Artificial-Intelligence-Based Optimization of the Management of Snow Removal Assets and Resources Final Report, October 2002

Geographic information systems (GIS) and artificial intelligence (AI) techniques were used to develop an intelligent snow removal asset management system (SRAMS). The system has been evaluated through a case study examining snow removal from the roads in Black Hawk County, Iowa, for which the Iowa Department of Transportation (Iowa DOT) is responsible. The SRAMS is comprised of an expert system that contains the logical rules and expertise of the Iowa DOT’s snow removal experts in Black Hawk County, and a geographic information system to access and manage road data. The system is implemented on a mid-range PC by integrating MapObjects 2.1 (a GIS package), Visual Rule Studio 2.2 (an AI shell), and Visual Basic 6.0 (a programming tool). The system could efficiently be used to generate prioritized snowplowing routes in visual format, to optimize the allocation of assets for plowing, and to track materials (e.g., salt and sand). A test of the system reveals an improvement in snowplowing time by 1.9 percent for moderate snowfall and 9.7 percent for snowstorm conditions over the current manual system.

Snow Removal on Iowa's Secondary Roads, HR-267, 1984

Snow removal on the 90,000 mile Iowa secondary road system is a major concern of county engineers. Rural residents rely almost entirely on motor vehicles for travel. They have come to expect passable roads during all types of weather and as most county engineers know, the public is less tolerant of problems in snow removal than in any other highway department function. To avoid snow removal problems, maintenance personnel begin preparation before the winter maintenance season. The slide tape presentation, "Snow Removal on Iowa's Secondary Roads", was developed to assist in training and retraining maintenance personnel each year prior to winter. The program covers preparation for winter, snow and ice removal, and after storm care of equipment.

Effect of Video Camera-Based Remote Roadway Condition Monitoring on Snow Removal-Related Maintenance Operations. 2014

Remote monitoring through the use of cameras is widely utilized for traffic operation, but has not been utilized widely for roadway maintenance operations. The Utah Department of Transportation (UDOT) has implemented a new remote monitoring system, referred to as a Cloud-enabled Remote Video Streaming (CRVS) camera system for snow removal-related maintenance operations in the winter. The purpose of this study was to evaluate the effectiveness of the use of the CRVS camera system in snow removal-related maintenance operations. This study was conducted in two parts: opinion surveys of maintenance station supervisors and an analysis on snow removal-related maintenance costs. The responses to the opinion surveys mostly displayed positive reviews of the use of the CRVS cameras. On a scale of 1 (least effective) to 5 (most effective), the average overall effectiveness given by the station supervisors was 4.3. An expedition trip for this study was defined as a trip that was made to just check the roadways if snow-removal was necessary. The average of the responses received from surveys was calculated to be a 33 percent reduction in expedition trips. For the second part of this study, an analysis was performed on the snow removal-related maintenance cost data provided by UDOT to see if the installation of a CRVS camera had an effect in reducing expedition trips. This expedition cost comparison was performed for 10 sets of maintenance stations within Utah. It was difficult to make any definitive inferences from the comparison of expedition costs over the years for which precipitation and expedition cost data were available; hence a statistical analysis was performed using the Mixed Model ANOVA. This analysis resulted in an average of 14 percent higher ratio of expedition costs at maintenance stations with a CRVS camera before the installation of the camera compared to the ratio of expedition costs after the installation of the camera. This difference was not proven to be statistically significant at the 95 percent confident level, but indicated that the installation of CRVS cameras was on the average helpful in reducing expedition costs and may be considered practically significant. It is recommended that more detailed and consistent maintenance cost records be prepared for accurate analysis of cost records for this type of study in the future.

Operator's manual : snow removal unit, self-propelled, gasoline driven, rotary, wheel mtd, winterized (FWD model S-349-V), serial numbers G30681 through G30690 and G30750 through G30759 FSN 3825-810-7074.

"December 1961."

Report on the problem of street cleaning and snow removal in the city of Minneapolis, Minnesota.

Mode of access: Internet.

Synthesis of Best Practice for Increasing Protection and Visibility of Highway Maintenance Vehicles, CTRE Final Report Iowa DOT Project TR-475, August 2002

The purpose of this research project is to study current practices in enhancing visibility and protection of highway maintenance vehicles involved in moving operations such as snow removal and shoulder operations, crack sealing, and pothole patching. The results will enable the maintenance staff to adequately assess the applicability and impact of each strategy to their use and budget. The report’s literature review chapter examines the use of maintenance vehicle warning lights, retroreflective tapes, shadow vehicles and truck-mounted attenuators, and advanced vehicle control systems, as well as other practices to improve visibility for both snowplow operators and vehicles. The chapter concludes that the Manual on Uniform Traffic Control Devices does not specify what color or kind of warning lights to use. Thus, a wide variety of lights are being used on maintenance vehicles. The study of the relevant literatures also suggests that there are no clear guidelines for moving work zones at this time. Two types of surveys were conducted to determine current practices to improve visibility and safety in moving work zones across the country and in the state of Iowa. In the first survey of state departments of transportation, most indicated using amber warning lights on their maintenance vehicles. Almost all the responding states indicated using some form of reflective material on their vehicles to make them more visible. Most participating states indicated that the color of their vehicles is orange. Most states indicated using more warning lights on snow removal vehicles than their other maintenance vehicles. All responding state agencies indicated using shadow vehicles and/or truck-mounted attenuators during their moving operations. In the second survey of Iowa counties, most indicated using very similar traffic control and warning devices during their granular road maintenance and snow removal operations. Mounting warning signs and rotating or strobe lights on the rear of maintenance vehicles is common for Iowa counties. The most common warning devices used during the counties’ snow removal operations are reflective tapes, warning flags, strobe lights, and auxiliary headlamps.

Web-based Implementation of Winter Maintenance Decision Support System Using GIS and Remote Sensing, May 2005

Winter maintenance, particularly snow removal and the stress of snow removal materials on public structures, is an enormous budgetary burden on municipalities and nongovernmental maintenance organizations in cold climates. Lately, geospatial technologies such as remote sensing, geographic information systems (GIS), and decision support tools are roviding a valuable tool for planning snow removal operations. A few researchers recently used geospatial technologies to develop winter maintenance tools. However, most of these winter maintenance tools, while having the potential to address some of these information needs, are not typically placed in the hands of planners and other interested stakeholders. Most tools are not constructed with a nontechnical user in mind and lack an easyto-use, easily understood interface. A major goal of this project was to implement a web-based Winter Maintenance Decision Support System (WMDSS) that enhances the capacity of stakeholders (city/county planners, resource managers, transportation personnel, citizens, and policy makers) to evaluate different procedures for managing snow removal assets optimally. This was accomplished by integrating geospatial analytical techniques (GIS and remote sensing), the existing snow removal asset management system, and webbased spatial decision support systems. The web-based system was implemented using the ESRI ArcIMS ActiveX Connector and related web technologies, such as Active Server Pages, JavaScript, HTML, and XML. The expert knowledge on snow removal procedures is gathered and integrated into the system in the form of encoded business rules using Visual Rule Studio. The system developed not only manages the resources but also provides expert advice to assist complex decision making, such as routing, optimal resource allocation, and monitoring live weather information. This system was developed in collaboration with Black Hawk County, IA, the city of Columbia, MO, and the Iowa Department of transportation. This product was also demonstrated for these agencies to improve the usability and applicability of the system.

Risk Mitigation Strategies for Operations and Maintenance Activities, Final Report, April 2012

The objective of this research was to investigate the application of integrated risk modeling to operations and maintenance activities, specifically moving operations, such as pavement testing, pavement marking, painting, snow removal, shoulder work, mowing, and so forth. The ultimate goal is to reduce the frequency and intensity of loss events (property damage, personal injury, and fatality) during operations and maintenance activities. This report includes a literature review that identifies the current and common practices adopted by different state departments of transportation (DOTs) and other transportation agencies for safe and efficient highway operations and maintenance (O/M) activities. The final appendix to the report includes information for eight innovative O/M risk mitigation technologies/equipment and covers the following for these technologies/equipment: Appropriate conditions for deployment Performance/effectiveness, depending on hazard/activity Cost to purchase Cost to operate and maintain Availability (resources and references)

Observing wind, aerosol particles, clouds and precipitation: Finland's new ground-based remote-sensing network

The Finnish Meteorological Institute, in collaboration with the University of Helsinki, has established a new ground-based remote-sensing network in Finland. The network consists of five topographically, ecologically and climatically different sites distributed from southern to northern Finland. The main goal of the network is to monitor air pollution and boundary layer properties in near real time, with a Doppler lidar and ceilometer at each site. In addition to these operational tasks, two sites are members of the Aerosols, Clouds and Trace gases Research InfraStructure Network (ACTRIS); a Ka band cloud radar at Sodankylä will provide cloud retrievals within CloudNet, and a multi-wavelength Raman lidar, PollyXT (POrtabLe Lidar sYstem eXTended), in Kuopio provides optical and microphysical aerosol properties through EARLINET (the European Aerosol Research Lidar Network). Three C-band weather radars are located in the Helsinki metropolitan area and are deployed for operational and research applications. We performed two inter-comparison campaigns to investigate the Doppler lidar performance, compare the backscatter signal and wind profiles, and to optimize the lidar sensitivity through adjusting the telescope focus length and data-integration time to ensure sufficient signal-to-noise ratio (SNR) in low-aerosol-content environments. In terms of statistical characterization, the wind-profile comparison showed good agreement between different lidars. Initially, there was a discrepancy in the SNR and attenuated backscatter coefficient profiles which arose from an incorrectly reported telescope focus setting from one instrument, together with the need to calibrate. After diagnosing the true telescope focus length, calculating a new attenuated backscatter coefficient profile with the new telescope function and taking into account calibration, the resulting attenuated backscatter profiles all showed good agreement with each other. It was thought that harsh Finnish winters could pose problems, but, due to the built-in heating systems, low ambient temperatures had no, or only a minor, impact on the lidar operation – including scanning-head motion. However, accumulation of snow and ice on the lens has been observed, which can lead to the formation of a water/ice layer thus attenuating the signal inconsistently. Thus, care must be taken to ensure continuous snow removal.

Field Measurements of Plow Loads During Ice Removal Operations, HR-334, 1993

One of the more severe winter hazards is ice or compacted snow on roadways. While three methods are typically used to combat ice (salting, sanding and scraping), relatively little effort has been applied to improve methods of scraping ice from roads. In this project, a new test facility has been developed, comprising a truck with an underbody blade, which has been instrumented such that the forces to scrape ice from a pavement can be measured. A test site has been used, which is not accessible to the public, and ice covers have been sprayed onto the pavement and subsequently scraped from it, while the scraping loads have been recorded. Three different cutting edges have been tested for their ice scraping efficiency. Two of the blades are standard (one with a carbide insert, the other without) while the third blade was designed under the SHRP H-204A project. Results from the tests allowed two parameters to be identified. The first is the scraping efficiency which is the ratio of vertical to horizontal force. The lower this ratio, the more efficiently ice is being removed. The second parameter is the scraping effectiveness, which is related (in some as yet unspecified manner) to the horizontal load. The higher the horizontal load, the more ice is being scraped. The ideal case is thus to have as high a horizontal load as possible, combined with the lowest possible vertical load. Results indicate that the SHRP blade removed ice more effectively than the other two blades under equivalent conditions, and furthermore, did so with greater efficiency and thus more control. Furthermore, blade angles close to 0 deg provide for the most efficient scraping for all three blades. The study has shown that field testing of plow blades is possible in controlled situations, and that blades can be evaluated using this system. The system is available for further tests as are deemed appropriate.

Measurement of Ice Scraping Forces on Snow-Plow Underbody Blades, HR-372, 1997

In this study, several new cutting edges for removal of ice from the roadway were tested in a series of closed road tests. These new cutting edges consisted of a variety of serrated shapes. The study also included measurement of ice scraping forces by in-service trucks. These trucks were instrumented in a similar manner as the truck used in the closed-road tests. Results from the closed-road and in-service tests were analyzed by two parameters. The first parameter is the scraping effectiveness, which is defined as the average horizontal force experienced by a cutting edge. The amount of ice scraped from the roadway is directly proportional to the magnitude of the scraping effectiveness. Thus an increase in scraping effectiveness indicates an increase in the amount of ice being scraped from the roadway. The second parameter is force angle, which is defined as tan to the -1 power [vertical force/horizontal force]. A combination of a minimal force angle and a maximized scraping effectiveness represents a case in which the maximal amount of ice is being removed from the pavement without an exceptionally large vertical force. Results indicate that each cutting edge produced a maximal scraping effectiveness with a testing configuration of a 15 deg blade angle and a 23,000 lb. download force. Results also indicate that each cutting edge produced a minimal force angle with a testing configuration of a 15 deg blade angle and a 10,000 lb. download force. Results from the in-service trucks produced similar data and also similar trends within the data when compared to the results of the closed-road tests. This result is most important, as it suggests that the closed-road tests do provide an accurate measure of ice scraping forces for a given blade and configuration of that blade. Thus if the closed-road tests indicate that certain blades perform well, there is now excellent reason to conduct full scale tests of such blades.

An AeroCom assessment of black carbon in Arctic snow and sea ice

Though many global aerosols models prognose surface deposition, only a few models have been used to directly simulate the radiative effect from black carbon (BC) deposition to snow and sea ice. Here, we apply aerosol deposition fields from 25 models contributing to two phases of the Aerosol Comparisons between Observations and Models (AeroCom) project to simulate and evaluate within-snow BC concentrations and radiative effect in the Arctic. We accomplish this by driving the offline land and sea ice components of the Community Earth System Model with different deposition fields and meteorological conditions from 2004 to 2009, during which an extensive field campaign of BC measurements in Arctic snow occurred. We find that models generally underestimate BC concentrations in snow in northern Russia and Norway, while overestimating BC amounts elsewhere in the Arctic. Although simulated BC distributions in snow are poorly correlated with measurements, mean values are reasonable. The multi-model mean (range) bias in BC concentrations, sampled over the same grid cells, snow depths, and months of measurements, are −4.4 (−13.2 to +10.7) ng g−1 for an earlier phase of AeroCom models (phase I), and +4.1 (−13.0 to +21.4) ng g−1 for a more recent phase of AeroCom models (phase II), compared to the observational mean of 19.2 ng g−1. Factors determining model BC concentrations in Arctic snow include Arctic BC emissions, transport of extra-Arctic aerosols, precipitation, deposition efficiency of aerosols within the Arctic, and meltwater removal of particles in snow. Sensitivity studies show that the model–measurement evaluation is only weakly affected by meltwater scavenging efficiency because most measurements were conducted in non-melting snow. The Arctic (60–90° N) atmospheric residence time for BC in phase II models ranges from 3.7 to 23.2 days, implying large inter-model variation in local BC deposition efficiency. Combined with the fact that most Arctic BC deposition originates from extra-Arctic emissions, these results suggest that aerosol removal processes are a leading source of variation in model performance. The multi-model mean (full range) of Arctic radiative effect from BC in snow is 0.15 (0.07–0.25) W m−2 and 0.18 (0.06–0.28) W m−2 in phase I and phase II models, respectively. After correcting for model biases relative to observed BC concentrations in different regions of the Arctic, we obtain a multi-model mean Arctic radiative effect of 0.17 W m−2 for the combined AeroCom ensembles. Finally, there is a high correlation between modeled BC concentrations sampled over the observational sites and the Arctic as a whole, indicating that the field campaign provided a reasonable sample of the Arctic.

Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2015S22, deployed during the Norwegian Young sea ICE cruise (N-ICE 2015) project

Snow height was measured by the Snow Depth Buoy 2015S22, an autonomous platform, drifting on Arctic sea ice, deployed during the Norwegian Young sea ICE cruise (N-ICE 2015) project. The resulting time series describes the evolution of snow depth as a function of place and time between 2015-03-01 and 2015-05-06 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on first year ice. In addition to snow depth, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Negative values of snow depth occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). Records without any snow depth may still be used for sea ice drift analyses.