, Professor, UMD-Electrical Engineering
In the past, Road/Weather Information Systems (R/WIS) data and traffic data have mostly been managed in isolation, and thus the benefits attainable by correlating both types of data have not been realized. This project developed a new data warehouse model for integrating Road Weather Information System (R/WIS) and traffic data and implemented a prototype. 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 these 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 can be accurately analyzed.