WINTER MAINTENANCE PERFORMANCE MEASURES

WINTER MAINTENANCE PERFORMANCE MEASURES
ENSURING AND QUANTIFYING RETURN ON INVESTMENT THROUGH THE
DEVELOPMENT OF WINTER MAINTENANCE PERFORMANCE MEASURES
DENNIS JENSEN
Winter Maintenance Coordinator
Idaho Transportation Department
3311 W State Street
Boise, ID 83703, USA
+1 208 334 8472,
dennis.jensen@itd.idaho.gov
BOB KOEBERLEIN, PE
Mobility Services Engineer
Idaho Transportation Department
3311 W State Street
Boise, ID 83703, USA
+1 208 334 8487,
robert.koeberlein@itd.idaho.gov
ED BALA, PE
District 5 Engineer
Idaho Transportation Department
5151 South 5th Avenue
Pocatello, ID 83205, USA
+1 208 239 3300,
ed.bala@itd.idaho.gov
PAUL BRIDGE
Meteorologist
Vaisala Inc
194 South Taylor Avenue
Louisville, CO 80027, USA
+1 303 324 1700,
paul.bridge@vaisala.com
ABSTRACT
The Idaho Transportation Department (ITD) budget is approximately $7.5 million
annually for winter maintenance materials, $12 million for snow plow operations and $6
million in operator salaries. However until recently, it was difficult to assess how well
the money was being spent and what efficiencies in terms of mobility and safety were
being realized on our road network. ITD places a high priority on providing excellent
customer service and this extends to our winter road operations for the traveling public.
This paper describes how two key performance measures for winter maintenance were
developed and implemented. It also outlines some of the immediate and potential
benefits of the performance measures.
Key Words: ITS, winter maintenance, performance measures, RWIS, mobility, safety.
BACKGROUND
Idaho has a very diverse geography, ranging from high desert to mountainous terrain as
illustrated in Figure 1 below. The Idaho Transportation Department has, over the years,
developed a progressive winter maintenance program which involves investment in a
number of areas such as labor, training, equipment and materials. Typical treatment
materials include salt, salt brine, magnesium chloride and anti-skid. These materials
are applied by a winter maintenance operations fleet of 500 plus vehicles to maintain
highways in order to promote safe travel and winter mobility.
Figure 1 – Idaho Terrain
ITD has currently deployed a network of 99 Road Weather Information Systems (RWIS)
statewide to monitor atmospheric and pavement conditions. The roadside RWIS stations
are polled on 15-minute intervals and data is plotted to track trends that reflect ice/snow
build up or removal, air and pavement temperature changes and surface friction.
Maintenance staffs are able to review the data plots and make decisions about road
treatment options, application rates and treatment timing.
When the RWIS system was first introduced, there was a mixed reaction from staff, both
positive and negative, as to how the data should be used and applied. However there has
been a gradual acceptance and increased use of the data within the winter decision
making process. In particular, two early ITD adopters of the system began to realize that
there were patterns in the RWIS data that could be directly linked with winter maintenance
operations.
The RWIS sites include atmospheric sensors and remote pavement sensors. The data
received from the pavement sensors includes: road surface temperature, surface condition
(e.g. dry, wet, snow and ice), layer thickness and friction coefficient (“grip”).
It was the latter parameter, grip, which allowed the users to make an easy connection
between a storms severity and the impact it had on vehicle mobility. The grip level is
provided as a number, as per international convention, between 0 and 1. Higher numbers
indicate good grip, so a figure of 0.82 would indicate bare pavement, whereas lower
numbers, such as 0.1 would indicate the presence of snow or ice and very slippery
conditions. Users in Idaho made the following observations (figure 2):

> 0.6 usually dry (or wet) surface

0.5 to 0.6 slush or ice forming

0.4 to 0.5 snow pack or icy

0.3 to 0.4 icy - vehicles may start sliding off

<0.3 icy - multiple vehicle slide offs possible; mobility greatly affected
Figure 2 – Grip Descriptions
The grip reading is derived from the Vaisala DSC111 sensor, which is based on active
transmission of infrared light beam on the road surface and detection of the backscattered
signal at selected wavelengths. By proper selection of wavelength it is possible to observe
absorption of water and ice practically independently of each other. Since white ice, i.e.
snow or hoar frost, reflect light much better than black ice, these two main types of ice can
be distinguished as well. The observed absorption signal is readily transformable to water
layer, to ice layer or to snow/frost amount in millimeters of water equivalent. With this
information it is straight forward to determine the surface state as dry, moist, wet, icy,
snowy/frosty or slushy. By correlating the surface state with a decelerometer it was
possible to derive friction values.
From 2010 to 2012, ITD developed and refined the relationships to produce two winter
maintenance performance measures:
1. Winter performance index
2. Storm Severity Index
3 The Winter Performance Index is derived using a two step process starting with the Storm
Severity Index that uses sensor data inserted into a formula (wind speed, surface
precipitation layers and surface temperatures) to calculate an index value. The Storm
Severity Index value is then inserted into a formula along with the ice-up duration to
establish the Winter Performance Index. This value is then compared with a performance
scale (typically 0.00 to 0.7 with a goal of 0.5 or less) to identify how successful the road
treatment and timing were by the field maintenance personnel.
Storm Severity Index = WS (Max) + WEL (Max) + 300/ST (Min)
Where the following units are used:
WS = Wind Speed (mph)
WEL = Water Equivalent Layer (millimeters)
ST = Surface Temperature (degrees F)
[Index range is 10 to 80 for typical storm events with severe cold and high winds running
as high as 500]
Winter Performance Index = Ice-Up Time (hours) / Storm Severity Index
Where:
Ice-Up Time is when the grip is below 0.6 for at least a 30 minute period
The goal is to have a Winter Performance Index of 0.5 or less.
Figure 3 – Storm Severity Index Formula
WINTER MOBILITY INDEX
The Winter Mobility Index (0-1.00) is derived using the percentage of time the road
conditions did not significantly impede mobility during a storm event (safe “grip” value of
0.6 or higher) when precipitation was on the surface with below freezing surface
temperatures being observed.
The calculations of the two indices was initially a manual process, with detailed analysis of
each winter storm performed for each set of RWIS surface and atmospheric data. This
proved to be a laborious task, so ITD partnered with Vaisala in order to automate the
process allowing the results to be produced much quicker. By integrating the model and
algorithms directly into the RWIS program visualization application (Navigator II),
maintenance managers can now view the results of their maintenance performance almost
immediately after each winter storm has passed. Both the Winter Performance Measure
and the Winter Mobility Index tabulations are shown in Figure 4.
Figure 4 – Layout of Winter Performance Measure and the Winter Mobility
Index
The goals of the Winter Maintenance Performance Measures are tied directly to ITD’s
Strategic Plan:
• Track progress to maintaining safe roads
• Track progress to maintaining mobility
• Promote economic opportunity by minimizing weather impacts on commerce
• Achieve greater uniformity in winter operations statewide
• Promote a cost-effective winter road maintenance program within available
resources
RESULTS
Each of the six ITD Districts deals with its own specific winter maintenance challenges that
are defined by topography, weather patterns, highway usage, maintenance resources
available and maintenance priorities. Therefore, the results should be viewed in relative
terms rather than comparing one area of the state to another area. The focus is to build
tools for managing winter performance improvements for each district, starting with
baseline data that was gathered in the most recent winter season.
Through the winter of 2011-2012 the results by ITD district are shown below in Table 1.
District
Number
Storm
Events
of Average
storm
duration
(hours)
Percentage
of
Storm
Events that
ice/snow
durations
significantly
reduced
Average
time
recovery
(hours)
Winter
to Mobility
Percentage
1
2
3
4
5
6
439
164
290
340
294
382
15.2
8.9
10.8
7.2
9.7
15.4
66%
67%
64%
91%
89%
66%
5.7
5.5
5.9
3.7
3.8
11.8
63%
39%
46%
49%
60%
23%
Table 1 - 2011-2012 the results by ITD district
Data collected over the past three winters (2010-2013) indicates a positive trend on the
Mobility Performance Index as shown in Figure 5.
Figure 5 - Data collected over the past three winters (2010-2013) indicates a positive
trend on the Mobility Performance Index
NEXT STEPS
ITD plans to continue monitoring the Winter Performance Measures and make any
adjustments to the formulas as necessary to more closely model and correlate the Winter
Performance Measures to winter road conditions experienced by our customers. ITD also
plans to add RWIS sites at strategic locations to increase the sampling density. Training
and coaching are also important components of the winter maintenance program and need
to be provided to the maintenance staff on a continuing basis.
CONCLUSIONS
The adoption of winter maintenance performance measures has created the ability for ITD
to quantify storm event severity, index the response into a measureable efficiency and
then allow the ITD districts to make adjustments to improve winter operations that were not
previously recognizable or quantifiable. This has been highly beneficial to ITD and the
traveling public.
ITD intends to continue the use of Winter Performance Measures and analysis to improve
services to our customers, increase our own efficiencies and reduce costs to the
taxpayers. While going through the development process ITD realized that the benefits
they we were seeing with respect to the return on investment in the RWIS program could
be utilized by other highway operators and these benefits are potentially enormous when
considered as a global application.
REFERENCES
1. Yrjö Pilli-Sihvola, Kimmo Toivonen, Taisto Haavasoja, Ville Haavisto, and Pauli
Nylander (2006) New Approach to Road Weather: Measuring Slipperiness.
SIRWEC Torino
2. Bridge, P (2008) Non-intrusive Road Weather Sensors and their role in ITS. 15th
World Congress on ITS, New York
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