Optimal RWIS Sensor Density and Location
Development of guidelines for determining the optimal location and density of RWIS to achieve more efficient and effective winter maintenance operations.
Accurate and timely information on road weather and surface conditions (RSC) in winter seasons is a necessity for road authorities to optimize their winter maintenance operations and improve the safety and mobility of the traveling public. One of the primary tools for acquiring this information is road weather information systems (RWIS) that include various environmental and pavement sensors for collecting real-time data on precipitation, pavement temperature, snow coverage, etc. Many transportation agencies have invested millions of dollars in establishing their current RWIS network and continue expanding their network for better winter maintenance decision support and traveller information provision.
While effective in providing real-time information on road weather and surface conditions, RWIS stations are costly to install and operate and therefore can only be deployed at a limited number of locations. Considering the vast road network that often needs to be monitored and the kind of varied road conditions that could occur during winter events, RWIS stations must be placed strategically so that they are collectively most informative in providing the inputs required for accurate estimation of the road weather and surface conditions of the whole highway network.
Thus, the primary goal of this project is to develop a methodology for determining the optimal RWIS sensor density and location over a highway network. In particular, the research has the following specific objectives:
1. Conduct a thorough review on literature related to the characterization, estimation and forecasting of winter road weather and road surface condition (RSC), cost-benefit analysis of RWIS, and methodologies and models for solving location problems;
2. Synthesize the current best practice and guidelines for expanding RWIS network and locating RWIS as well as regular weather stations;
3. Develop a quantitative understanding of spatial and temporal variation of road weather and surface conditions based on both RWIS and local weather data. The key parameters of interest include air temperature, surface temperature, and snow cover,
4. Develop guidelines and an optimization model for determining the the optimal number and location of RWIS sensors for different climate types.
The proposed research will begin in Oct. 2012 and be completed by Oct. 2014.