This report describes a new data warehouse model developed for integrating Road Weather Information System (R/WIS) and traffic data and the prototype implemented. The building blocks of the prototype include data aggregation methods from sensors, a data archiving system, and multi-user data access and retrieval environments through a network. This new data warehouse model seamlessly integrates the heterogeneous nature of R/WIS and traffic data. The key to this data model was utilization of a network storage model referred to as a parallel First-In-First-Out (FIFO) data storage where various sensor data are deposited as they are aggregated while different types of data-consuming modules obtain data without an explicit protocol requirement. For the prototype implementation, four different data aggregation methods from traffic and R/WIS sources were used to demonstrate that diverse data types and collection methods could be seamlessly integrated together. As an application of this data warehouse, weather impact on traffic flow was studied by retrieving traffic data under various atmospheric and pavement conditions, and the results are included. It was noticed that R/WIS provides a significant advantage over the traditional National Weather Service data in learning detailed location specific weather and pavement conditions from which weather impact on traffic flow could be accurately analyzed.
Kwon, Taek Mu.
Development of Efficient Integrated Data Archival/Retrieval Model for R/WIS, RTMS, and Loop Traffic Data.
Retrieved from the University of Minnesota Digital Conservancy,
Content distributed via the University of Minnesota's Digital Conservancy may be subject to additional license and use restrictions applied by the depositor.