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Leveraging Big Data to Improve Traffic Incident Management

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"Big data" is not new, but applications in the field of transportation are more recent, having occurred within the past few years, and include applications in the areas of planning, parking, trucking, public transportation, operations, ITS, and other more niche areas. A significant gap exists between the current state of the practice in big data analytics (such as image recognition and graph analytics) and the state of DOT applications of data for traffic incident management (TIM) (such as the manual use of Waze data for incident detection).

The term big data represents a fundamental change in what data is collected and how it is collected, analyzed, and used to uncover trends and relationships. The ability to merge multiple, diverse, and comprehensive datasets and then mine the data to uncover or derive useful information on heretofore unknown or unanticipated trends and relationships could provide significant opportunities to advance the state of the practice in TIM policies, strategies, practices, and resource management.

NCHRP (National Cooperative Highway Research Program) Report 904: Leveraging Big Data to Improve Traffic Incident Management illuminates big data concepts, applications, and analyses; describes current and emerging sources of data that could improve TIM; describes potential opportunities for TIM agencies to leverage big data; identifies potential challenges associated with the use of big data; and develops guidelines to help advance the state of the practice for TIM agencies.


Suggested Citation

National Academies of Sciences, Engineering, and Medicine. 2019. Leveraging Big Data to Improve Traffic Incident Management. Washington, DC: The National Academies Press.

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Publication Info

204 pages | 8.5 x 11

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