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Project Details
STATUS

Completed

PROJECT NUMBER

2010-04

START DATE

01/01/16

END DATE

06/14/16

SPONSORS

Federal Highway Administration Aurora Program Transportation Pooled Fund (TPF SPR-3(042))

Researchers
Principal Investigator
Tae J. Kwon

Department of Civil & Environmental Engineering, University of Alberta

Principal Investigator
Liping Fu

Innovative Transportation System Solutions (iTSS) Lab, Department of Civil & Environmental Engineering, University of Waterloo

About the research

Problem Statement

Road authorities rely on accurate and timely road weather and surface condition information provided by road weather information systems (RWIS) to optimize winter maintenance operations and improve the safety and mobility of the traveling public. However, RWIS stations are costly to install and operate and therefore must be placed strategically to accurately monitor the entire highway network. Few guidelines are available for optimizing RWIS networks and thus maximizing return on investment.

Objective

This project developed several approaches for determining the optimal location and density of RWIS stations over a regional highway network. To optimize locations, three approaches were developed: surrogate measure-based, cost-benefit-based, and spatial inference-based. The surrogate measure-based method prioritizes locations that have the highest exposure to severe weather and traffic. The cost-benefit-based method explicitly accounts for the potential benefits of an RWIS network in terms of reduced collisions and maintenance costs. The spatial inference-based method maximizes the use of RWIS information to optimize the configuration of an RWIS network. To optimize network density, a cost-benefit-based method and a spatial inference-based method were developed. To demonstrate the applications of the proposed approaches and evaluate existing RWIS networks, four case studies were conducted using data from one Canadian province (Ontario) and three US states (Minnesota, Iowa, and Utah).

Results

It was found that all approaches can be conveniently implemented for real-world applications. The approaches provide alternative ways of incorporating key road weather, traffic, and maintenance factors to optimize the locations and density of RWIS stations in a region; the alternative to use can be decided based on the data and resources available.

Project Details
STATUS

Completed

PROJECT NUMBER

2015-01

START DATE

12/01/14

END DATE

10/31/16

SPONSORS

Alaska Department of Transportation
Federal Highway Administration Aurora Program Transportation Pooled Fund (TPF SPR-3(042))

Researchers
Principal Investigator
National Center for Atmospheric Research

About the research

Problem Statement

In January 2015, personnel from the National Center for Atmospheric Research (NCAR) in Boulder, Colorado installed precipitation measurement sensors at two locations in Juneau, Alaska: one along Thane Road south of Juneau and another on top of Mt. Roberts at an existing weather station site. Both of these sites are part of the Alaska Department of Transportation and Public Facilities (DOT&PF) Road Weather Information System (RWIS). These two sites are separated by a straight two-dimensional, Euclidean distance of just more than 1 mile, but the altitude increases from the Thane Road location to the Mt. Roberts location by more than 500 meters (actually, by more than 1,732 feet). The research team conducted a study using data collected from the Juneau, Alaska sites and the NCAR Marshall Field test site south of Boulder, Colorado to evaluate Vaisala present weather detector (PWD) parameter settings.

Objective

The objectives of this project were to (1) assess the PWD12 performance for measuring LWE under various scenarios, (2) provide analysis on the observed differences reflecting on how they might impact avalanch hazard assessment, and (3) develop sensor recommendations.

Results

The results indicated that, overall, the Vaisala PWD sensors underestimated snowfall precipitation rates compared to Hotplate results. Additionally, the snowfall rate underestimate was more significant at higher snowfall rates.

Implementation

Based on the data analyses, this study provided the Alaska DOT&PF with a corrected estimated rate, which could be used by other organizations, for snow events in Juneau to correct for the bias from the PWD sensors and new weather parameter settings, which were implemented.

Project Details
STATUS

Completed

PROJECT NUMBER

2015-04

START DATE

06/01/16

END DATE

08/01/17

SPONSORS

Federal Highway Administration Aurora Program Transportation Pooled Fund (TPF SPR-3(042))

Researchers
Principal Investigator
Richard W. Wies

Associate Professor of Electrical and Computer Engineering, Institute of Northern Engineering, University of Alaska-Fairbanks

About the research

Problem Statement

The deployment of different alternative power sources and low-power sensors and equipment packages for remote (off-grid) road weather information system (RWIS) sites in the Aurora Program states in recent years has resulted in a number of system configurations and operational strategies.

Objective

This report provides a comprehensive review, investigation, and analysis of alternative power sources and power budgets for sensors and associated components used in remote RWIS applications.

Results

Through a literature review, investigation of recent developments, and a survey of the Aurora Program states, this study explored alternative power sources and power budgets of sensors and associated components and provides recommendations on existing remote RWIS configurations and methodologies. The study found that a variety of alternative power sources, low-power sensors, and associated equipment are currently available for remote RWIS applications. The survey results showed that a combination of fossil fuel-based and renewable power sources tied to a battery bank are employed as a viable means of reliable year-round operation of remote RWIS sites. The survey results also showed that many of the remote RWIS sites are using weather sensors, cameras, and associated equipment with a much higher power budget than products currently available on the market. These findings suggest that the reliability and efficiency of some remote RWIS sites could potentially be improved through the deployment of low-power sensors and associated equipment combined with alternative power sources.

Project Details
STATUS

Completed

PROJECT NUMBER

2012-02

START DATE

01/01/12

END DATE

10/01/12

SPONSORS

Federal Highway Administration Aurora Program Transportation Pooled Fund (TPF SPR-3(042))
Iowa Department of Transportation

Researchers
Principal Investigator
Tina Greenfield

RWIS Coordinator, Iowa Department of Transportation

About the research

Problem Statement

Weather indices are powerful tools that can help observers quantify the severity of a winter season and allow comparison of the severity from one region or year to another. Such comparisons provide very useful information for those studying winter maintenance costs or other department of transportation (DOT) functions that are influenced by weather. While weather indices can be powerful tools, they must be used appropriately. An agency must select the right weather index for the job and have the data needed to compute the chosen index.

Objective

This guide describes weather indices in general, shows how they are used and misused, refers to some existing indices and identifies the data needed for computations, and presents an example showing how to use an index to understand other winter maintenance issues.

Project Details
STATUS

Completed

PROJECT NUMBER

2013-04

START DATE

01/01/13

END DATE

05/01/13

SPONSORS

Federal Highway Administration Aurora Program Transportation Pooled Fund (TPF SPR-3(042))

Researchers
Principal Investigator
Laura Fay

Research Scientist, Western Transportation Institute, Montana State University

About the research

Problem Statement

Winter maintenance operations typically involve the application of salts to roadways to combat the formation of ice or snow pack on the road surface. Precise knowledge of pavement conditions, especially the amount of salt remaining on the pavement surface, is needed to maximize the benefits and reduce the negative effects of road salt usage. Salinity sensing technologies are effective solutions to meet such needs.

Objective

The focus of this phase of the research was to report on available mobile salinity measurement technologies. Technologies were identified through a literature search, a review of patents, information provided by vendors and manufacturers, survey responses, and follow-up interviews.

Results

Three types of salinity sensors were identified: in-pavement sensors, portable sensors, and vehicle-mounted sensors. Seven mobile salinity sensors were identified as potential candidates for Phase II field trials.

Project Details
STATUS

Completed

PROJECT NUMBER

2014-02

START DATE

06/01/16

END DATE

06/01/18

SPONSORS

Federal Highway Administration Aurora Program Transportation Pooled Fund (TPF SPR-3(042))
WTI

Researchers
Principal Investigator
Laura Fay

Research Scientist, Western Transportation Institute, Montana State University

About the research

This project presents the laboratory findings from an evaluation of the Lufft MARWIS and the Teconer RCM411 sensors. The study focused on each sensor’s ability to detect water film/ice depth (testing occurred at 28°F and -20°F), friction (testing occurred at 28°F), surface temperature (testing occurred at 28°F), and road condition/surface state (testing occurred at 28°F).

The objectives of this project were to evaluate the ability of the Lufft MARWIS and Teconer RCM411 sensors to perform in a typical winter maintenance environment, investigate the sensors’ sensitivity to varying chloride concentrations, assess the repeatability of data and the sensors’ mechanical reliability, and evaluate each sensor’s state of development and cost to purchase, install, and maintain. The laboratory testing simulated real-world conditions of snow, ice, and/or slush on pavement, trafficking, and plowing and assessed how each sensor performed with the addition of each of these variables and detected the change in pavement condition/deicer performance throughout winter maintenance operations. The testing conditions were ideal in that they were consistent and reproducible.

For both sensors, the response time for data reporting was almost immediate (0 to 4 seconds), and the sensors detected greater water depth on the concrete samples than on the asphalt samples. Based on the performance of the road condition rating (MARWIS) and surface state rating (Teconer RCM411) features, it is safe to say that for both sensors this feature is a dynamic tool that can be used to determine the road condition remotely.

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