The scale of the data, the concepts, and the paradigm shift that is necessary to move from traditional data collection, storage, and analytics to use Big Data poses new challenges for transportation practitioners. This shift is not simply a linear one; rather, it requires completely new approaches to data collection, storage and management, and procurement of information technology services, as well as skill sets that most transportation agencies lack and are difficult to acquire. Furthermore, few agencies are using Big Data data sets to demonstrate the benefits of this approach, particularly for TIM.
The recently completed project NCHRP Research Report 904: Leveraging Big Data to Improve Traffic Incident Management, (https://www.trb.org/Main/Blurbs/179756.aspx) begins to address these issues. Big Data applications related to the emergence of connected vehicle, traveler, and infrastructure data will soon drive this change. One of the findings from the report is that in order to gain benefits from Big Data approaches and analytics, a scalable solution is essential. Research is necessary to document issues and demonstrate the feasibility and value of Big Data approaches for use by state departments of transportation (DOTs) and other agencies to enhance operations and TIM programs.
The objective of this research is to (1) demonstrate the feasibility and practical value of Big Data approaches to improve TIM, and (2) provide guidelines, including techniques and tools, to address the findings and recommendations of NCHRP Research Report 904: Leveraging Big Data to Improve Traffic Incident Management.