Applications of Knowledge Discovery in Massive Transportation Data: The Development of a Transportation Research Informatics Platform (TRIP).

Transportation researchers and practitioners have access to unprecedented amounts of data but lack the tools to easily store, manipulate, and analyze these data. The Transportation Research Informatics Platform (TRIP) is an informatics-based system designed to manage massive amounts of transportation data and provide researchers an efficient way to conduct analytics on big data. The objectives of TRIP include creating the ability to handle massive amounts of transportation data; utilize open-source technologies and tools to ingest, store, align, and process data; accept structured, semistructured, and unstructured datasets from any source; provide an efficient way to query data without indepth knowledge of metadata; integrate with open-source and consumer off-the-shelf analytics products; and provide visualization tools to offer greater insights into data. TRIP architecture is flexible and built on opensource state-of-the-art technology developed with big data in mind. Although predominantly developed for transportation safety research, TRIP is domain agnostic and capable of addressing issues pertaining to operations and maintenance given the ingestion of the appropriate datasets

Applications of Knowledge Discovery in Massive Transportation Data: The Development of a Transportation Research Informatics Platform (TRIP).

Transportation researchers and practitioners have access to unprecedented amounts of data but lack the tools to easily store, manipulate, and analyze these data. The Transportation Research Informatics Platform (TRIP) is an informatics-based system designed to manage massive amounts of transportation data and provide researchers an efficient way to conduct analytics on big data. The objectives of TRIP include creating the ability to handle massive amounts of transportation data; utilize open-source technologies and tools to ingest, store, align, and process data; accept structured, semistructured, and unstructured datasets from any source; provide an efficient way to query data without indepth knowledge of metadata; integrate with open-source and consumer off-the-shelf analytics products; and provide visualization tools to offer greater insights into data. TRIP architecture is flexible and built on opensource state-of-the-art technology developed with big data in mind. Although predominantly developed for transportation safety research, TRIP is domain agnostic and capable of addressing issues pertaining to operations and maintenance given the ingestion of the appropriate datasets