The MDSS project goal is to develop a decision support tool that provides recommendations on road maintenance courses of action together with anticipated consequences of action or inaction. In order to meet these goals, the prototype MDSS utilizes output from several numerical weather prediction models, includes a road temperature model, rules of practice for anti- and deicing, and a fuzzy logic data fusion system that combines model and observational data. Several national laboratories are participating in this research and development effort. Technology transfer is a major objective of this project; therefore MDSS prototype technologies have been and will continue to be released publicly to allow the private sector to rapidly develop operational versions of the system.
During the user requirements phase of the project, the users strongly indicated that their first priority was to have a system that allowed them to do a better job of strategic planning, focusing on decisions that need to be made 24-48 hours in advance of a weather event. Because of this requirement, the MDSS was designed solely as a prediction system and therefore did not include tactical data such as radar, and satellite. Shortly after the winter 2003 Iowa demonstration began, the users identified the lack of a tactical component as a significant system limitation.
Based on preliminary user feedback and data analysis from the Iowa demonstration, it can be concluded that the winter road maintenance community could benefit greatly if future winter road maintenance decision support systems included a tactical component. A tactical component must include a current analysis of precipitation plus a 0-2-hour nowcast. Required parameters include precipitation type (snow, ice, mixed), precipitation rate (liquid equivalent), snowfall rate, temperature and wind speed. Precipitation rate is a critical parameter for the road temperature model and it has a major influence on determining the optimum treatment plan. Current observational platforms (e.g., ASOS, RWIS) do not adequately measure precipitation rate in real time. Other data sets, sensors or data fusion techniques need to be utilized or developed to address the lack of adequate precipitation rate data.
It is obvious from the preliminary findings of the MDSS project that radar data fused with other local observational data must play a key role in any future tactical decision support system that addresses winter road maintenance.
The paper and presentation will include an overview of the MDSS program, describe the current status of the development effort, present preliminary findings from the field demonstration, and discuss the potential of incorporating radar data in future decision support systems, not only for winter road maintenance, but other surface transportation sectors (e.g., rail, transit, marine).