Surface Transportation Weather

Background

RAL is a key contributor to the research and development of the weather component of the Federal Highway Administration’s wireless Connected Vehicle program, which has been implemented in several states. RAL also continues to support the adoption of the winter Maintenance Decision Support System (MDSS) technology across the national and to enhance and integrate weather into transportation decision support systems.

FY2018 Accomplishments

Pikalert®

Figure 1: Pikalert® display over Nevada
Figure 1: Pikalert® display over Nevada

The Connected Vehicle program is focused on improving safety, mobility, and environmental efficiency. Connected Vehicle technologies can provide data from millions of vehicles (including weather observations) that will be available to support both road weather applications and the wider weather community. RAL partners with multiple state Departments of Transportation (DOTs) to implement a Pikalert System (Figure 1) for their area of operation. Pikalert incorporates vehicle-based observations of the road and surrounding atmosphere with other, more traditional weather data sources (including weather radar and road-side weather observation stations). The vehicle data are quality checked and the fused vehicle and weather data are used for current weather assessments and forecasts of road weather conditions out to 72 hours. 

In FY2018, RAL continued to enhance the Pikalert system with our partner states. These enhancements included further implementation of the blowover algorithm and improvements to the web-based display.

Enhanced Products for Alaska

RAL continued its collaboration with the Alaska DOT to maintain the current Pikalert system across the state. Positive feedback was received from maintenance managers, who integrate use of the system into their daily activities. RAL will continue to partner with Alaska to enhance the system for use in the unique microclimates of the state.

The WYDOT CV Pilot

Figure 2: Pikalert integration into TMC operations
Figure 2: Pikalert integration into TMC operations

RAL concluded the second phase of the Wyoming DOT (WYDOT) Connected Vehicle Pilot, which is a USDOT-funded initiative to move developed Connected Vehicle technologies out of the research arena and into operational deployment. During FY2018, RAL completed the development phase with the WYDOT team to transition into the deployment phase. This included maintaining and enhancing Pikalert as required, working closely with WYDOT staff and onsite meteorologist to ensure the system was useful for WYDOT operations, and improving the blowover algorithm. The Pikalert assessments are currently being used in the Traffic Management Center to verify the assessment accuracy and assist operators in maintaining the road weather information products of WYDOT (Figure 2).  

Nevada DOT

In previous years, RAL partnered with Nevada DOT (NDOT) to deploy Connected Vehicles in NDOT’s fleet and deploy a Pikalert system across the state. In FY18, RAL began a new phase of work with NDOT that will involve additional Connected Vehicle data integration into Pikalert and enhancements to the system. The blowover algorithm was deployed as part of these enhancements, and NDOT blowover crash data will be used to enhance the accuracy of the product.

Minneapolis and Denver Airport MDSS and Friction

Figure 3: Runway Closure Matrix
Figure 3: Runway Closure Matrix

RAL is working with the international airports in Minneapolis and Denver to improve runway decision support. Adverse winter weather can significantly disrupt airport operations both in relation to aircraft safety and visibility as well as runway friction and surface conditions. At Denver, the MDSS is configured across all major runways and uses known pavement information and rules of practice for winter maintenance to assist in chemical and plow application and deployment. During FY18, RAL continued to support the MDSS at Denver and began work on improving the display system based on user feedback. RAL has also partnered with the Minneapolis airport in FY18 to deploy a Runway Friction and Closure Prediction System (RFCPS), which relies on data processing and machine learning techniques developed in RAL to combine a weather forecast with maintenance rules of practice to predict runway friction and runway closure alerts (Figure 3).