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Infrastructure Pavement Assessment and Management Applications Enabled by the Connected Vehicles Environment Research Program – Phase I: Proof-of-Concept

Abstract
A fundamental role of transportation agencies is to effectively manage the enormous public investment in pavement. This ranges from developing strategies and systems to periodically assess pavement condition and develop maintenance plans to maximize pavement life within limited budgets, to making tactical decisions regarding treatment during adverse weather conditions to keep roadways functional. A fundamental requirement in this management activity is to collect data to assess the condition of the pavement. The current state-of-the-practice in pavement condition data collection is to use specialized sensors and equipment to support this activity. This represents a significant cost burden on agencies, and also this technical approach to data collection scales poorly. In other words, given the need for specialized equipment and sensors, it is very difficult to collect data at more locations in a timely, cost effective manner. A potential advantage offered by connected vehicles is that this program promises to closely tie the infrastructure to the vast vehicle fleet using the infrastructure. Given the large set of sophisticated sensors integrated in modern vehicles, it is possible that these vehicular sensors may be used as a means to assess pavement conditions. In other words, the entire vehicle fleet can be transformed into probes measuring pavement conditions at all locations in frequent time intervals. The purpose of this collaborative research program between Virginia Tech and the University of Virginia is to conduct the applied research necessary to investigate the feasibility of this concept through component and system prototyping and testing on the Virginia Connected Vehicle UTC testbed. To provide a specific focus to the research program, the work will address 2 specific pavement applications: roughness measurement and friction assessment during snow and rain.

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