Surface Transportation Weather

BACKGROUND

RAL is a key contributor to road weather research across the US, including connected vehicle and winter maintenance support applications in multiple states and airports. Based around the Maintenance Decision Support System (MDSS) and the Pikalert® System, which were developed in conjunction and with support of the United States Department of Transportation’s (USDOT) Federal Highway Administration (FHWA), RAL’s road weather research program continues to engage with stakeholders in the public and private sectors to advance road weather research.

FY2019 Accomplishments

Figure 1: Updated Pikalert display over Iowa.
Figure 1. Updated Pikalert display over Iowa.

Pikalert®

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 FY19, RAL continued to enhance the Pikalert system with our partner states. These enhancements included tuning of the system running over Alaska, Iowa, Wyoming, Nevada, and Colorado as well as a new web-based display taking advantage of new display technology and based on stakeholder feedback.

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. System enhancements in FY19 included extension of the corridors covered north to Nome along with an update to the underlying model, where DICast location-based forecasts are expected to improve accuracy over the former grid-based system.

Figure 2: Mobile display forecast for a runway at Denver International Airport.
Figure 2: Mobile display forecast for a runway at Denver International Airport.

The WYDOT CV Pilot

The Wyoming DOT (WYDOT) Connected Vehicle Pilot is a USDOT-funded initiative to move developed Connected Vehicle technologies out of the research arena and into operational deployment. While technical deployment is delayed due to issues with vehicle and roadside hardware, RAL continues to work closely with WYDOT personnel and on-site meteorologists to tune the Pikalert® system, which is providing hazard assessments to the Traffic Management Center for updating their Traveler Information Messaging. RAL also began a supplemental project with WYDOT to tune the Pikalert system for the state.

Nevada DOT

RAL worked closely with Nevada DOT to produce the updated display capability discussed above under the Pikalert® update. RAL also continued tuning of the system to meet NDOT’s needs.

Iowa DOT

RAL stood up a Pikalert instance for the state of Iowa, covering major roadways across the state 

Minneapolis and Denver Airport MDSS and Friction

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 FY19, RAL continued to support the MDSS at Denver and  Minneapolis airports to support the 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. A major advancement in FY19 was the development of a mobile display in conjunction with airport stakeholders (Figure 2).

FY2020 Goals

RAL plans to continue developing Pikalert® with current and future state partners to provide high quality tactical and forecast weather hazard information in support of DOT operations and traveler information messaging. FY20 goals also include expansion of decision support services for transportation outside of winter, including flooding, fire, and summer-related hazard decision support.