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PILOT STUDY: PAVEMENT VISUAL CONDITION AND FRICTION AS A
PERFORMANCE MEASURE FOR WINTER OPERATIONS
Nishantha Bandara, Ph.D., P.E.
Department of Civil Engineering
Lawrence Technological University
21000 West Ten Mile Road
Southfield, Michigan 48075
Phone: 248-204-2602
Fax: 248-204-2568
[email protected]
Submission Date: August 1, 2014
Word Count: 3139 (words), 3250 (13 tables and figures), 6389 (Total)
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ABSTRACT
Snow and ice removal and clearing roadways after a winter storm account for major portion of
roadway agencies located in snowy region’s maintenance budget. With the decreasing funds and
increasing demand from the motoring public for mobility, roadway agencies are continuously
looking for new innovative approaches for winter maintenance operations. One of the main focus
of these approaches include performance measures for winter maintenance. This is specifically
important when roadway agencies use contractors to perform winter maintenance tasks.
In this study, two performance measures are studied extensively. These include, visual pavement
condition behind the snow plow and pavement friction behind the snow plow. Visual pavement
condition was observed and recorded into one of five categories while driving behind a snow
plow. Pavement friction behind snow plow is measured using a Continuous Friction Measuring
(CFT) device. Since visual pavement condition provides driver’s perception of the winter driving
condition and friction measurements provide an objective measurement of the safety of the
roadway under winter condition, a correlation of these provides a basic guideline for winter
maintenance and performance measurement of winter maintenance.
During the past winter, multiple data collection cycles were performed behind different type of
snow plows along I-96 in Livingston County. These preliminary data was used to obtain
relationships between winter storm severity, snow plow type and the selected performance
measures.
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INTRODUCTION
According to Federal Highway Administration (FHWA), nearly 70 percent of nation’s roadways
are located in snowy region and approximately 70 percent of the United States population lives in
these snowy areas. Snow and ice reduces friction between vehicle tire and pavement surface,
causing slower speeds and increased risk of vehicle crashes. In average, vehicle speeds in arterial
roads are reduced by 3 to 13 percent while in freeways reduced by 5 to 40 percent during heavy
snow events. Based on 2002-2012 crash data, the 10 year average number of crashes during
different types of winter weather conditions are shown below (1).
Table 1: Winter Weather Annual Average Accident Data in 2002 to 2012
Winter
Weather Total Number
Condition
Crashes
Snow/Sleet
211,188
Icy
154,580
Snow/Slushy
175,233
of Total Injured
58,011
45,133
43,503
Total Fatalities
769
580
572
Roadway agencies spent approximately 20 percent of their maintenance budget for winter
maintenance. Due to rising costs of winter maintenance activities and increased demand for
mobility from the public, roadway agencies constantly looking for better methods and materials
for winter maintenance. One of the main item that roadway agencies are looking at recent times
include using performance measures for winter maintenance. Traditionally roadway agencies used
materials spent, hours spent and labor costs for winter maintenance as agency performance
measures for accounting and reporting purposes. In addition to above input and output based
performance measures, outcome based performance measures provides a true picture of the winter
maintenance activities. Since more and more roadway agencies are using contractors for their
winter maintenance activities, these outcome based performance measures can be easily used for
contractor evaluations.
PERFORMANCE MEASURES FOR WINTER MAINTENANCE
NCHRP Web-Only Document 136, describes three types of performance measures for winter
maintenance; input measures, output measures and outcome measures (2, 6).
Input measures include the resources used for winter maintenance operations such as, equipment,
materials and labor. Most of the roadway agencies keep records of these input measures for
accounting purposes in terms of number of snow plows used for winter operations, volume or
weight of materials used and labor-hours etc. If the agency is using contractors for winter
maintenance operations, these input measures can be directly used as pay items.
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Output measures include physical outputs for the resources that are used in winter maintenance.
These include lane miles per unit of time plowed, truck plowing speed, material application
rates, payments for winterizing etc.
Outcome measures reflect the end result of winter maintenance during and after a winter storm
event typically from the motorist’s perspective. Common types of outcome measures include;
 Visual characteristics of road condition
 Bare pavement regain time
 Roadway friction
 Vehicle speed during winter storms and time to regain normal traffic speed
 Reduction in crashes
Since this paper is based on visual pavement condition and roadway friction under winter
conditions, more details of those measures are given below.
Visual Characteristics of Road Conditions
Visual characteristics of road conditions during various points of winter storm include the
following (3); Centerline bare, Wheel path bare, Loose snow covered (percent area and depth),
Packed snow covered (percent area and depth), Bare (percent area), Thin ice covered (percent
area), Thick ice covered (percent area), Dry, Damp, Slush (percent area and depth, Frost and
Wet. A Pavement Snow and Ice Condition (PSIC) table was developed for the NCHRP report
526 (3) using the above visual characteristics of the roadway with traffic flow and other visual
information of the road to develop a level of service of the road. PSIC helps an agency to
determine the level of maintenance activities related to maintain certain level of service. Some
agencies use pictorial reference templates to compare existing pavement conditions to aid to
observers.
Roadway Friction
The coefficient of friction between vehicle tire and pavement can be increased by winter
maintenance activities such as snow plowing, deicing, anti-icing and sanding of the roadway.
Coefficient of friction between vehicle tires and pavement can be measured using friction testers.
Although in the United States friction testing is not primarily used for winter performance
measures. However, number of European countries and Japan uses them regularly. Friction can
be measured/predicted using three methods; predicting friction using climate, traffic and other
roadway conditions, direct friction measurements using an extra wheel installed on vehicles or
by traction control systems. There are several types of equipment available for friction
measurements; deceleration devices, locked wheel devices, side force devices, fixed slip devices
and variable slip devices (4). NCHRP Web Document 136 (2) lists three operational uses of
friction measuring devices; they can be used to measure quality of winter maintenance
operations, can be used as source of road user information to inform motorists of hazardous
locations and also to determine amount of de-icing materials used on the roadway. Some of the
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problems with current friction measuring practices involve, number of different types of friction
measuring devices available and used by roadway agencies and lack of standards to compare the
results from different types of devices.
For the NCHRP report 136 (2), the researchers conducted a survey among highway agencies to
gather information on different types performance measures for winter maintenance used by
these agencies. The following table lists outcome based performance measures used by different
agencies.
Table 2: Outcome based Performance Measures used by Different Agencies (NCHRP 136,
2007)
Measure
Approach
Time to reasonable near-normal winter
a. Visual inspection by maintenance
conditions
personnel (AK, CA, NV, NM, NY)
b. Reports from field personnel (IA, CA,
NV, NM, NY)
c. Visual inspection by law enforcement
(NM)
Customer satisfaction
a. Annual season at end of season (AK)
b. Internet survey (CA)
Travel speed
a. Automatic traffic recorders (NY, IA)
Time to bare pavement
a. Visual inspection by maintenance
personnel (CO, MD, NV, OH, WA,
ON)
b. Reports from field personnel (CO,
MD, MO, NV, OH, WA)
c. Visual inspection by law enforcement
(WI)
Total time of road closure
a. Accounting records of hours closed
(CA)
Total time of chain restrictions
a. Records of chain restriction hours
(CA, CO)
Time to single bare wheel track
a. Reports from field personnel (IA, KS)
Time to two bare wheel paths
a. Reports from field personnel (KS)
Time to treat critical areas
a. Reports from field personnel (MO)
Friction
a. Testing (OH, ON)
b. Established friction coefficient
(Sweden)
As seen from the above table, majority of agencies surveyed during NCHRP Project 6-17 (2),
use visual pavement condition as a performance measure for winter maintenance. The safety of
this approach can be evaluated by investigating any correlation between visual pavement
condition and pavement friction during winter operations.
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DATA COLLECTION METHODS FOR SELECTED PERFORMANCE MEASUREMENTS
Data for this study was obtained from an on-going research study for Michigan Department of
Transportation (MDOT) titled “Evaluating the Use of Tow Plows in Michigan”. The following
methodology was used to collect data necessary for each selected performance measurements.
Visual Pavement Condition
A pavement condition evaluation scale was developed for this study based on prior literature and
discussions with winter maintenance personnel from Michigan Department of Transportation
(MDOT) as shown in the following figure. This pavement condition evaluation scale was
incorporated into commercially available Dynatest “SURVEY” program to use in this study.
Surface
Condition
Description
Bare
Bare Pavement
Centerline Bare
(CL Bare)
Entire lane is cleared
of snow, ice and
slush.
Wheel Track Bare
(WT Bare)
Only wheel tracks
are bare,
snow/ice/slush in the
other areas
Loose Snow/Slush
(Loose Snow)
Loose snow/slush
covered
Snow Covered
(Snow)
Entire roadway is
covered with packed
snow and ice
Picture
Figure 1: Winter Pavement Condition Evaluation Scale
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Visual pavement condition data was collected while driving behind snow plows and recorded at
528 feet intervals. The condition of the pavement was keyed into the “SURVEY” software into
one of the 5 categories listed in Figure 1 and a photograph of the pavement condition was recorded
for future use. After the data collection process is complete, quality checks were performed by
comparing recorded data against the photographs taken at the same location.
Friction
Friction testing was performed using Dynatest 6875 Continuous Friction Tester (CFT). This
tester uses a two-axis force transducer mounted on a retractable fifth wheel located under the
vehicle bed adjacent to the left wheel of the vehicle. Friction values between road surface and
test tire were measured for 500 feet at 1000 feet intervals. The average friction values for each
500 feet test sections were recorded and summarized.
Continuous friction testers are categorized as fixed-slip testers and generally measure maximum
friction value between the test tire and pavement as shown in the following figure. The maximum
friction value simulates ABS braking action.
Figure 2: Friction vs. Slip (5)
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SUMMARY OF DATA
Sample data for this study was obtained for winter maintenance road sections assigned to Michigan
Department of Transportation’s Brighton maintenance garage. Brighton garage is located in
Livingston County, Michigan.
Visual pavement condition data and pavement friction data were collected on three winter storms
during 2013-2014 winter season. The data collection was performed along I-96 freeway within
Livingston County, Michigan behind a regular snow plow and a Tow Plow. Surface condition
behind the Tow Plow/Regular Plow was visually evaluated and recorded on Dynatest “Survey”
data collection software. Friction values between road surface and test tire were measured for
500 feet at 1000 feet intervals. The average friction values for each 500 feet test sections were
recorded and summarized. A summary of collected data during three winter storms are show in
the table below.
Table 3: Summary of Collected Data
Winter
Storm
Event
Storm 1
1/1/2014
Storm 2
1/5/2014
Storm 3
2/1/2014
Winter
Storm
Condition
Lane/Plow Type
5.5” of
Snow for
12 hours
(Avg.
Temp.
12°F)
WB Middle
Lane
Tow Truck
WB Slow Lane
Tow Plow
EB Fast Lane
Regular Plow
WB Slow Lane
Tow Truck
WB Outside
Shoulder
Tow Plow
EB Fast Lane
Regular Plow
WB Slow Lane
Tow Truck
WB Shoulder
Tow Plow
EB Middle Lane
Tow Truck
EB Slow Lane
Tow Plow
10.2” of
Snow for
22 Hours
(Avg.
Temp
22°F)
7.4” of
Snow for
22 hours
(Avg.
Temp.
26°F)
N/A
Pavement Surface
Average
Operating Condition behind
Snow Plow
Speed of
the Plow
N/A
WT Bare – 100%
0.33
38.2
WT Bare – 100%
0.27
34.5
WT Bare – 100%
0.18
38.6
WT Bare - 100 %
NA
N/A
0.16
35.4
0.13
35.85
N/A
N/A
WT Bare – 43%
Loose Snow – 43%
CL Bare – 14%
Loose Snow – 91%
WT Bare – 9%
WT Bare – 50%
Loose Snow – 50%
Loose Snow – 100%
0.12
37.4
N/A
N/a
Average
MU
Loose Snow – 74%
WT Bare – 26%
Loose Snow – 62%
WT Bare – 27%
Snow Covered –
11%
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WB Fast Lane
Regular Plow
EB Fast Lane
Regular Plow
0.11
37.8
0.11
38.5
Loose Snow – 85%
WT Bare – 15%
Loose Snow – 91%
CL Bare – 6%
WT Bare – 3%
The following figure shows the amount of friction loss during winter storms by comparing baseline
friction values collected during the summer time to a sample of data collected during a winter
storm behind a snow plow. In average, the friction loss during a winter storm account for a
minimum of 0.69 to 0.33 (52% loss at the storm 1) and 0.69 to 0.13 (81% loss during the storm 3).
However, it should be noted these high friction losses occur immediately behind the snow plow
and once the salt or other chemicals start to work this friction losses may not be this high.
Friction Data Behind Tow Plow WB I‐96 Slow Lane
1.2
1
Mu
0.8
0.6
towplow Storm 1
0.4
WBI96 Baseline
0.2
0
‐2000
3000
8000
13000
18000
23000
28000
33000
Distance (ft) from NB US‐23 Ramp to WB I‐96
Figure 4: Friction Loss due to Winter Storms
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The sample of collected data for each storm are shown in figures below.
Figure 5: Pavement Condition behind Tow Plow and Regular Plow for Storm 1
As seen in the above figures, wheel track bare condition was observed immediately behind both
tow plow and regular plow. This is an expected condition due to same type of cutting edges are
used in both tow plow and underbody plow.
Figure 6: Friction Data behind Tow Plow and Regular Plow for Storm 1
The above friction plots show similar values in average behind both type of plows, ranging from
0.33 for tow plow and 0.27 for regular plow. It should be noted these measurements were taken
on different lanes of I-96 freeway.
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Figure 7: Pavement Condition behind Tow Plow and Regular Plow for Storm 2
Visual pavement condition behind the tow truck of the tow plow show wheel track bare condition
while behind regular plow shows loose snow for more than 90 percent of the lane area for the
Storm 2.
Figure 8: Friction Data behind Tow Plow and Regular Plow for Storm 2
Above friction plots behind snow plows show similar average friction values ranging from 0.18
for the tow plow and 0.16 for the regular plow.
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Figure 9: Pavement Condition behind Tow Plow and Regular Plow for Storm 3
Visual pavement condition during the Storm 3 behind both snow plows show loose snow condition
for the majority of lane areas.
Figure 10: Friction Data behind Tow Plow and Regular Plow for Storm 3
Similarly, average friction values behind both snow plows show similar values ranging from 0.13
for the tow plow and 0.11 for the regular plow.
The above pavement condition pictures and friction values show, differences in pavement
condition and pavement friction in different type of winter storms, despite of the visual similarity
seen behind both plows during each storm. During the winter storm 1 the average air temperature
was approximately 12°F and snow was light and dry. This resulted 100% wheel track bare
condition behind both tow plow and regular plow. Also pavement friction shows relatively high
values (average MU of 0.33 and 0.27 for tow plow and regular plow respectively). During winter
storm 3, the average air temperature was 26°F and snow was wet and heavy. This resulted loose
snow behind both snow plows and relatively low friction values (average mu of 0.13 and 0.11).
This clearly shows, the pavement condition behind the snow plow and pavement friction behind
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snow plow, is not only correlated to snow amount but also to type of snow event (dry snow, wet
snow etc.).
RESULTS
A comparison study was performed using collected hundreds of visual pavement condition data
behind the snow plows and pavement friction values from the three storms during 2013-2014
winter season. Friction values were summarized (maximum, average, minimum) for each observed
field conditions (Wheel track bare and loose snow). The following figure shows change in friction
values with different winter pavement conditions. Since only the wheel track bare and loose snow
conditions were observed during the field evaluations, only those conditions are shown.
0.7
Max
Friction Coefficient, MU
0.6
Average
Min
0.5
0.4
0.3
0.2
0.1
0
Bare
Centerline Bare
Wheel Track Bare
Loose Snow
Snow Covered
Winter Pavement Condition
Figure 11: Pavement Visual Condition and Measured Friction Values
As seen from the above figure, there are marked differences in measured friction values for
different winter pavement conditions. The visual pavement condition behind snow plows show
wheel track bare or loose snow during most of the snow storms. Therefore the above chart can be
used for 90 percent of the snow storms. This develop chart can be used as a safety predictor and
performance measure predictor for winter operations. Work is underway to include more data from
different types of snow storms to update this chart with other visual pavement conditions.
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SUMMARY AND CONCLUSIONS
With shrinking budgets and increasing demand for better mobility from the public, highway
agencies are constantly searching for better and improved methods for winter maintenance.
Performance measures for winter maintenance plays a critical role in this aspects especially for
highway agencies who uses contractors for winter maintenance operations. Many highway
agencies currently use visual pavement condition as an outcome based performance measure
among other traditional performance measures. However, there is no proper understanding related
to the safety level of different pavement conditions under winter storms. A relationship with
pavement friction levels and visual pavement condition under winter conditions was developed in
this pilot study. This relationship can be used by the highway agencies to predict the safety level
of the roadway under different pavement conditions. More work is underway to incorporate all
possible visual pavement conditions and corresponding friction values to provide a complete
understanding of how friction levels change with different pavement conditions.
REFERENCES
1. Snow and Ice, Road Weather Management Program, Federal Highway Administration,
Washington D.C. http://ops.fhwa.dot.gov/weather/weather_events/snow_ice.htm. Accessed July 7,
2014.
2. Maze, T.H., Albrecht, C., Kroeger, D and Wiegand, J., Performance Measures for Snow and Ice
Control Operations, NCHRP Web-Only Document 136, Transportation Research Board, National
Cooperative Highway Research Program, Washington D.C., 2007.
3. Blackburn, R.R., Bauer, K.M., Amsler, D.E., Boselley III, S.E., and McElroy, A.D., Snow and Ice
Control: Guidelines for Materials and Methods. NCHRP Report 526, Transportation Research
Board, National Cooperative Highway Research Program, Washington D.C., 2004.
4. Al-Qadi, I, Loulize, A., Flintsch, G.W., Roosevelt, D.S., Decker, R., Wambold, J.C., and Nixon,
W.A., Feasibility of using Friction Indicators to Improve Winter Maintenance Operations and
Mobility, NCHRP Web Document 53 (Project 6-14), Transportation Research Board, National
Cooperative Highway Research Program, Washington D.C., 2002
5. Henry, J.J., “Evaluation of Pavement Friction Characteristics: A synthesis of Highway Practice,
NCHRP Synthesis 291, Transportation Research Board, National Research Council, Washington,
D.C., 2000
6. Adams, T.M., Danijarsa, M., Martinelli, T., Stanuch, G., and Vonderohe A., “Performance
Measures for Winter Maintenance”, Transportation Research Record: Journal of the
Transportation Research Board, No. 1824, Transportation Research Board, National Academics,
Washington, D.C., 2004, pp 87-97.