Introduction

The U.S. spends $2.3 billion annually to keep roads clear of snow and ice¹; in Canada, more than $1 billion is spent annually on winter maintenance². Depending on the road weather scenarios, resources available and local rules of practice, maintenance agencies use a combination of tools for winter road maintenance and engage in activities that include anti-icing, deicing, sanding and mechanical means (e.g., snowplowing). They are under increasing pressure to maintain a high level of service (LOS) even during the winter months, while working with limited financial and staffing resources and recognizing the corrosion and environmental challenges related to chemical and material usage^3-6. As Figure 1 illustrates, these objectives may conflict or compliment one another. This paper provides an overview of best practices, emerging challenges, and research needs related to the snow and ice management, with the focus on winter road service in North America.

Best Practices for Winter Road Maintenance

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Figure 1. The multiple, sometimes conflicting, objectives of winter road maintenance

Recent advancements in science and technology have been made with the ultimate goal delivering the right type and amount of materials in the right place at the right time for snow and ice control. This section describes best practices that are expected to improve the effectiveness and efficiency of winter operations, to optimize material usage, and to reduce associated annual spending and corrosion and environmental impacts.

Computer-Aided Design of Passive Snow Control Measures

Engineered mitigation of blowing and drifting snow through road design and snow fences has been integrated into a software tool, which can reduce maintenance costs and closure times and enhance overall LOS by “improving visibility, preventing drifting on the road, and reducing road icing” ⁷.

Anti-Icing and Pre-Wetting Practices

Winter highway maintenance practices in North America have traditionally been based on reactive strategies (such as deicing and sanding) where the launch of maintenance operations relied on signs of snow and ice accumulation. In the two decades, anti-icing has been increasingly adopted by winter maintenance personnel, which is the early application of chemicals to help prevent black ice and prevent or weaken the bond between ice and the roadway surface. Another practice widely adopted by winter maintenance agencies is termed pre-wetting, i.e., the addition of a liquid chemical to an abrasive or solid chemical before it is applied to the road. The pre-wetting of solids is performed either at the stockpile or at the spreader. Compared with traditional methods for snow and ice control, anti-icing and pre-wetting lead to decreased applications of chemical products, reduced use of abrasives, decreased maintenance costs, improved roadway LOS (in terms of pavement friction), and lower accident rates⁸,

Anti-icing has been recognized as a pro-active approach to winter driver safety. Pre-wetting has shown to increase the performance of solid chemicals or abrasives and their longevity on the roadway surface, thereby reducing the amount of materials required⁸, Anti-icing and pre-wetting practices continue to evolve, as new snow and ice control materials are continually introduced into the market; for instance, the combined use of beet juice and other agricultural by-products with chlorides has shown to enhance performance and reduce corrosivity of products.

Improved Weather Forecasts

Accurate weather forecasts are critical in the successful implementation of anti-icing programs, since such information will guide the timing and amount of chemicals needed for applications. In general, the improved accuracy and increased usage of weather information can reduce costs for winter road maintenance, as shown in a recent case study of the Iowa Department of Transportation¹⁰. Recent years have seen significant improvements in technologies related to weather observations, forecasting, and information technology, including:

• Road Weather Information Systems/Environmental Sensor Stations (RWIS-ESS): As an aggregation of roadside sensing and processing equipment used to measure current weather conditions at the road environment and to transmit the data, RWIS-ESS have been widely used by transportation managers in North America since the late 1980s. They can provide information regarding pavement temperature and pavement conditions in addition to atmospheric observations, which is commonly used to aid in winter maintenance decisions and to help alert motorists of dangerous driving conditions¹¹.

• Mesonets: As regional networks of weather information, mesonets aim to integrate observational data from a variety of source and thus provide a more comprehensive and accurate picture of current weather conditions and great potential for improved weather forecasts. Regional mesonet examples include the Washington State’s rWeather, University of Utah’s MesoWest, Iowa’s WeatherView, California’s WeatherShare¹², among others. At the national level, the Clarus Initiative (www.clarusinitiative.org) is developing a partnership to establish a nationwide road weather observation network that provides the integration and quality control of atmospheric and pavement observations from both fixed and mobile platforms^13-15. Such a data management system is expected to maximize the accessibility and utility of road weather observations and facilitate more accurate, route-specific forecasting of road weather conditions (e.g., the starting and ending times of winter weather events).

A recent survey of winter maintenance practitioners suggested the benefits of customized weather forecasts, including more accurate forecasts; timely forecasts and access to a forecaster; advanced warning of storm conditions; better response time and improved planning and scheduling of staff; and better use of chemical products¹⁶. For instance, the Utah Department of Transportation (DOT) features a Weather Operations/RWIS Program that provides pre-storm, during-storm, and post-storm area-specific weather forecasts to the maintenance engineers, area supervisors and local sheds. The forecasts are tailored to the users’ needs, addressing issues such as the timing of events, temperature trends and precipitation rates. The UDOT’s Weather Operations Program was estimated to save the DOT maintenance sheds $2.2 million per year in labor and materials costs for snow and ice control, which corresponds to a benefit-cost ratio of 11:1¹⁷.

Fixed Automated Spray Technology (FAST)

FAST is an important tool for anti-icing at key locations, enabling winter maintenance personnel to treat potential conditions before snow and ice problems arise. Such tools, coupled with RWIS and reliable weather forecasts, promote the paradigm shift from being reactive to proactive in fighting winter storms. Installing a FAST system is complex and the challenges are often site-specific, and difficulties seem to be expected during the operations, particularly in areas related to software, system activation, and the pumping system. However, FAST systems can be cost-effective if their locations are carefully chosen and if the system is supported with reliable environmental sensors. On balance, North American transportation agencies consider FAST to be an evolving technology, and are not planning significant new installations of FAST in the near future¹⁸.

Advanced Snowplow Technologies

Cutting-edge snowplow technologies can make the task of maintaining winter roads more efficient, safer and less costly. Numerous vehicle-based sensor technologies, including automatic vehicle location (AVL), surface temperature measuring devices, freezing point and ice-presence detection sensors, salinity measuring devices, visual and multi-spectral sensors, and millimeter wavelength radar sensors, have been developed in recent years. Of these, AVL systems and road surface temperature measuring devices are the only ones that have matured and become fully operational, while the remainders are still in the development and testing phases¹⁹. While each of the advanced technologies may be used independently, their greatest benefit can be realized when they are integrated with one another to provide a greater depth of information. For example, AVL, when coupled with other sensor technologies, can record geo-referenced data such as the surface temperature, ice-presence, road surface salinity, blade position, engine hours and miles traveled²⁰.

Surface temperature measurement devices, on-board freezing point and ice presence detection sensors, and salinity sensors can be considered as vehicle-based RWIS technologies. The technologies, if successfully implemented, could be integrated with AVL to provide improved real-time knowledge of road and environmental conditions throughout a network, and not merely at points where in-pavement measurements are collected.

Other important snowplow technologies include: an improved displacement snowplow²¹, an automated snowblower or rotary plow²², driver assistive technologies such as the Minnesota’s Intelligent Vehicle Lab Snowplow Driver Assistive System, California’s Advanced Snowplow Driver Assistance System, and the emerging use of laser technology for collision avoidance²³, and virtual snowplow training²⁴. Currently, there are also application equipments that adjust the application rate of snow and ice control materials based on real-time data from onboard sensors.

Pavement Technologies

A few interesting pavement technologies related to snow and ice control are: bridge deck or pavement heating technologies²⁵, an in-place anti-icing pavement overlay technology²⁶, and a temperature-sensitive paint that can warn drivers about icy conditions ²⁷.

Maintenance Decision Support System (MDSS)

MDSS is a computer-based system that integrates current weather observations and forecasts to support the response of maintenance agencies to winter weather events^28-30. An MDSS can provide users with real-time road treatment guidance for each maintenance route, addressing the fundamental questions of what, how much and when according to the forecast road weather conditions, the resources available and local rules of practice. MDSS can also be used as a training tool with a what-if scenario treatment selector that can be used to examine how the road condition might change over a 48-hour period with the user-defined treatment times, chemical types, or application rates. Several different approaches have been used to develop an MDSS, including the Federal Highway Administration’s (FHWA) functional prototype, developed and tested in the last few years^31-33; an MDSS developed through a multi-state pooled fund study^34-37; as well as some private-sector approaches.

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Table 1. The summary of cost-benefit analysis of implementing MDSS.

A recent cost-benefit study revealed that the tangible benefits of MDSS significantly outweigh its costs, and relevant data for three case-study states are provided in Table 1³⁸. There are also many intangible benefits of MDSS implementation, such as improved documentation of actual maintenance activities, reduced response time and clearance time, reduced labor and equipment costs, reduced corrosion and environmental impacts, and establishment of a platform for future technology implementation.

In order to fulfill functions of MDSS and achieve maximum benefits, it is important to provide adequate training and technical support to boost user acceptance of the new technology. Since the quality of MDSS recommendations hinges on the accuracy and timeliness of input information (e.g., recent and current road and weather conditions), agencies should utilize accurate and reliable weather forecast services to support MDSS. The use of mobile data collection and other technologies can improve the efficiency of two-way communications between MDSS and truck operators and further enhance operators’ levels of acceptance of and trust in the MDSS³⁹.

Emerging Challenges and Research Needs

Significant progress has been made in the last two decades in North America’s winter road maintenance operations, as the joint outcome of improved understanding of the science relevant to snow and ice control, more accurate/reliable/timely road weather information, and enhanced tools (techniques, technologies, materials, and systems), all of which facilitated best practices such as the shift to more proactive and cost-effective approaches. Substantial investments in winter road maintenance research have been made in the United States and other countries, reflecting the importance of this crosscutting subject and its tremendous economic and safety implications.

Nonetheless, looking to the future, there are many emerging challenges and remaining knowledge gaps related to winter road maintenance. For instance, along with the trend of using snowplows as mobile sensor platforms, many of the advanced technologies discussed above are still evolving and a multitude of technological and institutional barriers exist to their successful implementation. Integration of various vehicle-based technologies is important yet challenging, particularly in the areas of communications, user interface, and software/hardware expandability and compatibility. Integration was an underlying goal in several U.S. winter maintenance vehicle-based technology projects, including RoadView^TM, Minnesota DOT’s Advanced Snow Plow, and the Highway Maintenance Concept Vehicle⁴⁰. There is also the trend toward increased automation of snowplow operations, in light of the complexity associated with executing winter maintenance tasks during storm events, when such tasks are most critical⁴¹. While there are ongoing evolutionary improvements in advanced snowplow technologies and more innovations can be expected, their ultimate integration into the winter maintenance toolbox will depend on continued investment and efforts in research and development as well as user-needs-driven product strategies by the private sector.

In light of growing concerns over the corrosion and environmental effects of snow and ice control materials and increased focus on sustainability, research is also needed to establish a framework that would integrate the agency priorities with reliable laboratory testing data and thus provide agencies with a systematic approach to decision-marking in materials selection^42-43.

To promote best practices and improve the effectiveness and benefits of snow and ice control operations, it will entail advancements in micro-scale road weather forecasting and sensing (especially important to the timing of anti-icing strategies), more integrated and automated onboard sensor technologies, vehicle-infrastructure integration (VII), improved understanding of the “dynamic layer” on the road surface and its relationship with pavement friction⁴⁴, better science to determine the proper timing and frequency of anti-icing and deicing, and better means to quantify the performance and impacts of winter road maintenance.

Finally, there are urgent needs for the winter road maintenance community to develop winter service benchmarks (for better planning and allocation of resources), to communicate winter maintenance priorities and performance to various stakeholders, to maintain a knowledgeable and well-rounded winter maintenance workforce in the increasingly challenging funding and staffing environments, to manage new challenges derived from climate change (e.g., less predictable timing, severity and duration of storm events) and driver behavior change (e.g., aging population), and to develop a long-range research roadmap to systematically examine and address the knowledge gaps.

In summary, the interdisciplinary nature of snow and ice control activities requires experts from a variety of disciplines working together to produce viable solutions and to promote best practices that address the multiple objectives of winter road service.

References

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9. Ibid.

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15. Pisano, P.A., Kennedy, P. J., and Stern, A.D. (2008). “A New Paradigm in Observing the Near Surface and Pavement: Clarus and Vehicle Infrastructure Integration”. Paper # Weather08-012. TRB Transportation Research Circular E-C126: Surface Transportation Weather and Snow Removal and Ice Control Technology. Proceedings of the 4th National Conference on Surface Transportation Weather, Indianapolis, Indiana, June 16-17, 2008.

16. Shi, X., O’Keefe, K., Wang, S., and Strong, C. (2007). Evaluation of Utah Department of Transportation’s Weather Operations/RWIS Program: Phase I. A final report prepared for the Utah Department of Transportation, Salt Lake, UT. <http://www.westerntransportationinstitute.org/documents/reports/4W0892_Final_Report.pdf>, (December 15, 2009).

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20. Ibid.

21. Pell, K.M. (1994). An Improved Displacement Snowplow. Final Report. Prepared for Strategic Highway Research Program (SHRP), Washington, D.C.

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24. CTC & Associates (2008). Virtual Snowplow Training: State of the Practice and Recent Research. Prepared for the Clear Roads Pooled Fund Study.

25. CTC & Associates (2008). Alternative Energy: Turning up the Heat on Snowy Roads and Bridges. Prepared for the Clear Roads Pooled Fund Study.

26. Nixon, W. (2006). An Analysis of the Performance of the Safelane? Overlay during Winter 2005-06. Final Report. Prepared for Cargill Deicing Inc.

27. Dumé, B. (2008). “Intelligent paint turns roads pink in icy conditions”, (Dec. 1, 2008).

28. Anderle, P., and McClellan, T. (2009). “Modernizing maintenance operations using a maintenance decision support system”, Journal of Public Works & Infrastructure, 2(1): 15-24.

29. Nixon, W.A. (2009). “Using A Maintenance Decision Support System in Winter Service Operations””, Journal of Public Works & Infrastructure, 2(1): *-*.

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31. CTRE (2003) Maintenance Decision Support System (MDSS) in Iowa. Report No. CTRE Project 02-129 (Ames, IA: Iowa Department of Transportation).

32. NCAR (2004) The Maintenance Decision Support System (MDSS) Project: Technical Performance Assessment Report, Second Iowa Field Demonstration Winter 2003-2004, Version 1.1 (Boulder, CO: Federal Highway Administration, U.S. Department of Transportation).

33. NCAR (2005) The Maintenance Decision Support System (MDSS) Project: Technical Performance Assessment Report, Colorado Field Demonstration Winter 2004-2005, Version 1.0. (Boulder, CO: Federal Highway Administration, U.S. Department of Transportation).

34. Hart, R., and Osborne, L.F. (2003) Development of a Maintenance Decisions Support System—Phase 1. Report No. SD2002-18-I (Grand Forks, ND: South Dakota Department of Transportation).

35. Hart, E., Osborne, L., Cammack, P. and Mewes, J. (2004) Development of a Maintenance Decisions Support System—Phase II. Report No. SD2002-18-I2 (Grand Forks, ND: South Dakota Department of Transportation).

36. Huft, D. (2006) The Pooled Fund Study Maintenance Decision Support System. Presented at 2006 National Rural ITS Conference, Big Sky, Montana.

37. Ye, Z., Strong, C., Shi, X., and Conger, S. (2009). Analysis of Maintenance Decision Support System (MDSS) Benefits and Costs. Final Report for the MDSS Pooled Fund led by the South Dakota Department of Transportation. May 2009. <http://www.westerntransportationinstitute.org/documents/reports/4W1408_Final_Report.pdf>, (December 15, 2009)..

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39. Ye et al., ref. 30 above.

40. Shi et al., ref. 19 above.

41. Ibid.

42. Shi, X. (2005). “The Use of Road Salts for Highway Winter Maintenance: An Asset Management Perspective”. 2005 ITE District 6 Annual Meeting. Kalispell, MT. July 10-13, 2005.

43. Shi et al., ref. 4 above.

44. Strong, C., Ye, Z., and Shi, X. (2009). “Safety Effects of Winter Weather: State of Knowledge and Remaining Challenges”. Transport Reviews, in press.