (“full”), and 49,400 kg (“max”); and♦ relative wind speed of 0 km/h.
For the purposes of data analysis, rougher roads were not considered as part of Phase III.
Earlier models, from previous phases, indicated that surface roughness was a significant factor
contributing to fuel consumption (i.e. the greater the road roughness, the greater the amount offuel burned per distance travelled).
The estimates derived from each seasonal model for the empty and full loads at 100 km/h areprovided in Tables 4-7 through 4-18. Each table gives point estimates, as well as 95%
confidence bounds for expected fuel consumption for trucks on smooth road surfaces. Further,

24

CSTT-HVC-TR-068

for each ambient temperature, the point estimates for expected fuel consumption were used tocompute a percentage change in fuel consumption when driving on asphalt relative to concreteand composite relative to concrete.
The differences in fuel consumption rate between asphalt and concrete were equal to thecoefficient of Pvash itself. The differences in fuel consumption rate between composite andconcrete were equal to the coefficient of Pvcomp itself. An example of this is illustrated below,
where Pvash is equal to 0.4327 (Table 4-1):
If the absolute fuel consumption rate, denoted fc , were known exactly, the 95 % confidence
interval, denoted CI, for the percent difference would be:⎡ Lower 95%CI Upper 95%CI ⎤
,
⎢
⎥
fc
fc
⎣
⎦
where [Lower 95%CI , Upper 95%CI ] represents the 95% confidence interval for the difference
itself. Hence, for example, for the full winter model when the PaveTemp is–15, the difference is equal to Pvash = 0.4327, and the SE Coef is equal to 0.1363 (Table 4-1),
for which the 95% confidence interval is:[0.4327 – 1.96 (0.1363), 0.4327 + 1.96 (0.1363)]= [0.17, 0.70]
The estimate for concrete is equal to fc = 49.9. Hence, if the absolute fuel consumption rate
were known exactly, the 95 % confidence interval for the percent difference would be:[0.17/49.9, 0.70/49.9] = [0.34%, 1.40%].
Unfortunately, the rate fc is not known exactly and a 95% confidence interval for it is obtained
from Table 4-7 as [49.6, 50.2].
[0.17/50.2, 0.70/49.6] = [0.34%, 1.41%].
In order to compute a 95% confidence interval it is necessary to combine two confidence
intervals into a single probability statement by making use of the Bonferroni inequality. Thisrequires the construction of 97.5 % confidence intervals for each of the numerator anddenominator. This, in turn, would require a cutoff value equal to 2.24 be used, instead of 1.96, inthe various calculations.
By way of example, 97.5% confidence intervals were calculated for the percentage difference aswell as for the absolute fuel consumption using concrete, using the example calculations shown
above. This yielded the intervals [0.1274, 0.7380] [49.557, 50.243] respectively. Hence, an
overall 95% confidence interval for the percentage difference becomes:[0.1274/50.243, 0.7380/49.557] = [0.25 %, 1.49%].
Therefore, there is a 95% probability that, under the conditions specified above, the test truck
would have burned between 0.25% and 1.49% less fuel on concrete roads, when compared toCSTT-HVC-TR-068

25

the asphalt roads. Since zero is not contained within this interval, the result is considered
statistically significant. Conversely, if the two values had spanned 0% (e.g. [-0.25%, 1.49%]),the results would be considered not statistically significant. Tables 4-7 through 4-18 illustrate all
the 95% confidence intervals over the various road and weather conditions and whether or noteach result is significant or not significant (calculated using the combined 97.5% intervals).
Shaded cells represent results that have been computed to be not statistically significant.
A graphical presentation of the point concrete road estimates for all the seasonal models andload conditions is shown in Figure 4-2. The asphalt and composite plots are similar but offset by
their model coefficients. Note that the Spring model data has been excluded from the graph as
the data points are very close to the Fall data and as the temperature differentials in the Springdata were small there is very low sensitivity in the model to temperature.
The point estimates for each seasonal model and the combined all season model for a “full”(43,660 kg) load condition and 100 km/h are illustrated in Figure 4-3 and clearly show thatacross all the seasonal models, the sensitivity to ambient temperature is higher using theseasonal models. Further, the seasonal models have higher coefficients of determination for themultiple regression models and thus are better models. Finally, the slopes of the temperaturesensitivity are not consistent among the seasonal models with the Fall and Summer Day models
indicating a positive slope and the Winter and Spring (and Summer Night although not plottedon the graph) had negative slopes. Even with these anomalies, the model clearly indicates thatthe effect of temperature far outweighs the effect of the pavement types, both within eachseason and across the total temperature range that was tested.
As a further example of the model’s sensitivity, Table 4-19 presents the estimates across thecomplete temperature range at the test speed of 60 km/h with no load (16,000 kg). The
estimates indicate a 0.25 L/100km difference between concrete and composite which is notstatistically significant at the 95th percentile confidence bound and a 0.55 L/100km difference
between concrete and asphalt which was computed to be statistically significant.

26

CSTT-HVC-TR-068
Table 4-7 Full, Winter Point Estimates, 100km/h,IRI =1.0,pavement temps= –20,–15, & –10o C
Pave
Temp

Surface

Estimate

95
Lower

95
Upper

97.5
Lower

97.5
Upper

-10
-15
-20
-10
-15
-20
-10
-15
-20

Asphalt
Asphalt
Asphalt
Composite
Composite
Composite
Concrete
Concrete
Concrete

49.3
50.3
51.4
49.6
50.6
51.7
48.9
49.9
50.9

48.9
50.0
51.0
49.2
50.3
51.3
48.5
49.6
50.6

49.6
50.6
51.7
49.9
50.9
52.0
49.2
50.2
51.3

48.9
50.0
51.0
49.2
50.3
51.3
48.5
49.6
50.5

49.7
50.7
51.8
50.0
51.0
52.1
49.3
50.2
51.3

Absolute
Diff (low)L/100km
0.1
0.1
0.1
0.3
0.3
0.3

Absolute
Diff (High)
L/100km
0.7
0.7
0.7
1.2
1.2
1.2

Percent
Diff (low)
0.3%
0.3%
0.2%
0.6%
0.5%
0.5%

PercentDiff(high)
1.5%
1.5%
1.5%
2.5%
2.4%
2.4%

Table 4-8 Full, Spring Point Estimates (100km/h,IRI =1.0,pavement temps = 10,15,& 20o C)
Pave
Temp

Surface

20
15
10
20
15
10
20
15
10

Asphalt
Asphalt
Asphalt
Composite
Composite
Composite
Concrete
Concrete
Concrete

Estimate

95
Lower

95
Upper

97.5
Lower

97.5
Upper

44.9
44.9
44.9
44.5
44.6
44.6
44.2
44.2
44.2

44.5
44.7
44.6
44.1
44.4
44.3
43.8
44.0
43.9

45.3
45.1
45.2
44.9
44.8
44.9
44.6
44.4
44.5

44.4
44.7
44.6
44.1
44.3
44.3
43.7
44.0
43.9

45.3
45.1
45.3
45.0
44.8
44.9
44.6
44.4
44.6

Shaded cells indicate fuel consumption differences that are not statistically significant

Absolute
Diff (low)L/100km
0.5
0.5
0.5

Absolute
Diff (High)
L/100km
0.9
0.9
0.9

-0.1
-0.1
-0.1

0.8
0.8
0.8

1.0%
1.0%
1.0%

PercentDiff(high)
2.1%
2.0%
2.0%

-0.1%
-0.1%
-0.1%

1.8%
1.8%
1.8%

Percent
Diff (low)CSTT-HVC-TR-068

27

Table 4-9 Full, Summer Day Point Estimates, 100km/h, IRI =1.0, pavement temps = 25, 30 & 35o C
Pave
Temp

Surface

35
30
25
35
30
25
35
30
25

Asphalt
Asphalt
Asphalt
Composite
Composite
Composite
Concrete
Concrete
Concrete

Estimate

95
Lower

95
Upper

97.5
Lower

97.5
Upper

41.5
40.4
39.4
40.3
39.3
38.2
40.8
39.8
38.8

41.1
39.9
38.6
39.9
38.7
37.5
40.5
39.3
38.1

41.8
41.0
40.1
40.6
39.8
39.0
41.2
40.4
39.6

41.1
39.8
38.5
39.9
38.6
37.4
40.4
39.2
37.9

41.9
41.0
40.2
40.7
39.9
39.1
41.2
40.4
39.6

Absolute
Diff (low)L/100km
0.3
0.3
0.3
-1.0
-1.0
-1.0

Absolute
Diff (High)
L/100km
0.9
0.9
0.9
-0.1
-0.1
-0.1

Percent
Diff (low)
0.7%
0.7%
0.7%
-2.5%
-2.5%
-2.6%

PercentDiff(high)
2.3%
2.4%
2.5%
-0.2%
-0.2%
-0.2%

Table 4-10 Full, Summer Night Point Estimates, 100km/h, IRI =1.0, pavement temps = 20, 25 & 30o C
Pave
Temp

Surface

Estimate

95
Lower

95
Upper

97.5
Lower

97.5
Upper

30
25
20
30
25
20
30
25
20

Asphalt
Asphalt
Asphalt
Composite
Composite
Composite
Concrete
Concrete
Concrete

39.0
39.7
40.5
39.7
40.4
41.2
38.8
39.6
40.3

38.6
39.4
40.2
39.3
40.1
40.9
38.4
39.2
40.0

39.5
40.1
40.9
40.2
40.8
41.6
39.3
39.9
40.7

38.5
39.3
40.1
39.2
40.0
40.8
38.3
39.2
39.9

39.5
40.1
40.9
40.2
40.8
41.6
39.3
40.0
40.7

Shaded cells indicate fuel consumption differences that are not statistically significant

Absolute
Diff (low)L/100km
-0.1
-0.1
-0.1
0.4
0.4
0.4

Absolute
Diff (High)
L/100km
0.5
0.5
0.5
1.3
1.3
1.3

Percent
Diff (low)
-0.3%
-0.3%
-0.3%
0.9%
0.9%
0.9%

PercentDiff(high)
1.2%
1.2%
1.1%
3.4%
3.4%
3.3%

28

CSTT-HVC-TR-068
Table 4-11 Full, Fall Point Estimates, 100km/h, IRI =1.0, pavement temps = -5, 0 & +5o C
Pave
Temp

Surface

Estimate

95
Lower

95
Upper

97.5
Lower

97.5
Upper

5
0
-5
5
0
-5
5
0
-5

Asphalt
Asphalt
Asphalt
Composite
Composite
Composite
Concrete
Concrete
Concrete

47.0
46.6
46.2
47.8
47.4
47.0
46.6
46.2
45.8

46.4
46.2
45.9
47.2
47.0
46.7
46.0
45.8
45.5

47.7
47.1
46.6
48.5
47.9
47.4
47.3
46.7
46.2

46.3
46.1
45.8
47.1
46.9
46.6
45.9
45.7
45.4

47.8
47.1
46.6
47.8
47.4
47.0
47.4
46.8
46.2

Pave
Temp

Surface

35
25
15
0
-15
35
25
15
0
-15
35
25
15
0
-15

Asphalt
Asphalt
Asphalt
Asphalt
Asphalt
Composite
Composite
Composite
Composite
Composite
Concrete
Concrete
Concrete
Concrete
Concrete

Absolute
Diff (low)L/100km
0.1
0.1
0.1
0.7
0.7
0.7

Absolute
Diff (High)
L/100km
0.7
0.7
0.7
1.7
1.7
1.7

Percent
Diff (low)
0.2%
0.2%
0.2%
1.5%
1.5%
1.5%

Table 4-12 Full, All data Model Point Estimates (Speed=100, IRI =1.0)
Absolute Absolute
95
95
97.5
97.5
Percent
Estimate
Diff (low) Diff (High)
Lower Upper Lower Upper
Diff (low)L/100km
L/100km
42.3
43.0
43.8
44.9
46.0
41.8
42.6
43.4
44.5
45.6
41.7
42.5
43.2
44.4
45.5

42.2
42.9
43.7
44.8
45.8
41.7
42.5
43.3
44.4
45.4
41.6
42.3
43.1
44.2
45.3

42.5
43.2
44.0
45.1
46.2
42.0
42.8
43.6
44.7
45.8
41.9
42.6
43.4
44.5
45.7

42.1
42.8
43.6
44.7
45.8
41.6
42.4
43.2
44.3
45.4
41.5
42.3
43.0
44.2

42.5
43.2
44.0
45.1
46.2
41.8
42.6
43.4
44.5
45.6
41.9
42.7
43.4
44.6

0.4
0.4
0.4
0.4
0.4
-0.1
-0.1
-0.1
-0.1
-0.1

0.7
0.7
0.7
0.7
0.7
0.4
0.4
0.4
0.4
0.4

1.0%
1.0%
1.0%
0.9%
0.9%
-0.2%
-0.2%
-0.2%
-0.2%
-0.2%

PercentDiff(high)
1.5%
1.5%
1.6%
3.7%
3.7%
3.7%

PercentDiff(high)
1.6%
1.6%
1.6%
1.5%
1.5%
0.9%
0.8%
0.8%
0.8%
0.8%

CSTT-HVC-TR-068

29

Table 4-13 Empty,Winter Point Estimates,100km/h,IRI =1.0,pavement temp= -20,-15,& -10o C
Pave
Temp

Surface

Estimate

95
Lower

95
Upper

97.5
Lower

97.5
Upper

-10
-15
-20
-10
-15
-20
-10
-15
-20

Asphalt
Asphalt
Asphalt
Composite
Composite
Composite
Concrete
Concrete
Concrete

42.3
43.3
44.3
42.6
43.6
44.7
41.8
42.9
43.9

41.9
43.0
44.0
42.2
43.3
44.3
41.5
42.60
43.6

42.6
43.6
44.7
42.9
43.9
45.0
42.20
43.20
44.30

41.9
43.0
43.9
42.2
43.3
44.3
41.4
42.5
43.5

42.7
43.7
44.7
43.0
44.0
45.1
42.2
43.2
44.3

Absolute
Diff (low)L/100km

Absolute
Diff (High)
L/100km

Percent
Diff (low)PercentDiff(high)

0.1
0.1
0.1

0.7
0.7
0.7

0.3%
0.3%
0.3%

1.8%
1.7%
1.7%

0.3
0.3
0.3

1.2
1.2
1.2

0.6%
0.6%
0.6%

2.9%
2.8%
2.8%

Table 4-14 Empty, Spring Point Estimates,100km/h,IRI =1.0,pavement temps =10,15, & 20o C
Pave
Temp

Surface

Estimate

95
Lower

95
Upper

97.5
Lower

97.5
Upper

10
15
20
10
15
20
10
15
20

Asphalt
Asphalt
Asphalt
Composite
Composite
Composite
Concrete
Concrete
Concrete

40.2
40.2
40.3
39.9
39.9
40.0
39.6
39.6
39.6

39.7
39.8
39.9
39.3
39.5
39.6
39.0
39.2
39.3

40.8
40.6
40.6
40.4
40.3
40.3
40.1
40.0
40.0

39.6
39.8
39.9
39.3
39.5
39.6
39.0
39.1
39.2

40.8
40.7
40.7
40.5
40.4
40.4
40.2
40.1
40.0

Shaded cells indicate fuel consumption differences that are not statistically significant

Absolute
Diff (low)L/100km

Absolute
Diff (High)
L/100km

Percent
Diff (low)PercentDiff(high)

0.5
0.5
0.5
-0.1
-0.1
-0.1

0.9
0.9
0.9
0.8
0.8
0.8

1.2%
1.2%
1.2%
-0.1%
-0.1%
-0.1%

2.3%
2.3%
2.3%
2.0%
2.0%
2.0%

30

CSTT-HVC-TR-068

Table 4-15 Empty, Summer Day Point Estimates, 100km/h, IRI =1.0, pavement temps = 25, 30, & 35o C
Pave
Temp

Surface

35
30
25
35
30
25
35
30
25

Asphalt
Asphalt
Asphalt
Composite
Composite
Composite
Concrete
Concrete
Concrete

Estimate

95
Lower

95
Upper

97.5
Lower

97.5
Upper

36.3
35.2
34.2
35.1
34.1
33.1
35.7
34.6
33.6

35.9
34.7
33.4
34.7
33.5
32.3
35.3
34.1
32.8

36.7
35.8
35.0
35.5
34.6
33.9
36.1
35.2
34.4

35.8
34.6
33.3
34.7
33.5
32.1
35.2
34.0
32.7

36.7
35.9
35.1
35.6
34.7
34.0
36.1
35.3
34.5

Absolute
Diff (low)L/100km

Absolute
Diff (High)
L/100km

Percent
Diff (low)PercentDiff(high)

0.3
0.3
0.3

0.9
0.9
0.9

0.8%
0.8%
0.8%

2.7%
2.7%
2.9%

-1.0
-1.0
-1.0

-0.1
-0.1
-0.1

-2.8%
-2.9%
-2.9%

-0.2%
-0.2%
-0.2%

Table 4-16 Empty, Summer Night Point Estimates, 100km/h, IRI =1.0, pavement temps = 20, 25, & 30o C
Pave
Temp

Surface

Estimate

95
Lower

95
Upper

97.5
Lower

97.5
Upper

30
25
20
30
25
20
30
25
20

Asphalt
Asphalt
Asphalt
Composite
Composite
Composite
Concrete
Concrete
Concrete

37.8
38.6
39.3
38.6
39.2
40.0
37.6
39.1
38.4

37.4
38.3
38.9
38.1
38.9
39.6
37.2
38.8
38.0

38.3
39.0
39.7
39.0
39.6
40.4
38.1
39.5
38.8

37.3
38.2
38.8
38.0
38.8
39.5
37.1
38.7
37.9

38.3
39.0
39.8
39.1
39.6
40.5
38.1
39.5
38.9

Shaded cells indicate fuel consumption differences that are not statistically significant

Absolute
Diff (low)L/100km

Absolute
Diff (High)
L/100km

Percent
Diff (low)PercentDiff(high)

-0.1
-0.1
-0.1

0.5
0.5
0.5

-0.3%
-0.3%
-0.3%

1.2%
1.2%
1.2%

0.4
0.4
0.4

1.3
1.3
1.3

1.0%
0.9%
1.0%

3.6%
3.4%
3.5%

CSTT-HVC-TR-068

31

Table 4-17 Empty, Fall Point Estimates, 100km/h, IRI =1.0, pavement temps = -5, 0 & +5o C
PaveT
emp

Surface

Estimate

95
Lower

95
Upper

97.5
Lower

97.5
Upper

5
0
-5
5
0
-5
5
0
-5

Asphalt
Asphalt
Asphalt
Composite
Composite
Composite
Concrete
Concrete
Concrete

39.8
39.4
39.0
40.6
40.2
39.8
39.5
38.7
39.1

39.3
39.0
38.7
40.1
39.8
39.5
39.0
38.2
38.7

40.3
39.9
39.4
41.1
40.7
40.2
40.0
39.1
39.4

39.3
38.9
38.6
40.1
39.7
39.4
38.9
38.2
38.7

40.4
39.9
39.4
41.2
40.8
40.2
40.1
39.2
39.5

Absolute
Diff (low)L/100km
0.1
0.1
0.1
0.7
0.7
0.7

Absolute Diff
(High)
L/100km
0.7
0.7
0.7
1.7
1.7
1.7

Percent
Diff (low)Percent
Diff (high)

0.2%
0.2%
0.2%
1.8%
1.8%
1.8%

1.8%
1.9%
1.8%
4.3%
4.4%
4.3%

Percent
Diff (low)Percent
Diff (high)

1.1%
1.1%
1.0%
1.0%
1.0%
-0.2%
-0.2%
-0.2%
-0.2%
-0.2%

1.8%
1.7%
1.7%
1.6%
1.6%
0.9%
0.9%
0.9%
0.9%
0.8%

Table 4-18 Empty, All data Model Point Estimates (Speed=100, IRI =1.0)
PaveT
emp
35
25
15
0
-15
35
25
15
0
-15
35
25
15
0
-15

Surface

Estimate

95
Lower

95
Upper

97.5
Lower

97.5
Upper

Absolute
Diff (low)L/100km
0.4
0.4
0.4
0.4
0.4
-0.1
-0.1
-0.1
-0.1
-0.1

Asphalt
39.6
39.4
39.8
39.4
39.8
Asphalt
40.4
40.3
40.6
40.2
40.6
Asphalt
41.2
41.1
41.4
41.0
41.4
Asphalt
42.3
42.2
42.5
42.1
42.5
Asphalt
43.4
43.3
43.6
43.2
43.6
Composite
39.2
39.0
39.4
39.0
39.4
Composite
40.0
39.9
40.2
39.8
40.2
Composite
40.7
40.6
40.9
40.5
40.9
Composite
41.9
41.8
42.1
41.7
42.1
Composite
43.0
42.9
43.2
42.8
43.2
Concrete
39.1
38.9
39.3
38.9
39.3
Concrete
39.9
39.7
40
39.7
40.1
Concrete
40.6
40.5
40.8
40.4
40.8
Concrete
41.7
41.6
41.9
41.5
41.9
Concrete
42.9
42.7
43
42.7
43.1
Shaded cells indicate fuel consumption differences that are not statistically significant

Absolute Diff
(High)
L/100km
0.7
0.7
0.7
0.7
0.7
0.4
0.4
0.4
0.4
0.4

32

CSTT-HVC-TR-068
Table 4-19 Empty, All Data Model Point Estimates (IRI = 1.0, Speed = 60 km/h)

Pave
Temp

Surface

Estimate

95
Lower

95
Upper

97.5
Lower

97.5
Upper

35
25
15
0
-15
35
25
15
0
-15
35
25
15
0
-15

Asphalt
Asphalt
Asphalt
Asphalt
Asphalt
Composite
Composite
Composite
Composite
Composite
Concrete
Concrete
Concrete
Concrete
Concrete

19.2
17.8
16.3
15.3
14.4
19
17.6
16.1
15.1
14.2
18.7
17.3
15.8
14.9
13.9

19.0
17.6
16.1
15.1
14.2
18.8
17.3
15.9
14.9
13.9
18.6
17.1
15.8
14.7
13.7

19.4
17.9
16.5
15.5
14.6
19.3
17.8
16.3
15.4
14.4
18.9
17.5
16.0
15.0
14.1

19.0
17.6
16.2
15.1
14.2
18.8
17.4
16.0
14.9
14.0
18.5
17.1
15.7
14.7
13.7

19.4
18.0
16.4
15.5
14.6
19.2
17.8
16.2
15.3
14.4
18.9
17.5
15.9
15.1
14.1

Shaded cells indicate fuel consumption differences that are not statistically significant

Absolute
Diff (low)L/100km
0.4
0.4
0.4
0.4
0.4
-0.1
-0.1
-0.1
-0.1
-0.1

Absolute
Diff (High)
L/100km
0.7
0.7
0.7
0.7
0.7
0.4
0.4
0.4
0.4
0.4

Percent
Diff (low)
2.2%
2.4%
2.7%
2.8%
3.0%
-0.4%
-0.4%
-0.5%
-0.5%
-0.5%

PercentDiff(high)
3.7%
4.0%
4.4%
4.6%
5.0%
1.9%
2.1%
2.3%
2.4%
2.6%

33

CSTT-HVC-TR-068

60

Estimated Fuel Consumption Rate (L/100km)

50

40

30

Model Assumptions
Grade = 0%
IRI = 1Wind = 0 km/h
Loads:
Empty = 16,600 kg
Full = 43,600 kg
Maximum = 49,500 kg

20

10

0
-30

-20

-10

0

10

20

30

40

Ambient Temperature (C)Empty-Summer Day
Empty-Fall

Full-Summer Day
Full-Fall

Max-Summer Day
Max-FallEmpty-Summer Night
Empty-WinterFull-Summer Night
Full-Winter

Max-Summer Night
Max-WinterModel Estimated Fuel Consumption (L/100km) 45,600 kg,IRI=1.0, Grade =0,Wind =0

Figure 4-2 Truck Model Estimates – Concrete

52.0

50.0

48.0

46.0

44.0

42.0

40.0

38.0

36.0
-30.0

-20.0
Summer Day Concrete
Spring Concrete
Fall Concrete
Winter Concrete
All ConcreteLinear (All Composite)

-10.0

0.0

10.0

Ambient Temperature (C)Summer Day Asphalt
Spring Asphalt
Fall Asphalt
Winter Asphalt
All Asphalt
Linear (All Asphalt)

20.0

30.0

Summer Day Composite
Spring Composite
Fall Composite
Winter Composite
All Composite
Linear (All Concrete)Figure 4-3 Truck Model Estimates, Full Load At 100 km/h (43,660 kg)

40.0

34

CSTT-HVC-TR-068

Estimated Percent Change in Fuel Consumption Rate (L/100km) on Asphalt Comparedto Concrete

The results of the percentage change analysis for all load conditions at 100 km/h arepresented in Figure 4-4 for the comparison of asphalt to concrete pavements and in Figure
4-5 for the composite to concrete comparison. Both these figures do not show theconfidence bounds to allow clarity of presentation. However, examples of these bounds
are presented for the full load condition for the asphalt to concrete comparison in Figure 47 and for the composite to concrete comparison in Figure 4-9. The corresponding
comparisons at empty loading are found at Figures 4-6 and 4-8. Similar plots of thepercentage changes at 60 km/h are provided at Figures 4-10 through 4-15.

2.0%Model Assumptions
Grade = 0%
IRI = 1Wind = 0 km/h
Loads:
Empty = 16,600 kg
Full = 43,600 kg
Maximum = 49,500 kg

1.8%

1.6%

1.4%

SpringSummer Day

1.2%

1.0%

0.8%
Winter

Fall

0.6%

0.4%
Summer Night
0.2%

0.0%
-30

-20
Summer Day Empty
Fall Full
Summer Night Max-10
Summer Day Full
Spring Empty
Summer Day Max

010

Summer Night Empty
Spring Full
Fall Max

20
Summer Night Full
Winter Empty
Spring Max

30
Fall Empty
Winter Full
Winter Max

Figure 4-4 Asphalt-Concrete Percentage Change Estimates at 100 km/h

40

35

CSTT-HVC-TR-068

Estimated Percent Change in Fuel Consumption Rate (L/100km)
on Composite Pavement Compared to Concrete

4.0%
Model Assumptions
Grade = 0%
IRI = 1Wind = 0 km/h
Loads:
Empty = 16,600 kg
Full = 43,600 kg
Maximum = 49,500 kg

3.0%

2.0%

Fall

Summer Night
Winter
1.0%
Spring
0.0%
-30

-20

-10

0

10

20

30

40

Summer Day

-1.0%

-2.0%
Pavement/Ambient Temperature (C)Empty-Summer Day
Max-Summer Night
Full-WinterFull-Summer Day
Empty-Fall
Max-Winter

Max-Summer Day
Full-Fall
Empty-Spring

Empty-Summer Night
Max-FallFull-Spring

Full-Summer Night
Empty-Winter
Max-Spring

Figure 4-5 Composite-Concrete Percentage Change Estimates at 100 km/h

3.50%

Percentage Difference (Aphalt to Concrete)

3.00%

2.50%

2.00%

1.50%

1.00%

0.50%

0.00%

-0.50%
-25

-20

-15

-10

-5

0

5

10

15

20

25

Temperature (deg C)
WinterSummer Night

SpringFall

Summer Day

Figure 4-6 Asphalt 95% Confidence Bounds - Empty, 100 km/h

30

35

40

36

CSTT-HVC-TR-068

3.00%

Percentage Difference (Asphalt to Concrete)

2.50%

2.00%

1.50%

1.00%

0.50%

0.00%

-0.50%
-30

-20

-10

0

10

20

30

40

Temperature (deg C)
WinterFall

SpringSummer Night

Summer Day

Figure 4-7 Asphalt 95% Confidence Bounds – Full Load at 100 km/h

5.00%

Percentage Difference (Composite to Concrete)

4.00%

3.00%

2.00%

1.00%

0.00%

-1.00%-2.00%-3.00%-4.00%
-25

-20

-15

-10

-5

0

5

10

15

20

25

30

35

Temperature (deg C)
WinterSummer Day

Fall

SpringSummer Night

Figure 4-8 Composite, 95% Confidence Bounds - Empty, 100 km/h

40

37

CSTT-HVC-TR-068

5.00%

Percentage Difference (Composite to Concrete)

4.00%

3.00%

2.00%

1.00%

0.00%

-1.00%-2.00%-3.00%
-30

-20

-10

0

10

20

30

40

Temperature (deg C)
WinterFall

SpringSummer Night

Summer Day

Figure 4-9 Composite, 95% Confidence Bounds, Full, 100 km/h

Estimated Percent Change in Fuel Consumption Rate (L/100km)
on Asphalt Compared to Concrete

4.0%
SpringM odel Assum ptions
Speed = 60 km /h
Grade = 0%
IRI = 1
W ind = 0 km /h
Loads:Em pty = 16,600 kg
Full = 43,600 kg
Maxim um = 49,500 kg

3.5%

3.0%

2.5%

Sum m er Day

2.0%
Fall

W inter
1.5%

1.0%

0.5%

Sum m er Night

0.0%
-30

-20

-10

0

10

20

30

Pavem ent/Am bient Tem perature (C)Em pty-Sum m er Day
Max-Sum m er Night
Full-W interFull-Sum m er Day
Em pty-Fall
M ax-W interM ax-Sum m er Day
Full-FallEm pty-Spring

Em pty-Sum m er Night
Max-FallFull-Spring

Full-Sum m er NightEm pty-W interM ax-Spring

Figure 4-10 Asphalt Percent Change At 60 km/h (All loads)

40

38

CSTT-HVC-TR-068

Estimated Percent Change in Fuel Consumption Rate (L/100km)
on Composite Pavement Compared to Concrete

8.0%
Model Assumptions
Speed = 60 km/h
Grade = 0%
IRI = 1Wind = 0 km/h
Loads:
Empty = 16,600 kg
Full = 43,600 kg
Maximum = 49,500 kg

6.0%

4.0%

Fall

Summer Night

Winter2.0%
Spring0.0%

Summer Day

-2.0%

-4.0%
-30

-20

-10

0

10

20

30

40

Pavement/Ambient Temperature (C)Empty-Summer Day
Max-Summer Night
Full-WinterFull-Summer Day
Empty-Fall
Max-Winter

Max-Summer Day
Full-Fall
Empty-Spring

Empty-Summer Night
Max-FallFull-Spring

Full-Summer Night
Empty-Winter
Max-Spring

Figure 4-11 Composite Percent Change At 60 km/h (All loads)

5.0%

Percentage Difference (Asphalt to Concrete)

4.0%

3.0%

2.0%

1.0%

0.0%
-30

-20

-10

0

10

20

30

40

-1.0%
Temperature (deg C)
WinterFall

SpringSummer Night

Summer Day

Figure 4-12 Asphalt Percent Difference- 95% Confidence Bounds, empty, 60 km/h

39

CSTT-HVC-TR-068

4.0%

Percentage Difference (Asphalt to concrete)

3.5%

3.0%

2.5%

2.0%

1.5%

1.0%

0.5%

0.0%
-30

-20

-10

0

10

20

30

40

50

Temperature (deg C)
WinterFall

SpringSummer Night

Summer Day

Figure 4-13 Asphalt, Percent Difference- 95% Confidence Bounds, full, 60 km/h

10.0%Percentage Difference (Composite to Concrete)

8.0%

6.0%

4.0%

2.0%

0.0%

-2.0%

-4.0%

-6.0%

-8.0%
-30

-20

-10

0

10

20

30

40

50

Temperature (deg C)
WinterFall

SpringSummer Night

Summer Day

Figure 4-14 Comp. Percent Difference- 95% Confidence Bounds, empty, 60 km/h

40

CSTT-HVC-TR-068

8.0%

Percentage Difference (Composite to Concrete)

6.0%

4.0%

2.0%

0.0%

-2.0%

-4.0%

-6.0%
-30

-20

-10

0

10

20

30

40

Temperature (deg C)
WinterFall

SpringSummerNight

Summer Day

Figure 4-15 Comp. Percent Difference- 95% Confidence Bounds, full, 60 km/h

50

CSTT-HVC-TR-068

4.6

41

Phase III Truck Model Findings

There are a number of overall observations to be made from the truck point estimates:•

The Fall and Summer Day models have positive coefficients for pavement temperatureand thus the fuel consumption increases with higher temperatures. This anomaly may
have some basis in physics due to the plastic nature of the materials which may
increase the rolling resistance at higher material temperatures;•

The Summer Night data is an anomaly as it indicates a very low sensitivity to loadchanges.

The fuel consumption difference analysis indicates that, if the 95% confidence bounds are
considered:•In all five seasons, the vehicle’s fuel consumption increased when pulling the empty
trailer while driven at 100 km/h over asphalt roads, compared to concrete roads.
These differences ranged between 0.4 L/100 km to 0.7 L/100km (1.0% to 1.8%). All ofthese values were calculated to be statistically significant. There was a 0.2 L/100 km(0.4%) increase in fuel consumption on asphalt when compared to concrete in
Summer Night data, however, these data were not statistically significant (Figure 4-6).

•

In all five seasons, the vehicle’s fuel consumption increased when pulling the full trailer
while driven at 100 km/h over asphalt roads, compared to concrete roads. Theses
differences ranged between 0.4 L/100 km to 0.7 L/100 km (0.8% to 1.6%). There wasa 0.1 L/100 km (0.4%) increase in fuel consumption on asphalt when compared toconcrete in Summer Night data, however, these data were not statistically significant(Figure 4-7).

•

In four of the five seasons, the vehicle’s fuel consumption increased when pulling theempty trailer while driven at 100 km/h over composite roads, compared to concreteroads. This increase ranged between 0.2 L/100km to 1.5 L/100km (1.0% to 3.1%).
However, the difference for summer day data was roughly -0.5 L/100km (-1.5%),
indicating a decrease in fuel consumption on composite roads, when compared toconcrete. All these differences were found to be statistically significant with the
exception of the Spring data which was not significant (Figure 4-8).

•

In four of the five seasons, the vehicle’s fuel consumption increased when pulling thefull trailer while driven at 100 km/h over composite roads, compared to concrete roads.
This increase ranged between 0.4 L/100 km to 1.2 L/100 km (0.8% to 2.6%).
However, the difference for summer day data was roughly -0.5 L/100 km (-1.3%),
indicating a decrease in fuel consumption on composite when compared to concrete.
All these differences were found to be statistically significant with the exception of theSpring data which was not significant (Figure 4-9).

•

The multi regression analysis models (winter, spring, summer day/night, fall and allseason) all have positive asphalt pavement coefficient values, indicating lower fuel42

CSTT-HVC-TR-068
consumption on concrete pavement compared to asphalt pavement. In addition, all
but one composite pavement coefficient values are positive.
•

The fuel savings for the empty trailer at 60 km/h when comparing concrete toasphalt roads ranged from 0.4 L/100km to 0.5 L/100km (1.7% to 3.9%) in favour ofconcrete and were all statistically significant in four of the five seasons. The fuelsavings for the Summer Night data were 0.1 L/100 km (0.5%) but they were notstatistically significant. Figure 4-12.

•

The fuel savings for the full trailer at 60 km/h when comparing concrete to asphalt
roads ranged from 0.2 L/100km to 0.4 L/100km (1.3% to 3.0%) in favour ofconcrete and were all statistically significant. The fuel savings for the SummerNight data were 0.1 L/100 km (0.5%) but they were not statistically significant.
Figure 4-13.

•

The fuel savings for the empty trailer at 60 km/h in four of the five seasons whencomparing concrete to composite roads ranged from 1.1 L/100km to 1.9 L/100km(2.0% to 6.0%), in favour of concrete. However, the summer day data indicated asavings in favour of composite, when compared to concrete, of 0.2 L/100 km(3.0%). All of these savings were statistically significant. Figure 4-14.

•

The fuel savings for the full trailer at 60 km/h in four of the five seasons whencomparing concrete to composite roads ranged from 0.6 L/100km to 1.4 L/100km(1.9% to 4.1%) in favour of concrete. However, the summer day data indicated asavings in favour of composite, when compared to concrete, of 0.2 L/100 km(2.4%). All of these savings were statistically significant except the Spring data.
Figure 4-15.

43

CSTT-HVC-TR-068

5 COAST DOWN TESTS
5.1

Coastdown Procedure

The use of vehicle coastdown tests allows for the direct observation and measurement of
the drag forces on a rolling vehicle. The basic method is defined by the Society ofAutomotive Engineers (SAE) test method standard SAE J2263 -- Road Load
Measurement Using On-Board Anemometry and Coastdown.
The SAE test methods are aimed at measuring all the road forces that affect a vehicle,
including aerodynamic and rolling drag using a “batch” test file – i.e. the starting speed andend speed and starting time and end time and collect one data set per coastdown test.
The tests are required to be repeated at least 10 tmes in both directions to allow for alarge enough data set for statistical analysis and to compensate for any relative windinfluences. Also, the test method involve a high-speed coastdown portion as well as a lowspeed coastdown. The test method requires a starting speed of 125 km/h (for the aerodrag coefficient) and the lowest end speed to be below 15 km/h (for the rolling dragcoefficient).
As this research program was only interested in assessing the change in rolling drag and
CSTT had access to both realtime engine/vehicle information, wind speed and the exact
grade and curvature of the roadway, the study team developed a modified test protocol
aimed at measuring only a low-speed coastdown and extracting drag-coefficient-related
data from the collected data. The principal advantage in performing low-speed
coastdowns was that it required less lineal length of roadway and thus it was operationallymuch easier, safer and faster. Further, with the availablity of continuous data (2-3 Hz.) alarge number of data points during one coastdown trial were available – unlike thestandard SAE method that produces one data point per coastdown. Also, because theroad grade and curvature as well as instantaneous wind speeds were avaialble, these
could be treated as independent variables in a multiple regression analysis. Finally, whilebi-directional tests were conducted on two of the sites (1 e/w and 5 e/w) these tests werein fact on opposing sections of roadway (the alternate lane) and thus the road grades andunderlying road structures were not necessarily the same. Thus, each test site wasanalyzed separately.
The coastdown tests required the following steps:
1. The vehicle was loaded to the test condition (49,400 kg GVW) and all tires checked for
condition and pressure.
2. The vehicle was driven at least one hour to fully warmup the drivetrain, tires and wheel
bearings.
3. The vehicle speed was reduced to approximately 30 km/h at the start of the test
section and the clutch disengaged (all windows were closed and brake fully
disengaged). The vehicle and wind data logging was started.
4. The vehicle was then allowed to freely decelerate to approximately 10 km/h or until theend of the test section was reached.
5. Coastdowns were repeated a minimum of three times and in both directions, where
possible. Bi-directional testing was conducted on opposing East and West boundlanes as it was impossible to close roads and perform bi-directional testing on one sideof the highway.

44

CSTT-HVC-TR-068

6. The average ambient wind conditions were required to be less than 10 km/h, and no
precipitation was allowed.
7. A minimum of two complete tests were completed on each test section.
Because of the extremely slow speed that the vehicle travels on the high-speed highway(where the speed limit was 100 km/h), significant safety measures had to be employedduring the testing. This involved the notification of the local provincial police and the hiringof two safety trucks which were equipped with collision bumpers and large flashingdirectional signs which travelled behind the test truck by some 200 to 300m. This
procedure appeared to be effective in moving traffic around the test convoy although there
were instances of vehicles moving into the passing lane as they approached the warning
trucks and moving back into the right hand lane between the warning trucks and the test
vehicle.
5.2

Test Sites

A total of six test sections of roadway were used for the coastdown testing. They
represented three asphalt sections (Site 1 - Highway 417 East and West near Carp, site
11 -- Highway 417 near Casselman), one concrete (Site 2 - Highway 417 East of
Casselman) and two composite sections (Site 5 - Highway 401 East and West near
Prescott). For each pavement test section, a detailed analysis of the road gradientthrough the section was undertaken prior to the tests and specific portions selected which
exhibited the most level conditions possible. The longitudinal elevations of the sections
are graphically portrayed in the following figures.
Site 1 - Highway 417 West (KP 158 -162)
3.5
3.0
2.5
2.0

Percent Grade

1.5
1.0

Test Section

0.5
0.0
-0.5

Coastdown
start at 156 at 30 km/h

-1.0
-1.5
-2.0
ASPHALT-2.5
164

Figure 5-1 Site 1 West Coastdown Test Area

163

162

161

160

159

158

157

156

155

154

Kilometre Post (km)45

CSTT-HVC-TR-068

Site 1 Highway 417 East (162-158.2)
2.0
Coastdown start at 158.7 andthen again at 157.1. On final run
could do one or more after 156
and before curve

1.5
1.0

Highway 49 Interchage
Test Section

0.5

-0.5
-1.0
-1.5
-2.0
-2.5
-3.0
ASPHALT
-3.5
153

153.5

154

154.5

155

155.5

156

156.5

157

157.5

158

158.5

159

159.5

160

160.5

161

161.5

162

162.5

163

163.5

164

164.5

165

165.5

166

Horizontal Distance (km)Figure 5-2 Site 1 East Test Areas
80

350
Overpass
Start at 64
end before
sight curve

300

Start at ~61.5
after small
hill end
before sight
curve

250

Start at 59
end before
sight curve May be able
to do two
tests?

79
78
77

only onepass
required

76
75
74

72
200

71
70
69

150

68
67
66

100

65
64
63

50

62
61
0

60
66

65

64

63

62

61

60

59

58

57

56

55

54

53

52

Kilometer Post
Grade

Heading

Figure 5-3 Site 11 East Coastdown Test Sites

51

50

Elevation (m)Heading (deg)73Percent Grade

0.0

46

CSTT-HVC-TR-068

160

110

140

105

120

100

Start just past 18 at end of curve
Start at 30

80

90

60

85

40

80

20

75

0

70
34

33

32

31

30

29

28

27

26

25

24

23

22

21

20

19

18

17

16

15

14

13

12

11

10

9

8

Horizontal Distance (km)
Grade

Curve

Figure 5-4 Site 2 East Coastdown Test Areas
Site 5 - Highway 401 West
1.5

96

1.0

94
start 751

start 749.9

0.5

92

0.0

90

-0.5

88

-1.0

86

-1.5

84

COMPOSI TE ( ASPH AL T OVER CONCRETE)
-2.0

82
738

739

Figure 5-5 Site 5 West Coastdown Test Areas

740

741

742

743

744

745

746

747

748

749

750

751

752

753

754

755

756

757

758

Kilometre Post

Elevation (m)start 757.2

Elevation (m)95Grade (%)

Heading (deg)Start at 14 at start of curve
100

47

CSTT-HVC-TR-068

Site 5 - Highway 401 East
1.5

96

1.0
Start at 749

94

Start at 756

0.5

0.0
90
-0.5
88

Ov erpassturn around-1.0

Elevation (m)Percent Grade

92

86
-1.5

84

-2.0
COMPOSI TE ( ASPH AL T OVER CONCRTETE)
-2.5

82
758

757

756

755

754

753

752

751

750

749

748

747

746

745

744

743

742

741

740

739

738

Kilometre Post
Grade

Elev ation

Figure 5-6 Site 5 East Coastdown Test Areas

5.3

Test Results

The coastdown tests were completed in late August 2003. The testing was undertaken in
the evening so that traffic conditions and winds were the lightest possible. Test conditions
are presented in Table 5-1 and an example set of test data is plotted (speed vs. time) in
Figure 5-7. It can be noted in Figure 5-7, a simple plot of vehicle speed against time, thatthe deceleration rate (the slope is the regression equation first coefficient) exhibited
relatively good repeatability on the six coastdown tests (2 sub-sections per pass and 3
passes). All test sections had high measures of repeatability. A total of 1299 data points
were collected on the 6 sites.
The continuous data were analysed in a multiple regression model which included
variables for the time, instantaneous wind speed, instantaneous grade and instantaneous
curvature. The analysis indicated (Table 5-2) that the most significant explanatory factor
was simply the time (deceleration rate). The other factors were all significant explanatory
variables on some of the sites (all the variables were statistically significant on at least oneof the sites) but all the additional variables together only improved the over coefficient ofdetermination (R2) by 2-3% with 88 to 94% of the variation being explained by the time
variable.

48

CSTT-HVC-TR-068
Table 5-1 Coastdown Test Conditions

Test Conditions

Site 1E Site 1W
Asphalt Asphalt

Date
Ambient Temperature (C )Pavement Temperature (C )Average Ambient Wind (km/h)

28/8/03
17.8
20
-4.5

28/8/03
17.8
20
+2.7

Site
11E
Asphalt
28/8/03
11.20
14
-0.6

Site
2E
Concrete
28/8/03
11
12.8
-1.4

Site 5E
Composite
28/8/03
12.4
13.8
-0.5

Site 5W
Composite
28/8/03
12.4
13.8
+0.4

Site 11 - Highway 417 E - Asphalt
1098

Speed (m/s)

7
6
5
4
3
2
1
0
0

20

40

60

80

100

120

140

Time (s)Site 11E1
Linear (Site 11E2)

Site 11E2
Linear (Site 11E1)

Site 11E3
Linear (Site 11E3)

Site 11E4
Linear (Site 11E4)

Site 11E5
Linear (Site 11E5)

Figure 5-7 Coastdown Speed vs. Time at Site 11 East - Asphalt
Looking just at the acceleration measure (time), which is the measure of the rolling force
differences between the pavements, there is not consistent difference between theconcrete and other pavements (see Table 5-2 and Figure 5-8). Between the concrete andcomposite pavements there are no consistent differences with one composite coefficienthigher (-0.055) and the other lower (-0.051) than the concrete coefficient (-0.054). The
three asphalt sections exhibit substantially wider spreads in their coefficients (-0.046 to –0.64) again straddling the concrete value. The higher variation in the 1E/W test site may inpart be due to the differences in the pavement structure (the East lane was part of theoriginal highway while the West lane was new construction when built).
.

49

CSTT-HVC-TR-068

Table 5-2 Regression Equation Summary
Statistical
Coefficients

1E 417Asphalt3

Number of Runs

Test Site and Pavement Type
1W 417
11E 417
2E 417
5E 401
5W 401Asphalt
Asphalt
Concrete Composite Composite
3
3
2
2
2

Intercept

7.71

8.82

8.42

8.70

8.82

8.67

Time (s)-0.046-0.064-0.056-0.054-0.051-0.055Wind Speed (km/h)-0.0740.003-0.052-0.016-0.0180.000% Grade-0.119

1.762

0.403

0.426

-0.282

0.275% Curve

0.089

0.045

-0.099

0.559

0.035

0.006

Speed=a+b*Time+c*Wind+d*%Grade+e*%curve
Observations

304

148

176

219

232

220

2

0.897

0.960

0.973

0.922

0.938

0.950
0.422

R

Standard Error

0.597

0.376

0.317

0.552

0.479

Adjusted R

0.895

0.959

0.972

0.921

0.937

0.949

Multiple R

0.947

0.980

0.986

0.960

0.969

0.975

2

0.07

0.06

Average Decceleration (m/s^2)

0.05

0.04

0.03

0.02

0.01

0
1E 417 Asphalt

1W 417 Asphalt

11E 417 Asphalt

2E 417 Concrete

5E 401 Composite 5W 401 Composite

Test Site and Pavement Type

Figure 5-8 Coastdown Average Decceleration Rates

50

5.4

CSTT-HVC-TR-068

Coastdown Findings

The following findings are made based on the tractor trailer coastdown test data:•

The coastdown tests were successfully completed and met all the test conditions
required.

•

Excellent repeatability of the tests was achieved.

•

The test results were analysed by means of a multiple regression model which allowed
for the estimation of each independent variable’s (rolling resistance (time), wind speed,
grade, and curvature) coefficient to be measured. Comparing the rolling resistance(time) coefficient of concrete to the other pavements does not indicate any consistentdifferences between pavement types.

51

CSTT-HVC-TR-068

6 PASSENGER CAR TESTS
6.1

Test Method

Measurement of the impact of pavement differences on a passenger car’s fuelconsumption were undertaken following similar test criteria and procedures as those usedin the truck test program. The vehicle was equipped with a communication cableconnected to the onboard diagnostic (OBDII) engine communication system on the vehicle
with laptop computer recording the information. The test sites were the same as thoseused in the truck program and the test conditions included winter and summer temperature
ranges. Note that the sample rate for these data was approximately three times thatavailable for the truck testing.
The winter car tests were performed after the winter truck test program had been
completed in early March 2003. The pavement temperature conditions ranged from -11 to+5oC. The summer tests were undertaken after the summer truck test program wascompleted in September 2003. The pavement temperature conditions for the summer
tests ranged from +20 to +34 oC. All the other test condition criteria were met during thetests.
The data were processed in a similar manner to the truck data with the raw data being
merged into meta-files and then these files being processed using the Minitab statisticalanalysis program. The model form used was the same as that used in the truck modelwith the exception that there was no variation in load in the test data. As with the truck
analysis, the results were based on the model, which included an IRI ranging from 0 to 2.
6.2

Data Analysis

6.2.1 Car Winter Model
The data set for the winter testing contained 28,517 observations. The regression
modeling resulted in the following regression equation:
FuelCon = 12.6 + 0.285 Pvash - 0.227 Pvcomp - 0.0417 IRI + 2.03 Grade - 0.0607
Pavetemp - 0.0509 Speed +0.000202 AirSpdSq
As shown in the following table, all the variables were measured as being significant to
explaining the said data. However, overall the coefficient of determination (R- Sq) is only
47.9%, which is acceptable but significantly lower than that obtained for the truck testing
data.

52

CSTT-HVC-TR-068
Table 6-1 Car Winter Model Coefficients
Predictor
Coef
SE Coeft-testConstant
12.6184
0.0535235.94Pvash
0.28465
0.013920.48
Pvcomp-0.22678
0.02035-11.14IRI-0.04169
0.02029-2.06
Grade
2.029760.01453
139.74
Pavetemp-0.06071
0.003584-16.94Speed-0.05092
0.00087-58.51
AirSpdSq
0.000202
5.06E-06
39.86
Number of Observations 28,517 R2 = 47.9%

P-test
0
0
0
0.04
0
0
0
0

6.2.2 Car Summer
The car Summer testing program collected essentially the same number of observations(28,913) as the winter data set. Again, the same model formulation was used as for the
winter data and resulted in the following equational form:
FuelCon = 14.2 - 0.0263 Pvash + 0.125 Pvcomp - 0.0772 IRI + 1.78 Grade - 0.0462
Pavetemp - 0.0744 Speed +0.000252 AirSpdSq
Table 6-2 presents the statistical indicators for each variable and again the significance of
all the variables is high. The overall explanatory power of the model is almost identical to
the Winter model.
Table 6-2 Summer Car Model Coefficients
Predictor Coef
SE Coeft-testConstant 14.2340
0.0720197.69Pvash-0.02632
0.01313-0.20
Pvcomp0.125010.02097
5.96
IRI-0.07724
0.02056-3.76
Grade
1.779610.01477
120.49Pavetemp -0.046154
0.002231-20.69Speed-0.0743939 0.0007668-97.02
AirSpdSq 0.00025224 0.00000398 63.38
Number of observations 28,913 R2=48.3%

6.3

P-test
0.000
0.045
0.000
0.000
0.000
0.000
0.000
0.000

Car Data Point Estimates

Each table below gives point estimates, as well as 95% and 99% confidence bounds for
expected fuel consumption for cars on smooth road surfaces. Increases in expected fuelconsumption for asphalt and composite surfaces relative to concrete are also given. All
calculations are based on an IRI of 1.0, a vehicle speed of 100 km/hr, an air speed of 100
km/hr (assuming a wind speed of 0 km/hr), and a grade of 0.
For the winter point estimates, ambient temperatures of -10, -5, and 0 degrees Celsius are
assumed. And for the summer model, temperatures of 20, 25, and 30 degrees Celsius are
assumed. The resulting point estimates are provided along with their 95th percentile

53

CSTT-HVC-TR-068

confidence bounds (once again calculated as a combined 97.5% confidence as explainedin Section 4.5). Also the percentage change from concrete pavement for both the asphalt
and composite pavements are calculated for each test condition plus their confidencebounds. The estimates are presented in Tables 6-3 and 6-4. The data is presented
graphically in Figure 6-1 and Figure 6-2.

Table 6-3 Car Winter Point Estimates, 100 km/h
Pave
Temp

Surface

Estimate

95
Lower

95
Upper

97.5
Lower

97.5
Upper

0
-5
-10
0
-5
-10
0
-5
-10

Asphalt
Asphalt
Asphalt
Composite
Composite
Composite
Concrete
Concrete
Concrete

9.8
10.1
10.4
9.3
9.6
9.9
9.5
9.8
10.1

9.8
10.1
10.3
9.3
9.6
9.8
9.5
9.8
10.0

9.8
10.1
10.5
9.3
9.6
10.0
9.5
9.8
10.2

9.8
10.1
10.3
9.3
9.6
9.8
9.5
9.8
10.0

9.8
10.1
10.5
9.3
9.6
10.0
9.5
9.8
10.2

Absolute
Diff (low)L/100km
0.2
0.2
0.2
-0.3
-0.3
-0.3

Absolute
Diff (High)
L/100km
0.3
0.3
0.3
-0.2
-0.2
-0.2

Absolute
Diff (low)L/100km
-0.1
-0.1
-0.1
0.1
0.1
0.1

Absolute
Diff (High)
L/100km
0.0
0.0
0.0
0.2
0.2
0.2

Percent
Diff (low)Percent
Diff (high)

2.7%
2.6%
2.5%
-2.9%
-2.8%
-2.7%

3.3%
3.2%
3.2%
-1.9%
-1.9%
-1.8%

Percent
Diff (low)Percent
Diff (high)

-0.7%
-0.7%
-0.7%
1.0%
1.0%
0.9%

0.0%
0.0%
0.0%
2.2%
2.1%
2.1%

Table 6-4 Car Summer Point Estimates, 100 km/h
Pave
Temp

Surface

Estimate

95
Lower

95
Upper

97.5
Lower

97.5
Upper

30
25
20
30
25
20
30
25
20

Asphalt
Asphalt
Asphalt
Composite
Composite
Composite
Concrete
Concrete
Concrete

7.8
8.1
8.3
8.0
8.2
8.4
7.9
8.1
8.3

7.7
8.1
8.2
7.9
8.2
8.3
7.8
8.0
8.3

7.9
8.1
8.4
8.1
8.2
8.5
7.9
8.1
8.4

7.7
8.1
8.2
7.9
8.2
8.3
7.8
8.1
8.2

7.9
8.1
8.4
8.1
8.2
8.5
8.0
8.1
8.4

Shaded cells represent data that was not statistically significant

54

CSTT-HVC-TR-068

10.5
Winter Model

Estimated Fuel Consumption Rate (L/100 km)

10

9.5

9

Assumptions:
Road Speed = 100 km/h
Grade = 0
IRI = 1
Relative Wind = 0 km/h

8.5

Summer Model

8

7.5
-15

-10

-5

0

5

10

15

20

25

30

35

Test Temperature
Asphalt

Composite

Concrete

Figure 6-1 Car Point Estimates (-10 to 30 C)

Percentage Difference from Concrete Fuel Consumption Rate (%)

4.0%

3.0%

2.0%

1.0%

0.0%

-1.0%

-2.0%

-3.0%

-4.0%
-15

-10

-5

0

5

10

15

20

25

30

Ambient Temperature (deg C)
Asphalt

Composite

Figure 6-2 Car Percentage Change Estimates vs. Concrete, 100 km/h

35

CSTT-HVC-TR-068

55

From these model estimates the following conclusions can be made regarding thepassenger vehicle testing:•

The point estimates for both the summer and winter models show excellent continuity
and illustrate essentially the same linear relationship in both temperature ranges
tested.

•All models have negative coefficients for pavement and ambient temperature and thus
as the temperature increases the fuel consumption decreases.

•

As illustrated in Figure 6-1, the winter model estimates that both the asphalt and thecomposite pavements are statistically different in energy requirements than theconcrete.

•

In the winter model, as shown in Figure 6-2, the composite pavements decreased thefuel requirements at –5C by 0.2 L/100km (2.1%) while the asphalt surfaces required an
additional 0.3 L/100km (2.9%) fuel from the vehicle when compared to concrete. Bothof these are statistically significant differences at the 95th percentile level;•

In the summer model (Figure 6-2), there is a 0.05 L/100km (0.3%) reduction in fuelconsumption on asphalt when compared to concrete, however, this difference is notstatistically significant.

•At 25 deg C there was a 0.1 L/100km (1.4%) increase in fuel consumption whendriving over the composite pavements when compared to concrete roads. Thisdifference was found to be statistically significant.

56

CSTT-HVC-TR-068

7 COMPARISON BETWEEN PHASES II AND III
7.1

PurposeUpon delivery of a Draft Final report in January 2004, it became clear that the conclusions
based on Phase III data differed from those of Phase II. NRCan and the Cement
Association of Canada felt that this warranted additional analysis to resolve the basis forthe differences. NRC/CSTT and its project partners were engaged to determine if thedifference lay in the data, the models or the analyses. Although not part of the original
scope of work, this Section attempts to explain the differences between the two Phases.
The following factors could have contributed to the differences:•••

Poor data collection in either or both of the Phases (Section 6.2);
Incorrect Model and/or analysis (Section 6.3);
Different test conditions, parameters or road courses (Section 6.3).

In order to determine which of these factors caused the differences it was necessary to reanalyse the way in which the data were collected, review the methods that were used toanalyse the data and review the various test conditions. In addition, it was felt that running
the Phase II data in the Phase III model and running the Phase III data in the Phase IImodel could also shed some light on the issue.
7.2

Possibility of poor data collection in either of both of the phases

NRC/CSTT reviewed the way in which the data were collected and the parameters thatwere used to reject or accept data into the models. After considerable review CSTT/NRC
has concluded that the raw data, the methodology used to gather the data and the filtering
used to reject ‘out of range‘ data were not flawed. Although the roads and vehicles weredifferent, the methodology did not vary significantly from previous phases.
Additionally, CSTT/NRC confirmed the sampling rate provided by the data acquisition
system, performed a post test calibration of the wind speed anemometer and confirmedthat the pre-determined weather parameters had not been exceeded.
7.3

Comparative Analysis of the Tanker semi-trailer (Phase II) and Van Semi-trailer(Phase III) Data

This section presents a comparison of the two most recent sets of semi-trailer test data,
analyzed in two different ways. The first is the use of the regression models that wereused in the Phase II rework analysis while the second uses the regression model
developed in Phase III.
The total test program aimed at assessing the effect of pavement structure on vehicle fuelconsumption was collected in two large sets of test data. The first set collected in Phase II
was documented in a report entitled “Analysis of the Effect of Pavement Structure of Truck
Fuel Consumption ” and statistically re-analyzed in the Phase II rework report titled“Additional Analysis of the Effect of Pavement Structure on Truck Fuel Consumption”.

CSTT-HVC-TR-068

57

These data were based on the use of tanker variants of a straight truck, semi-trailer andB-train vehicle configurations. In the Phase III set of data, a van style semi-trailer wasused for the tests. Pavement test sites were selected in the Phase III tests on similar
criteria and in many cases contained the same road sections as those in the Phase II test
program but the Phase II sites had a wider range of pavement roughness such that theeffect of roughness could be more discreetly measured.
7.3.1 Phase II Model
The Phase II data were collected on similar pavement types, but not necessarily the same
locations as the Phase III tests. The vehicle used in the Phase II model analysis was atanker semi-trailer which had a lower drag coefficient and was expected to minimize theeffects of the aerodynamic drag forces and thus maximize the relative size and influenceof the rolling forces.
The Phase II re-work multiple regression models were developed in such a way that fueleconomy was specified to be a function of pavement structure, load, air temperature,
pavement temperature, vehicle speed, wind speed, IRI, grade, and various interactions
among these variables. Pavement structure was represented in the model by two indicatorvariables. The first took on a value of 1 for asphalt and 0 otherwise; the other a value of 1
for composite and 0 otherwise. Thus, concrete pavement was defined as the basecategory road type structure. Vehicle speed was also reflected by two indicator variables,with 60 km/hr set as the base category and 75 km/h and 100 km/h being discrete values.
The results of this investigation suggested that the relative effects on fuel economy of air
temperature, wind speed, and numerous variable interactions were small when comparedto the remaining variables listed above. Thus, the model arising from the analysis was
FUELCON(L/100km) = β0 + β1*PVASH + β2*PVCOMP + β3*LOAD + β4*PAVETEMP +β5*IRI + β6*GRADE + β7*SPEED75 + β8*SPEED100

7.3.2 Phase III Model
The multiple regression analysis of the Phase III van semi-trailer data were undertaken ina slightly different manner than the Phase II model. This was due to an interest in having
more engineering physics basis to the model form in which the speed terms would be
included as continuous variables including measures of distance (1/v)1, speed (v) andaerodynamic force (v2). The pavement types were still used as indicator values.
FUELCON(L/100km) = β0 + β1*PVASH + β2*PVCOMP + β3*LOAD + β4*PAVETEMP +β5*IRI + β6*GRADE + β7*SPEED + β8*AIRSPDSQ + β9*INVSPD
1

Note that the comparative review model was modified slightly from the original Phase III model with theaddition of the inverse of speed term. This was suggested in the external technical audit report bySypher:Mueller International (A Review of the Report on Effects of Pavement Structure on Vehicle Fuel
Consumption – Phase III, SYPHER:MUELLER International Inc., October 2004). The addition of the term
improved the overall coefficient of determination by less than 1%.

58

CSTT-HVC-TR-068

7.3.3 Comparison of the Models and Data Sets
The analysis of the data was undertaken with the regression software package Minitab.
Each dataset was run twice through the model, once using the Phase II model formulationand then with the Phase III formulation. In addition, two ranges for the IRI (internationalroughness index) were used within each data set. The first restricted IRI to less than 1.2
while the second set contained IRI values between 1.2 and 1.6. Although some of thedata collected for Phase III were greater than 1.6, these values were rejected from thePhase III mathematical model as they were outside the pre-determined limits. As in
Section 4.4, Tables 7-1 through 7-8 have been generated to display all the coefficientsfrom the various models. These values were then used to generate the data presented inTables 7-9 and 7-10.
7.3.4

Phase II Model Fit to Phase III Van Semi-trailer Data

The Phase II model, based on IRI less than 1.2, was developed using 46,624
observations. The regression equation estimated was:FuelCon = 12.8 + 0.488 Pvash + 0.654 Pvcomp + 0.000091 Load - 0.0968 Pavetemp +0.500 IRI + 14.9 Grade + 8.81 Spd75 + 22.4 Spd100
A coefficient of determination (R2) for the model of 71.8% was obtained with all thevariables statistically significant.
Table 7-1 Phase II Model with Phase III data, IRI Less Than 1.2
Predictor
Coef
SE Coef
Constant
12.78510.2466Pvash
0.48776
0.07594
Pvcomp0.65410.1129Load
0.00009065 0.00000109
Pavetemp-0.096766
0.002039
IRI
0.49990.2125Grade
14.8676
0.0831
Spd75
8.80955
0.07459
Spd10022.42380.0809R2 = 71.8%, n=46,624 observations

tratio
51.84
6.42
5.79
83.21
-47.452.35
178.88118.11
277.27

Pvalue
0.000
0.000
0.000
0.000
0.000
0.019
0.000
0.000
0.000

The IRI data between 1.2 and l.6 contained 27,033 observations and estimated thefollowing equation which had a R2 of 67.7%.
FuelCon = 13.1 + 0.711 Pvash - 0.402 Pvcomp + 0.000089 Load - 0.0774 Pavetemp +0.088 IRI + 15.0 Grade + 9.11 Spd75 + 22.5 Spd100

59

CSTT-HVC-TR-068

Table 7-2 Phase II Model with Phase III data, IRI between 1.2 And 1.6
Predictor
Coef
SE Coef
Constant
13.1375
0.5983Pvash
0.71080.1032
Pvcomp-0.40240.2548Load
0.00008870 0.00000162
Pavetemp-0.077372
0.003049
IRI
0.08790.4260Grade
14.96460.1199Spd75
9.10750.1125
Spd10022.5353
0.1166
R2 = 67.7%, n=27,033 observations

7.3.5

tratio
21.96
6.89
-1.58
54.82
-25.370.21
124.8580.93
193.29Pvalue
0.000
0.000
0.114
0.000
0.000
0.837
0.000
0.000
0.000

Phase III Model Fit to Van Semi-trailer Data

The Phase III model results, while providing different coefficients for some common
variables between the models, had very similar R2 to the Phase II model.
For IRI less than 1.2 (46,624 observations), the regression equation was:FuelCon = - 68.5 + 0.469 Pvash + 0.267 Pvcomp + 0.000093 Load - 0.0968 Pavetemp +0.532 IRI + 15.0 Grade + 0.713 Speed + 0.00130 AirSpdSq + 2068 InvSpd
Table 7-3 Phase III Model with Phase III data, IRI Less Than 1.2
Predictor
Coef
SE Coef
Constant-68.482
2.307
Pvash
0.46883
0.07604
Pvcomp0.26730.1134Load
0.00009252 0.00000109
Pavetemp-0.096841
0.002040
IRI
0.53220.2126Grade
15.0491
0.0831
Speed
0.71286
0.01651
AirSpdSq
0.00129913 0.00003763
InvSpd2067.57
84.42
R2 = 71.8%, n=46,624 observations

tratio-29.69
6.17
2.36
84.81
-47.472.50
181.05
43.18
34.53
24.49

Pvalue
0.000
0.000
0.018
0.000
0.000
0.012
0.000
0.000
0.000
0.000

The data with an IRI of at least 1.2 and less than 1.6 (27,033 observations) provided aregression equation of
FuelCon = - 58.6 + 0.772 Pvash + 0.429 Pvcomp + 0.000094 Load - 0.0817 Pavetemp +0.080 IRI + 15.0 Grade + 0.629 Speed + 0.00151 AirSpdSq + 1743 InvSpd

60

CSTT-HVC-TR-068
Table 7-4 Phase III Model with Phase III data, IRI between 1.2 And 1.6
Predictor
Coef
SE Coef
Constant-58.598
3.529
Pvash
0.77220.1033
Pvcomp0.42880.2569Load
0.00009382 0.00000163
Pavetemp-0.081705
0.003054
IRI
0.08040.4263Grade
14.97210.1202
Speed
0.629380.02446
AirSpdSq
0.00151453 0.00005304
InvSpd1742.6127.2
R2 = 67.6% , n=27,033 observations

7.3.6

tratio-16.61
7.47
1.67
57.66
-26.750.19
124.56
25.73
28.56
13.70

Pvalue
0.000
0.000
0.095
0.000
0.000
0.850
0.000
0.000
0.000
0.000

Phase II Model Fit to Phase II Tanker Semi-trailer Data

The Phase II data for IRIs less than 1.2 produced a regression equation of:
FuelCon = 17.2 + 1.76 Pvash + 1.18 Pvcomp + 0.000295 Load - 0.156 Pavetemp + 2.33
IRI + 737 Grade + 3.87 Spd75 + 10.3 Spd100
In this case, the R2 is marginally lower (59.5%) than the Phase II model but still quite an
acceptable value given the large number of observations (22,678).
Table 7-5 Phase II Model with Phase II data, IRI Less Than 1.2
Predictor
Coef
SE Coef
Constant
17.1533
0.2362Pvash
1.76370.1072
Pvcomp1.17550.1106Load
0.00029459 0.00000342
Pavetemp-0.155534
0.001903
IRI
2.33260.1887
Grade736.97
14.42
Spd75
3.874590.07080
Spd10010.2833
0.07792
R = 59.6%, n=22,678 observations

tratio
72.63
16.46
10.63
86.02
-81.74
12.36
51.11
54.72
131.94

Pvalue
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000

For road sections with an IRI of at least 1.2 and less than 1.6, the model fit was slightly
lower than for the smoother sections at an R2 of 54.7% and a regression equation of:
FuelCon = 17.9 + 1.80 Pvash + 0.805 Pvcomp + 0.000310 Load - 0.148 Pavetemp + 1.13
IRI + 330 Grade + 4.03 Spd75 + 10.4 Spd100

61

CSTT-HVC-TR-068

Table 7-6 Phase II Model with Phase II data, IRI Between 1.2 And 1.6
Predictor
Coef
SE Coef
Constant
17.87960.5553Pvash
1.79730.1175
Pvcomp0.80540.1192Load
0.00031042
0.00000482
Pavetemp-0.148435
0.002545
IRI
1.12500.3874Grade329.8617.20
Spd75
4.034120.09936
Spd10010.3752
0.1066
R2 = 54.7%, n=14,772 observations

7.3.7

tratio
32.20
15.30
6.76
64.36
-58.32
2.90
19.17
40.60
97.29

Pvalue
0.000
0.000
0.000
0.000
0.000
0.004
0.000
0.000
0.000

Phase III Model Fit To Phase II Tanker Semi-trailer Data

The 2003 model, for IRI ranges less than 1.2, had a very similar R2 to the Phase II modelof 58.5%. The regression equation was:FuelCon = - 29.1 + 1.90 Pvash + 1.15 Pvcomp + 0.000297 Load - 0.155 Pavetemp + 2.37
IRI + 748 Grade + 0.441 Speed + 0.000039 AirSpdSq + 1196 InvSpd

Table 7-7 Phase III Model with Phase II data, IRI Less Than 1.2
Predictor
Coef
SE Coef
Constant-29.057
1.499
Pvash
1.8966
0.1140Pvcomp1.15340.1170Load
0.00029732 0.00000356
Pavetemp-0.154658
0.001979
IRI
2.37440.1964Grade748.1615.05
Speed
0.441430.01198
AirSpdSq
0.00003857 0.00003286
InvSpd1195.96
53.72
2
R = 58.5%, n=22,039 observations

tratio-19.38
16.64
9.86
83.50
-78.13
12.09
49.70
36.84
1.17
22.26

Pvalue

0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.240
0.000

Similarly, test data with IRIs of at least 1.2 and less than 1.6 provided a model with an R2of 53.9%.
FuelCon = - 22.4 + 1.91 Pvash + 0.963 Pvcomp + 0.000311 Load - 0.149 Pavetemp +1.14 IRI + 342 Grade + 0.390 Speed + 0.000177 AirSpdSq + 1001 InvSpd

62

CSTT-HVC-TR-068
Table 7-8 Phase III Model with Phase II data, IRI between 1.2 And 1.6
Predictor
Coef
SE Coef
Constant-22.397
2.196
Pvash
1.90540.1218
Pvcomp0.96320.1293Load
0.00031061 0.00000492
Pavetemp-0.149181
0.002680
IRI
1.14000.3964Grade341.9517.67
Speed
0.389780.01720
AirSpdSq
0.00017685 0.00005040
InvSpd1000.60
73.98
R2 = 53.9%, n=14,223 observations

7.3.8

tratio-10.20
15.64
7.45
63.16
-55.67
2.88
19.35
22.67
3.51
13.53

Pvalue
0.000
0.000
0.000
0.000
0.000
0.004
0.000
0.000
0.000
0.000

Comparisons

In comparing the Phase II models with the Phase III models, it is clear that bothformulations provide very similar coefficient of determinations. This means that either
model has essentially the same accuracy of prediction. In both models, the predictive
accuracy is higher with the lower range of IRI values.
To more easily compare the two model formulations, point estimates were developed fromeach equation set under a number of standard conditions (temperature =25 deg C,
grade=0, relative wind=0) with the load and speed varying. For the low IRI models, thepoint estimate was made with an IRI value of 1.0 while for the higher IRI model the IRI was
fixed at 1.5. The output of these estimates is provided in Table 7-9 for the van semi trailer
data (Phase III) and in Table 7-10 for the tanker semi trailer data (Phase II).
The absolute differences in fuel consumption from a concrete reference for the asphalt
and composite pavements were calculated as well as the percentage differences. The
statistical significance of the differences were also assessed at the 95th percentile level(non-significant differences are shaded). The tables reveal the following findings:•

The Phase III model consistently predicts a higher fuel consumption rate for all theload, speed and pavement conditions than the Phase II model.

•

For the van semi trailer data (Phase III), the composite pavements were notsignificantly different from the concrete for all load, speed and road roughness
condition, except for the Phase II model estimates, with low IRI values.

•

In all the load, speed and roughness value conditions, the van model (Phase III)estimated that the asphalt pavements produced statistically significantly higher fuelconsumption than the concrete pavements.

•

For the tanker semi trailer models (Phase II), all the differences between concrete andboth asphalt and composite are statistically significant at the 95th percentile withabsolute difference estimates of 1.2 and 1.9 L/100km for the composite and asphalt
pavements respectively.

CSTT-HVC-TR-068•

63

Both models provide constant absolute change estimates, independent of vehicle
speed and load.

For the Phase III van semi trailer data:
•
•
•
•
•

The Phase III model estimates the absolute difference for the low IRI model as 0.3
L/100km for the composite pavement (significant) and 0.5 L/100km (significant) for theasphalt;
The Phase II model estimates significant differences of 0.7 and 0.5 L/100km for thecomposite and asphalt pavements respectively;
For the rougher IRI range at 1.5, the asphalt was predicted to have a 0.8 L/100km
increase and the composite a not-significant 0.4 L/100km increase using the Phase III
model;The Phase II model predicted a –0.4 L/100km (not-significant) change for thecomposite pavement and a 0.7 L/100km (significant) change on the asphalt.
The largest overall percentage difference for the Phase III van data was 5.2%, which
was recorded on rougher roads and with an empty trailer. The lowest percentage
difference was 1.1% which was recorded on smoother roads with the maximum
payload.

For the Phase II tanker data:•••

The absolute difference estimates were higher than the van semi trailer and alldifferences were statistically significant at the 95th percentile level;
With the Phase III model at an IRI of 1.0, a difference of 1.2 and 1.9 L/100km forcomposite and asphalt pavements was estimated while the Phase II tanker model had
estimates of 1.2 and 1.8 L/100km, at an IRI of 1.5;
The Phase III model differences were 1.0 and 1.9 L/100km for composite and asphalt
and using the Phase II tanker model, the differences were 0.8 and 1.8 L/100km forcomposite and asphalt respectively.

The insensitivity of the fuel consumption differences to temperature, load and speed issomewhat counterintuitive to the engineering physical models. As a result, additional
statistical tests were undertaken on the data to assure that the models were not missing
interaction terms which measure the possibility that the relationship between fuelconsumption and pavement type is different for different speeds, or for different loads. In
all cases the inclusion of the interaction terms had no effect on the value of " R2", thecoefficient of determination for the model which measures the overall fit of the model to thedata. The interactive terms are thus very minor compared to the other variables included inthis model.
Additionally, the constant nature of the absolute differences means that as the vehicle’s
fuel consumption rate increases with load and speed, the percentage differences decreaseas the load and speed increase. At 25 deg C on smooth roads (IRI= 1.0), the tanker semi
trailer’s (Phase II) maximum percent differences of 9.2% (1.9 L/100km) and 5.5% (1.2
L/100km) in concrete’s favour with respect to asphalt and composite respectively and wereat 60 km/h in an empty condition. The van semi trailer (Phase III) had its highest percent
difference of 3.2% (0.5 L/100km) in favour of concrete relative to asphalt at the same

64

CSTT-HVC-TR-068

conditions at an IRI value of 1.0. However, the differences relative to composite for IRI
values of 1.5 were not statistically significant.
The smallest percentage changes were calculated at 100 km/h with the maximum legal
load and were 1.1% (0.5 L/100km) for the van semi trailer (Phase III) on asphalt and 4.3%
(1.8 L/100km) and 1.9% (0.8 L/100km) for the tanker semi trailer (Phase II) on asphalt andcomposite pavements respectively. All these differences were statistically significant.
The point estimates for the “full” load (43,660 kg or 96,000 lb.) at a speed of 100 km/h
from the tables are shown graphically in Figures 7-1 through 7-4. These plot the mean
estimate and the 95th percentile confidence bounds for each of the point estimates of fuelconsumption against test temperature. Both the Phase II and the Phase III models
estimates are presented in each graph. The fact that the Phase III model produceshigher absolute fuel consumption estimates than the Phase II model can be seen on thegraphs.

CSTT-HVC-TR-068

65

Table 7-9 Comparison of Phase III data (van) in Phase III and Phase II Models, at 25 deg C
Phase III Data and Phase
Conditions: Temp=25 deg C, Grade=0, Rel Wind=0
III Model
Empty
Typical Full
Maximum
Load
36,000 lb/ 16,329 kg
96,000 lb/ 43,660 kg
109,000 lb/ 49,441 kg
Model Mean Estimate
Absolute
Percentage
Model Mean Estimate
Absolute
Percentage
Model Mean Estimate
Absolute
Percentage(L/100km)
Difference
Difference(L/100km)
Difference
Difference(L/100km)
Difference
Difference
Speed
AspCompCompCom- AspCom- AspCom- Asp- Com- AspComp
Com- Aspom-Con
Concrete
Concrete
Asphalt
Asphalt
Concrete
Asphalt(km/h)
Con
osite
osite
Con
Con
Con
Con
Con
Con Con
Con
osite
Con
Con
IRI=1.0
60

14.9

15.1

15.3

0.3

0.5

1.9%

3.2%

20.4

20.7

20.9

0.3

0.5

1.4%

2.4%

21.6

21.9

22.1

0.3

0.5 1.3%

2.2%

75

21.3

21.6

21.8

0.3

0.5

1.3%

2.3%

26.9

27.1

27.3

0.3

0.5

1.1%

1.8%

28.1

28.3

28.5

0.3

0.5 1.0%

1.7%

100

37.9

38.2

38.4

0.3

0.5

0.7%

1.3%

43.5

43.7

43.9

0.3

0.5

0.6%

1.1%

44.7

44.9

45.1

0.3

0.5 0.6%

1.1%

60

15.1

15.5

15.9

0.4

0.8

2.8%

5.2%

20.7

21.2

21.5

0.4

0.8

2.2%

3.8%

22.0

22.4

22.7

0.4

0.8 2.1%

3.5%

75

21.8

22.2

22.6

0.4

0.8

2.1%

3.6%

27.4

27.9

28.2

0.4

0.8

1.7%

2.9%

28.7

29.1

29.4

0.4

0.8 1.6%

2.7%

100

38.4

38.8

39.1

0.4

0.8

1.1%

2.1%

44.0

44.4

44.8

0.4

0.8

1.1%

1.8%

45.2

45.6

46.0

0.4

0.8 1.0%

1.7%

IRI=1.5

Phase III data and Phase II Model Conditions: Temp=25 deg C, Grade=0, Rel Wind=0
IRI=1.0
60

14.1

14.8

14.6

0.7

0.5

4.8%

3.5%

19.6

20.2

20.1

0.7

0.5

3.4%

2.5%

20.7

21.4

21.2

0.7

0.5 3.2%

2.4%

75

22.9

23.6

23.4

0.7

0.5

2.9%

2.2%

28.4

29.0

28.9

0.7

0.5

2.4%

1.8%

29.6

30.2

30.0

0.7

0.5 2.3%

1.7%

100

36.6

37.2

37.0

0.7

0.5

1.8%

1.4%

42.0

42.6

42.5

0.7

0.5

1.6%

1.2%

43.2

43.8

43.7

0.7

0.5 1.6%

1.2%

60

14.5

14.1

15.2

-0.4

0.7 -2.6%

4.9%

19.9 19.4

20.6

-0.4

0.7 -2.0%

3.7%

22.0

22.4

22.7

-0.4

0.8 -1.7% 3.3%

75

23.6

23.2

24.3

-0.4

0.7 -1.6%

3.0%

29.0 28.6

29.7

-0.4

0.7 -1.4%

2.5%

30.1

29.7

30.8

-0.4

0.7 -1.2% 2.4%

100

37.1

36.7

37.8

-0.4

0.7 -1.1%

1.9%

42.4 42.0

43.1

-0.4

0.7 -0.9%

1.7%

43.5

43.1

44.2

-0.4

0.7 -0.9% 1.7%

IRI=1.5th

Shaded cells indicate non-significant differences at 95 percentile confidence bounds

66

CSTT-HVC-TR-068

Table 7-10 Comparison of Phase II Data (tanker) in Phase II and Phase III Models, at 25 deg C
Phase II data in Phase III
Model
Load

Speed
(km/h)
IRI=1.0

Conditions: Temp=25 deg C, Grade=0, Rel Wind=0

Empty
Typical Full
Maximum
36,000 lb/ 16,329 kg
96,000 lb/ 43,660 kg
109,000 lb/ 49,441 kg
Model Mean
Absolute
Percentage
Model Mean Estimate
Absolute Percentage Model Mean Estimate
Absolute
Percentage
Estimate (L/100 km) Difference
Difference(L/100 km)
Difference Difference(L/100 km)
Difference Difference
Conc CompCom- Asp- ComAspCompCom- Asp- Com- AspCompCom- AspAsphalt
Concrete
Asphalt
Concrete
Asphalt
Com-Conrete osite
Con Con Con
Con
osite
Con Con Con Con
osite
Con Con

AspCon

60

20.9

22.0

22.8

1.2

1.9

5.5%

9.2%

29.0

30.1

30.9

1.2

1.9

4.0%

6.6%

30.7

31.9

32.6

1.2

1.9

3.8%

6.2%

75

23.6

24.7

25.5

1.2

1.9

4.9%

8.1%

31.7

32.8

33.6

1.2

1.9

3.6%

6.0%

33.4

34.6

35.3

1.2

1.9

3.5%

5.7%

100

30.8

32.0

32.7

1.2

1.9

3.8%

6.2%

38.9

40.1

40.8

1.2

1.9

3.0%

4.9%

40.7

41.8

42.5

1.2

1.9

2.7%

4.7%

60

21.4

22.3

23.3

1.0

1.9

4.6%

8.9%

29.8

30.8

31.7

1.0

1.9

3.2%

6.4%

31.7

32.6

33.6

1.0

1.9

3.1%

6.1%

75

24.2

25.2

26.1

1.0

1.9

4.1%

7.9%

32.7

33.7

34.6

1.0

1.9

2.9%

5.8%

34.5

35.5

36.4

1.0

1.9

2.8%

5.6%

100

31.4

32.4

33.3

1.0

1.9

3.1%

6.1%

39.9

40.8

41.8

1.0

1.9

2.4%

4.8%

41.7

42.7

43.6

1.0

1.9

2.4%

4.6%

IRI=1.5

Phase II data in Phase II model Conditions: Temp=25 deg C, Grade=0, Rel Wind=0
IRI=1.0
60

20.4

21.6

22.2

1.2

1.8

5.8%

8.7%

28.4

29.6

30.2

1.2

1.8

4.1%

6.2%

30.2

31.4

31.9

1.2

1.8

3.9%

5.8%

75

24.3

25.5

26.1

1.2

1.8

4.8%

7.3%

32.3

33.5

34.1

1.2

1.8

3.6%

5.5%

34.1

35.2

35.8

1.2

1.8

3.5%

5.2%

100

30.7

31.9

32.5

1.2

1.8

3.8%

5.7%

38.7

39.9

40.5

1.2

1.8

3.0%

4.6%

40.5

41.6

42.2

1.2

1.8

2.9%

4.4%

60

20.9

21.7

22.7

0.8

1.8

3.8%

8.7%

29.4

30.2

31.2

0.8

1.8

2.7%

6.1%

31.2

32.0

33.0

0.8

1.8

2.6%

5.8%

75

25.0

25.8

26.8

0.8

1.8

3.2%

7.3%

33.4

34.2

35.2

0.8

1.8

2.4%

5.4%

35.3

36.1

37.0

0.8

1.8

2.3%

5.1%

100

31.3

32.1

33.1

0.8

1.8

2.6%

5.7%

39.8

40.6

41.6

0.8

1.8

2.0%

4.5%

41.6

42.4

43.4

0.8

1.8

1.9%

4.3%

IRI=1.5th

Shaded cells indicate non-significant differences at 95 percentile confidence bounds

67

CSTT-HVC-TR-068

50
49

Estiamted Fuel Consumption Rate (L/100km)(95th Percentile Confidence Limits)

48
47
46

2003 Model Estimates

45
44

2002 Model Estimates
43
42
41
40

Van Semi-trailer Model Estimates at
Speed=100 km/h
Load=96,000 lb
IRI=1
Rel Wind Speed =0
Grade=0

39
38
37
36
35
-20

-10

0

10

20

30

40

Ambient Temperature (C)Concrete 03

Composite 03

Asphalt 03

Concrete 02

Composite 02

Asphalt 02

Figure 7-1 Van Model Comparisons at 100 km/h and IRI=1.0

50
49
48

Estimated Fuel Consumption Rate (L/100km)(95th Percentile Confidence Limits)

47
46

2003 Model

45
44
43
2002 Model
42
41
40
Van Semi-trailer Model Estimates at
Speed=100 km/h
Load=96,000 lb
IRI=1.5
Rel Wind Speed =0
Grade=0

39
38
37
36
35
-20

-10

0

10

20

30

Ambient Temperature (C)Concrete 03

Composite 03

Asphalt 03

Concrete 02

Composite 02

Asphalt 02

Figure 7-2 Van Model Comparisons at 100 km/h and IRI=1.5

40

68

CSTT-HVC-TR-068
50
49
48

Estiamted Fuel Consumption Rate (L/100km)(95th Percentile Confidence Limits)

47
46
45
44
43
2003 Model Estimates
42
2002 Model Estimates
41
40

Tanker Semi-trailer Model Estimates at
Speed=100 km/h
Load=96,000 lb
IRI=1
Rel Wind Speed =0
Grade=0

39
38
37
36
35
-20

-10

0

10

20

30

40

Ambient Temperature (C)Concrete 03

Composite 03

Asphalt 03

Concrete 02

Composite 02

Asphalt 02

Figure 7-3 Tanker Model Comparions at 100 km/h and IRI=1.0

50
49
48

Estimated Fuel Consumption Rate (L/100km)(95th Percentile Confidence Limits)

47
46
45
44

2003 Model

43

2002 Model

42
41
40

Tanker Semi-trailer Model Estimates at
Speed=100 km/h
Load=96,000 lb
IRI=1.5
Rel Wind Speed =0
Grade=0

39
38
37
36
35
-20

-10

0

10

20

30

Ambient Temperature (C)Concrete 03

Composite 03

Asphalt 03

Concrete 02

Composite 02

Asphalt 02

Figure 7-4 Tanker Model Comparisons at 100 km/h and IRI=1.5

40

69

CSTT-HVC-TR-068

8 SUMMARY OF FINDINGS
From the testing undertaken as part of this study, the following observations are made:8.1

2003 Van Semi-trailer Tests

1. The testing of a tractor/tridem van semi-trailer was successfully completed over a variety oftest sites composed of concrete, asphalt, and composite (concrete with an asphalt top coat)in temperature conditions ranging from –20 deg C to +34 deg C;
2. Given the immense amount of data, the models for each seasonal truck data set and also
the total combined truck data set provided good coefficients of determination with values
between 66% and 78%;
3. The Phase III (van semi-trailer) multiple regression was used to estimate fuel consumption
rates on the various pavements at temperatures from –20 deg C to +35 deg C andassuming 0% grade, IRI =1, relative wind = 0 km/h, and loads of 16,000, 46,500, and49,400kg.
4. At 100 km/h, the results showed a mean percentage difference favouring concrete roads(i.e. reduced fuel consumption) when compared to asphalt pavements. The value of the fuelsavings ranged from 0.8% to 1.8% and these results were all statistically significant with the
exception of the Summer Night data, at 0.4% (Figure 4-4).
5. At 100 km/h, the concrete roads performed better than composite roads in four of the fiveseasons with savings as low as 0.8% and as high as 3.1%. These differences were all
statistically significant with the exception of the spring data. However, at all loading
conditions the composite pavements were found to be 1.5% more fuel efficient than theconcrete roads for summer day conditions. This difference was statistically significant(Figure 4-5).
6. The fuel savings for the empty trailer at 60 km/h when comparing concrete to asphalt roads
ranged from 0.4 L/100km to 0.5 L/100km (1.7% to 3.9%) in favour of concrete and were all
statistically significant in four of the five seasons. The fuel savings for the Summer Nightdata were 0.1 L/100 km (0.5%) but they were not statistically significant. Figure 4-12.
7. The fuel savings for the full trailer at 60 km/h when comparing concrete to asphalt roads
ranged from 0.2 L/100km to 0.4 L/100km (1.3% to 3.0%) in favour of concrete and were all
statistically significant. The fuel savings for the Summer Night data were 0.1 L/100 km(0.5%) but they were not statistically significant. Figure 4-13.
8. The fuel savings for the empty trailer at 60 km/h in four of the five seasons when comparing
concrete to composite roads ranged from 1.1 L/100km to 1.9 L/100km (2.0% to 6.0%), in
favour of concrete. However, the summer day data indicated a savings in favour of

70

CSTT-HVC-TR-068
composite, when compared to concrete, of 0.2 L/100 km (3.0%). All of these savings were
statistically significant. Figure 4-14.

9. The fuel savings for the full trailer at 60 km/h in four of the five seasons when comparing
concrete to composite roads ranged from 0.6 L/100km to 1.4 L/100km (1.9% to 4.1%) in
favour of concrete. However, the summer day data indicated a savings in favour ofcomposite, when compared to concrete, of 0.2 L/100 km (2.4%). All of these savings were
statistically significant except the Spring data. Figure 4-15.
8.2

Coastdown Tests

1. The test results were analysed by means of a multiple regression model which allowed forthe estimation of each independent variable’s (rolling resistance (time), wind speed, grade,and curvature) coefficient to be measured. Comparing the rolling resistance (time)coefficient of concrete to the other pavements does not indicate any consistent differences
between pavement types.
8.3

Passenger Vehicle Tests

1. The point estimates for both the summer and winter models show excellent continuity andillustrate essentially the same linear relationship in both temperature ranges tested.
2. Due to the limited number of data points and seasonal conditions, the results from thepassenger car testing were less conclusive than the tractor and trailer testing.
3. Of the four seasonal car models presented below, three were statistically significant and one
was not (asphalt versus concrete in summer).
4. In winter testing, the passenger car consumed 0.3 L/100 km more (2.9%) on asphalt than on
concrete. These savings were all statistically significant.
5. In winter testing, the car consumed 0.2 L/100 km less fuel (2.3%) on composite pavement
when compared to concrete. These savings were all statistically significant.
6. In summer testing, the passenger car consumed 0.1 L/100 km (1.5%) more fuel oncomposite roads when compared to concrete. These savings were all statisticallysignificant.
7. In summer testing, the passenger car consumed 0.05 L/100 km (0.3%) less fuel on asphalt
roads when compared to concrete. However, these savings were not statistically significant.
8.4

Phase II to Phase III Model Comparison

1. The comparison of the two sets of test data from the Phase III (van semi-trailer) and Phase
II (tanker semi-trailer) was successfully undertaken using two different regression models

CSTT-HVC-TR-068

71

and indicated that the two model formulations produced very similar results in terms of their
explanatory power expressed as the coefficient of determination (R2).
2. The individual variables in each model were all measured to be significant in both data sets.
3. Point estimates made using both models for both vehicle types at a series of standard
conditions indicated that fuel consumption of the vehicle was consistently lower on concrete
than on asphalt or composite pavement types.
4. The absolute differences in fuel consumption of the vehicles driving on asphalt andcomposite pavements respective to concrete pavements were constant with respect to load,
speed and temperature conditions.
5. The fuel consumption rate of both vehicle types on asphalt pavement, relative to concrete,
was statistically significantly higher using both models and in both IRI ranges (less than 1.2,
and 1.2 to1.6).
6. The differences on the asphalt pavement compared to concrete were constant (in absolute
L/100km) with respect to temperature, speed and load and ranged between 0.5 L/100 km(1.1% to 3.5%) for smooth pavements (IRI = 1.0) and 0.8 L/100 km (1.7% to 5.2%) forpavements with an IRI of 1.5 for the Phase III (van semi trailer) data in each of the models(Table 7-9). All these differences were statistically significant and in concrete’s favour.
7. The differences on the asphalt pavement compared to concrete for the Phase II data (tanker
semi-trailer) using the Phase III model (van semi-trailer) were 1.9 L/100 km (4.7 to 9.2%) for
both smooth (IRI = 1.0) and rougher roads (IRI = 1.5). The differences for the Phase II datausing the Phase II model were 1.8 L/100km (4.3% to 8.7%) for both smooth and rougherroads. All these differences were statistically significant and in concrete’s favour (Table 710).
8. The fuel consumption rate on composite pavement, when compared to concrete, wasstatistically significantly higher using both models and in both IRI ranges (lRI = 1.0 and 1.5)
for the Phase II data. The value of this difference was between 0.8 L/100km and 1.2
L/100km (1.9% to 5.8%) (Table 7-10).
9. For the Phase III data (van semi trailer) on smooth roads (IRI = 1.0) the fuel savings were
statistically significant in concrete’s favour relative to composite, and were estimated using
both models to be 0.7 L/100km difference (1.6% to 4.8%) in the Phase II model (tanker
semi-trailer) and 0.3 L/100km in the Phase III model (0.6% to 1.9%). The values on rougherroads (IRI = 1.5) for the Phase III data were not statistically significant in either of the models(Table 7-9).

72

CSTT-HVC-TR-068

73

CSTT-HVC-TR-068

9 CONCLUSIONSThe testing of typical van style heavy goods trailer and passenger car on a variety of pavement
structures including concrete, asphalt and composite (concrete with an asphalt top coat) wascompleted over temperatures representing a complete annual range of operating seasons in
Eastern Canada. From this testing, the following conclusions have been formulated and relate
to the specific sections of roads tested as part of the Phase III programme:
1. The effects of load and rolling resistance in the summer night model appear to be muchless than those seen in the other models. It is therefore likely that no conclusions can be
drawn from the summer night data and corresponding model.
2. The Phase III multi regression analysis models (winter, spring, summer day/night, fall
and all-season) all have positive asphalt pavement coefficient values, indicating lower
fuel consumption on concrete pavement compared to asphalt pavement. In addition, all
but one of the composite pavement coefficient values were positive.
3. At 100 km/h, on smooth roads, fuel consumption reductions were realised on allconcrete roads when compared to asphalt. The savings ranged from 0.4 L/100 km to
0.7 L/100 km (0.8% to 1.8%) when compared to asphalt roads. These savings wererealised for both empty and fully loaded vehicle conditions for four of the five seasons.
All these differences were found to be statistically significant at the 95% level. The
savings during the fifth season, Summer Night, were 0.25 L/100 km (0.4%), however,these data were found to be not statistically significant.
4. When comparing concrete roads to composite roads at 100 km/h, the results showedthat fuel consumption savings ranged from 0.2 L/100 km to 1.5 L/100 km (0.8% to 3.1%)
in favour of concrete. However, under Summer day conditions, less fuel was consumedon the composite roads, as compared to concrete. The value of these savings wasroughly 0.5 L/100 km (1.5%). All composite to concrete comparisons were found to bestatistically significant except the Spring data, which was not statistically significant.
5. The fuel savings for the empty trailer at 60 km/h when comparing concrete to asphalt
roads ranged from 0.4 L/100km to 0.5 L/100km (1.7% to 3.9%) in favour of concrete andwere all statistically significant in four of the five seasons. The fuel savings for theSummer Night data were 0.1 L/100 km (0.5%) but they were not statistically significant.
Figure 4-12.
6. The fuel savings for the full trailer at 60 km/h when comparing concrete to asphalt roads
ranged from 0.2 L/100km to 0.4 L/100km (1.3% to 3.0%) in favour of concrete and were
all statistically significant. The fuel savings for the Summer Night data were 0.1 L/100
km (0.5%) but they were not statistically significant. Figure 4-13.
7. The fuel savings for the empty trailer at 60 km/h in four of the five seasons whencomparing concrete to composite roads ranged from 1.1 L/100km to 1.9 L/100km (2.0%
to 6.0%), in favour of concrete. However, the summer day data indicated a savings in

74

CSTT-HVC-TR-068
favour of composite, when compared to concrete, of 0.2 L/100 km (3.0%). All of thesesavings were statistically significant. Figure 4-14.
8. The fuel savings for the full trailer at 60 km/h in four of the five seasons when comparing
concrete to composite roads ranged from 0.6 L/100km to 1.4 L/100km (1.9% to 4.1%) in
favour of concrete. However, the summer day data indicated a savings in favour ofcomposite, when compared to concrete, of 0.2 L/100 km (2.4%). All of these savings
were statistically significant except the Spring data. Figure 4-15.
9. Different mathematical models were developed for the Phase II and Phase III studies.
The data from both studies (Phase II and Phase III) were analyzed and compared using
both models for the data collected at 25 deg C. For the Phase II data, these analyses
showed statistically significant fuel savings when operating on concrete pavementcompared to asphalt pavement ranging from 4.3% to 9.2%, depending on model used,
IRI range, vehicle speed and weight. It is important to note that these higher percentage
differences between the two data sets were likely affected by the different types of roadsurfaces and not the models. When similarly comparing concrete pavement andcomposite pavement, the savings ranged from 1.9% to 5.8% in favour of concrete onsmooth roads (IRI = 1.0) and were statistically significant.
10. The comparison using the two models for the Phase III (van) data at 25 deg C showedstatistically significant fuel savings when operating on concrete pavement compared toasphalt pavement ranging from 1.1% to 5.2%, depending on model used, IRI range,
vehicle speed and weight.
11. The comparison using the two models for Phase III data showed that the fuelconsumption differences between composite and concrete pavements on rougher roads
were not statistically different. However, the fuel consumption savings for concrete
pavements, when compared to composite, on smoother roads ranged between 0.6%
and 4.8% and were all statistically significant.
12. The predicted fuel savings on concrete, when compared to asphalt and composite, arevery similar when Phase III data is inserted into each of the models. Similarly, thepredicted fuel savings on concrete, when compared to asphalt and composite, are very
similar when Phase II data is inserted into each of the models. However, the predictedfuel savings when comparing Phase II data to Phase III data are not similar. CSTT
therefore concludes that the differences between Phase II and Phase III results stem
primarily from the collected data themselves (i.e. the prevailing road conditions) and not
the mathematical models.

75

CSTT-HVC-TR-068

10 FOLLOW ON WORK
In order to better understand how roads can be built with the aim of reducing greenhouse gas
emissions, CSTT recommends the following possible actions for future analysis:•

An in depth study focussing on International Roughness Index (IRI) to determine theeffects that road surface roughness can play on fuel consumption;•

An analysis of using concrete pavement on sections of roadway that are primarily
exposed to speeds less than 60 km/h, such as intersections or congested urban areas;and•An expanded scope of work to fully understand other differences between asphalt andconcrete pavement surfaces. This would include, but would not be limited to: soundabsorption/reflection, noise absorption/reflection, cost of installation, cost of
maintenance, propensity to cause/sustain aquaplaning.

76

CSTT-HVC-TR-068

11 REFERENCES[1] Effect of Pavement Surface Type on Fuel Consumption, Phase II, CSTT-HVW-CTR-041,
Taylor, Marsh, Oxelgren, August 2000.
[2] Additional Analysis of the Effect of Pavement Structure on Truck Fuel Consumption, Taylor,
GW, July 2002.
[3] Cummins MPG Guide[4] Cummins Engine Vehicle Evaluation, Vehicle Mission Simulation[5] MIT, EL 00 – 001

Appendix A
Falling Weight Deflectometer Summary Table

CSTT-HVC-TR-068

Location
Highway 417, EB,
East of Ottawa
Highway 417, EB,
West of Ottawa
Highway 115, EB,
West ofPeterboroughHighway 115, WB,
West ofPeterboroughHighway 401, WB,
near Upper
Canada Rd.

79

Normalized
Deflection(µm)

Pavement
Type

Concrete
Asphalt
Concrete

Asphalt

Composite

StaticK-value(Mpa/m)Resilient
Modulus
(Mpa)

Fall
2002

Spring2003

Summer
2003

Fall
2002

Spring2003

Summer
2003

Fall
2002

Fall
2002

Spring2003

Summer
2003

Mean
SD
Mean
SD
Mean

63
9
268
60
78

79
14
240
40
69

63
13
190
36
65

193
67

153
58

203
62

121

42,175
9,761
619
144
57,65533,135
10,190650
12554,091

41,061
11,891811
161
23,798

101

112

SD

26

10

10

26

22

23

29,953

14,822

15,506

Mean

231

214

149

44

60

94

1,418

1,198

1,580

SD

42

34

19

10

12

18

400

276

293

Mean

143

141

128

60

103

112

7,050

5,122

6,224

SD

35

27

25

33

45

51

2,603

1,586

1,979

64
15

Spring2003

Pavement
Modulus( Mpa)

81
13

Summer2003

110
21