Institute for Transportation

About the research

This project pursued a large-scale effort to deploy non-invasive sensors adjacent to invasive sensors (embedded in the pavement) located at existing road weather information system (RWIS) stations and to consider agency suitability between the different sensors. While some RWIS stations may have multiple invasive sensors measuring pavement temperature at various locations (e.g., bridge deck and approach), this deployment was unique in that both the invasive and non-invasive sensors were measuring the same, proximate physical locations.

Within this effort, the project team was responsible for identifying the non-invasive sensors on the market, purchasing and distributing the compatible devices and necessary auxiliary equipment to participating Aurora member states and, once installed, assimilating agency experiences and establishing access, if possible, to the sensor data for comparison and visual presentation. The participating Aurora agencies were responsible for site selection, sensor calibration, installation, and maintenance.

In general, many participating states provided positive feedback with respect to non-invasive sensors and their reported data. Some of the challenges that were shared included identifying a suitable installation location due to sensor specifications, initial sensor operation, and integration and data retrieval.

As a result of this experience, some participating state departments of transportation (DOTs) have decided to adopt non-invasive sensors, expand their deployment of them, or even consider applications beyond those planned with this project. While this project initially targeted pavement surface temperature, one participating agency with limited non-invasive sensor experience is planning on statewide deployment for real-time friction measurements for use in agency decision making.

The project allowed participating agencies to work with new vendors, creating an opportunity to evaluate the different products, encounter potential issues, and identify possible solutions through a low-risk environment. This effort will support future research on both pavement temperatures and friction across the US based on data from the same makes and models of non-invasive equipment.

About the research

Atmospheric rivers (ARs) are severe winter storms affecting the West Coast of the US. ARs decrease the safety of roadways, bringing heavy rainfall and winds, ice, and snow to the roads and increasing crashes, delays, and travel time.

This project included a literature review; developed a methodology to estimate the impacts of ARs on traffic, crashes, and road closures; applied the methodology to test sites in California, Colorado, and Utah; and estimated the direct costs of these impacts.

The California case study quantified the impacts of ARs on traffic volumes and vehicle miles traveled from 1996 to 2019 on I-5 from San Ysidro to the Oregon border.

The Colorado case study quantified the impacts of ARs on crashes, road closures, and delays during the severe avalanche month of March 2019 on 84 miles of I-70 west of Denver.

The Utah case study quantified the impacts of ARs on crashes, road closures, and delays from 2012 to 2019 at four sites: I-70 at Clear Creek Canyon, I-80 at Parley’s Canyon, US 6 from Spanish Fork to Helper, and US 91 from Brigham City to Wellsville.

ARs were found to have significant impacts on crashes, road closures, delays, and traffic flows.

About the research

Road weather information systems (RWIS) have long been regarded as one of the most advanced technologies for monitoring road surface conditions (RSCs) during the winter season. While RWIS provide information essential for winter road maintenance (WRM) services, they can only be implemented at select areas largely due to budgetary constraints. It is therefore indispensable to fill large spatial gaps that exist between RWIS stations to promote safer driving conditions and lower the cost of WRM activities. Furthermore, most RWIS stations nowadays are equipped with cameras that provide users with a direct view of the road conditions being covered; however, checking RSCs via these cameras is still being done manually, which hinders the full utilization of the rich image-based road condition data.

To help tackle these challenges, this project aimed to develop a systematic, yet transferrable, method for estimating key RSC variables (i.e., road surface temperature and slipperiness) between RWIS stations using large-scale data and two advanced modeling techniques—kriging and deep learning (DL). Road surface temperatures, dash camera images, and remotely sensed data collected along selected Iowa interstate highway segments between October 2018 and April 2019 were used to develop the models for estimating RSCs. A total of 262 hourly events and more than 10,000 images were processed and utilized for model development.

The findings suggest that the proposed kriging method is able to capture the general RSC pattern along the highway stretch with as few as one RWIS input. In addition, the DL model developed in this study showed promising performance in automatically classifying dash camera images. The road condition images labeled by the DL model were later used for road slipperiness estimations between existing RWIS stations. Although additional data sets would be required to further confirm the validity of the developed models and the conclusiveness of the results documented herein, the proposed method will undoubtedly provide decision makers with a tool that helps to implement WRM activities more quickly, efficiently, and cost effectively.

About the research

The purpose of this research is to outline a path forward for the creation of a working group of experts to serve to advance the state-of-the-practice of weather severity indices (WSI).

To accomplish this task, the following will be completed:

1. Create a white paper on the current state-of-the-practice
2. Identify issues with WSIs
3. Identify lessons learned in the process of development

These lessons learned will serve as the basis for identifying individuals and agencies to be invited to participate in the working group. Finally, the effort will result in the coordination and assembly of a volunteer working group to review the state-of-the-practice of WSIs, known issues, and work to identify methods to solve these issues. 

About the research

Life-cycle cost analysis (LCCA) is a data-driven tool that provides a detailed account of the total costs of a project over its expected life. LCCA has been proven to create short-term and long-term savings for transportation agencies by helping decision-makers identify the most beneficial and cost-effective projects and alternatives. To help state departments of transportation (DOTs) make more informed decisions with regard to budget planning for the various costs associated with the use of a road weather information system (RWIS), the Aurora Pooled Fund Program initiated the RWIS Life-Cycle Cost Analysis research project. The objectives of this research were to develop guidelines to do the following:

• Help quantify the costs and benefits associated with RWIS sites
• Better assess costs arising from RWIS assets over the life cycle
• Provide a framework for calculating net present worth
• Assess alternatives and associated cost implications
• Determine long-term RWIS life-cycle costs and the optimal point to replace RWIS equipment
• Support decisions on repair versus replacement based on projected expenses
• Assist in planning and funding the replacement or repair of RWIS infrastructure

This report provides methods and general guidelines to assist public agencies with determining RWIS site life-cycle costs. Public agencies can follow the information provided herein to gather necessary data and perform the analysis to help quantify the costs and benefits associated with RWISs. The methodologies presented in this report provide a framework for calculating life-cycle costs and net present worth, which helps agencies make more informed decisions in repairs and replacement of RWIS sites. It also helps assess and compare alternatives and associated cost implications.

Tool Development

This effort took the results from the RWIS Life-Cycle Cost Analysis findings and Task 6 final report to create an interactive tool. The purpose was to provide agencies a practical tool for performing life-cycle cost analysis for their RWIS. This interactive tool was developed in the form of Microsoft Excel to provide convenience and easy access to agencies. The interactive tool includes the following:

• An instruction sheet that introduces the tool and provides instructions on using the tool.
• A database that can store average cost information for RWIS components. Average costs of RWIS and its components were collected from one selected agency. The Aurora Program through the Institute for Transportation at Iowa State University collected the data and other agency input variables. Data already collected through the RWIS Life-Cycle Cost Analysis project was also used to supplement this database. The costs include breakdown of capital equipment, installation, maintenance, upgrade, and replacement costs.
• A data input feature that allows users to search the database to find and select the information and parameters for performing the analysis.
• A manual data entry feature that allows users to input tailored agency data for the analysis.
• An annualized cost calculation worksheet(s).
• A benefits/savings estimation worksheet(s).
• A summary result sheet that presents basic parameters used in the analysis, calculated annualized cost, life-cycle cost, net present worth, and expected benefit-cost ratio.

The final interactive tool is available to Aurora program members.

About the research

Preventing weather-related crashes is a significant part of maintaining the safety and mobility of the travelling public during winter months. A road weather information system (RWIS) is a combination of advanced technologies that collect, process, and disseminate road weather and condition information. This information is used by road maintenance authorities to make operative decisions that improve safety and mobility during inclement weather events. Many North American transportation agencies have invested millions of dollars to deploy RWIS stations to improve the monitoring coverage of winter road surface conditions. However, the significant costs of these systems motivate governments to develop a framework to optimize the spatial design of the RWIS network. The design of these networks often varies by region, and it remains an unresolved question what should be the optimal density and location of an RWIS network to provide adequate monitoring coverage of a given region.

To fill this gap, this project aimed to develop a methodology for optimizing the density and location of an RWIS network for a given region based on its topographic and weather characteristics. A series of geostatistical spatiotemporal semivariogram models were constructed and compared using topographic position index (TPI) and weather severity index (WSI) to measure relative topographic variation and weather severity, respectively. Specifically, this project considered the nature of spatiotemporally varying RWIS measurements by integrating larger case studies and examining two analysis domains: space and time. The study area captured varying environmental characteristics, including regions with flatland or varied terrain and different severities of winter weather. The optimal RWIS density and location for different topographic and weather severity regions were determined using spatiotemporal semivariogram parameters. Output of this study revealed a strong dependency of optimal RWIS density on topographic and weather characteristics of a region. Moreover, this study suggests that RWIS data collected from a specific region can be used to estimate the number of stations required for regions with similar zonal characteristics. The proposed method will provide decision-makers with a tool they need to develop a long-term RWIS implementation plan.

About the research

Objective

The purpose of this research was to survey road authorities on their data collection and retention practices and to share the findings with Aurora member agencies. A survey was undertaken of road authorities across the United States of America, Canada, and some European organizations regarding their data collection practices for road weather information systems (RWIS), automated vehicle location (AVL) / global positioning systems (GPS), camera images, and traffic data. The results of this survey can be used by Aurora members to assess their data collection practices with respect to other road authorities.

About the research

The major tasks of Phase II were to implement the models recommended from Phase I at demonstration sites and to calibrate those models, if required. The models included both degree-day threshold and frost-thaw depth prediction models. Output from the models was then compared with validation data provided by the departments of transportation (DOTs) involved in this study. These validation data consisted of subsurface temperature data (which were reduced by the research team to compute frost and thaw depths) and, in some cases, deflection and/or stiffness data from lightweight deflectometer (LWD) and falling weight deflectometer (FWD) tests.

With the results of these implementation and validation efforts, recommendations based on accuracy, simplicity of use, and cost were developed to aid road management agencies in selecting which model or protocol is most appropriate for their intended purposes, personnel, and specific conditions.