Several factors complicate the ability of state agencies to meet motorists expectations for clear and safe roadways:
The expectations of the traveling public and commercial carriers are rising because of increasing need to travel during all hours of the day;
Agencies are constrained to relatively fixed levels of funding and staffing;
Reliable and timely reports of conditions for specific areas can be difficult to obtain;
Certain weather conditions, like blowing and drifting snow, are difficult to forecast;
The response of pavements to changing weather conditions and maintenance treatments are not well established;
Innovative maintenance treatments, such as anti-icing technology, are becoming available, but their effect and effectiveness over the full range of possible conditions are not well understood;
Retiring maintenance staff are being replaced by less experienced workers.
Agencies could provide more effective maintenance, and provide it more efficiently, with the help of an automated Maintenance Decision Support System (MDSS) that could:
assess current road and weather conditions using observations and reasonable inferences based upon observations;
provide time- and location-specific weather forecasts along transportation routes;
predict how road conditions would change due to forecast weather and the application of several candidate road maintenance treatments;
notify state agencies of approaching conditions and suggest optimal maintenance treatments that can be achieved with resources available to the transportation agencies; and
evaluate the reliability of predictions and the effectiveness of applied maintenance treatments for specific road and weather conditions so decision support can be improved.
Research is needed to develop these capabilities in a manner that is technically practical and operationally friendly to maintenance forces. This research could build on states successful efforts to provide better road and weather information through #SAFE and other initiatives.