RWIS Sensor Density and Location

Project Details









Federal Highway Administration Aurora Program Transportation Pooled Fund (TPF SPR-3(042))

Principal Investigator
Tae J. Kwon

Department of Civil & Environmental Engineering, University of Alberta

Principal Investigator
Liping Fu

Innovative Transportation System Solutions (iTSS) Lab, Department of Civil & Environmental Engineering, University of Waterloo

About the research

Problem Statement

Road authorities rely on accurate and timely road weather and surface condition information provided by road weather information systems (RWIS) to optimize winter maintenance operations and improve the safety and mobility of the traveling public. However, RWIS stations are costly to install and operate and therefore must be placed strategically to accurately monitor the entire highway network. Few guidelines are available for optimizing RWIS networks and thus maximizing return on investment.


This project developed several approaches for determining the optimal location and density of RWIS stations over a regional highway network. To optimize locations, three approaches were developed: surrogate measure-based, cost-benefit-based, and spatial inference-based. The surrogate measure-based method prioritizes locations that have the highest exposure to severe weather and traffic. The cost-benefit-based method explicitly accounts for the potential benefits of an RWIS network in terms of reduced collisions and maintenance costs. The spatial inference-based method maximizes the use of RWIS information to optimize the configuration of an RWIS network. To optimize network density, a cost-benefit-based method and a spatial inference-based method were developed. To demonstrate the applications of the proposed approaches and evaluate existing RWIS networks, four case studies were conducted using data from one Canadian province (Ontario) and three US states (Minnesota, Iowa, and Utah).


It was found that all approaches can be conveniently implemented for real-world applications. The approaches provide alternative ways of incorporating key road weather, traffic, and maintenance factors to optimize the locations and density of RWIS stations in a region; the alternative to use can be decided based on the data and resources available.