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Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2019. Leveraging Big Data to Improve Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/25604.
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Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2019. Leveraging Big Data to Improve Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/25604.
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Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2019. Leveraging Big Data to Improve Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/25604.
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Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2019. Leveraging Big Data to Improve Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/25604.
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Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2019. Leveraging Big Data to Improve Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/25604.
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Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2019. Leveraging Big Data to Improve Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/25604.
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Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2019. Leveraging Big Data to Improve Traffic Incident Management. Washington, DC: The National Academies Press. doi: 10.17226/25604.
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125 Alvarez, P. 2015. “Big Data Helps Pedestrian Planning Take a Big Step Forward.” Transportation Professional. Barrett, Byrd Associates, Tunbridge Wells, Kent, UK. Amazon. 2017. “Amazon Rekognition.” AWS. Webpage: https://aws.amazon.com/rekognition/ (accessed June 2017). Amazon Web Services. n.d. “C-SPAN Case Study.” AWS Website: https://aws.amazon.com/solutions/case-studies/ cspan/ (accessed July 24, 2018). American Automobile Association. 2017. AAA Digest of Motor Laws. Webpage: http://drivinglaws.aaa.com/tag/ move-over-law/ (accessed May 15, 2017). Anderson, J. M., N. Kalra, K. D. Stanley, P. Sorensen, C. Samaras, and O. A. Oluwatola. 2016. Autonomous Vehicle Technology. Santa Monica, CA: RAND Corporation. Available online: http://www.rand.org/content/dam/ rand/pubs/research_reports/RR400/RR443-2/RAND_RR443-2.pdf. APCO International. 2010. High Priority Information Sharing Needs for Emergency Communications and First Responders. Association of Public Safety Communications Officials, Daytona Beach, FL. Available online: https://www.apcointl.org/doc/911-resources/375-high-priority-info-sharing-needs-for-emerg-comm- and-first-responders-final-pdf/file.html. Applied Engineering Management Corp. and toXcel, LLC. 2018. NCHRP Research Report 865: Guidance for Development and Management of Sustainable Enterprise Information Portals. Transportation Research Board, Washington, D.C. Automotive Supply Chain. 2016. Automotive Supply Chain. Webpage: http://automotivesupplychain.org/supply- chain/crown-commercial-service-appoints-ald-automotive-to-vehicle-telematics-framework-agreement/ (accessed July 2018). Baltimore Metropolitan Council. 2017. “Traffic Incident Management Committee” webpage: https://baltometro. org/community/committees/traffic-incident-management-committee (accessed June 2017; url updated May 2019). Barichello, K., and S. Knickerbocker. 2017. “Using INRIX Data in Iowa.” www.mtmug.org.Webpage: http:// www.mtmug.org/Presentations/rev_INRIX%20presentation%20MTMUG.pdf (accessed May 15, 2017). Barrachina, J., P. Garrido, M. Fogue, and F. J. Martinez. 2012. “VEACON: a Vehicular Accident Ontology Designed to Improve Safety on the Roads.” Journal of Network and Computer Applications. Available online: https://www.researchgate.net/publication/236842047_Efficient_Regression_Testing_of_Ontology-Driven_ Systems. BDE n.d. “Big Data Europe Empowering Communities with Data Technologies—Integrating Big Data, Software, and Communities for Addressing Europe’s Societal Needs.” Big Data Europe. Webpage: https://drive.google. com/file/d/0By9UYHuCedbmcjJKZF9YQkROcG8/view (accessed June 10, 2017). BDE and ERTICO-ITS Europe. 2015. Big Data Europe for Smart, Green and Integrated Transport. First Workshop Report, ITS Conference (Bordeaux, Oct. 7, 2015). Beach, J. 2014. “Big Data & Trucking.” Trucking Info. Webpage: http://www.truckinginfo.com/article/ story/2014/12/big-data-tracking.aspx (accessed November 2016). Birenbaum, I., C. Creel, and S. Wegmann. 2009. Traffic Control Concepts for Incident Clearance. FHWA- HOP-08-057. Office of Transportation Operations, Federal Highway Administration, U.S. Department of Transportation, Washington, D.C. Available online: https://ops.fhwa.dot.gov/publications/fhwahop08057/ fhwahop08057.pdf. Bonetti, P. 2013. “How to Really Outsmart Traffic.” HERE 360. Webpage: http://360.here.com/2013/07/09/ how-to-really-outsmart-traffic/ (accessed June 2017). Bostock, M. 2015. “Bivariate Hexbin Map—Released Under the GNU General Public License, Version 3.” Mike Bostock’s Blocks. Webpage: https://bl.ocks.org/mbostock/4330486 (accessed October 19, 2018). References

126 Leveraging Big Data to Improve Traffic Incident Management Branch, A. 2016. “Victims ID’d in Fatal Quassy Amusement Park Crash: Report.” Patch. Webpage: https:// patch.com/connecticut/woodbury-middlebury/victims-idd-fatal-quassy-amusement-park-crash-report (accessed July 2018). Brickley, D. n.d. “W3C Semantic Web Interest Group.” W3C. Webpage: https://www.w3.org/2003/01/geo/. Broad, E. (2015) “Closed, Shared, Open Data: What’s in a Name?” Blog (September 17, 2015): http://oldsite. theodi.org/blog/closed-shared-open-data-whats-in-a-name. Brooke, K., K. Dopart, T. Smith, A. Flannery. 2004. NCHRP Report 520: Sharing Information Between Public Safety and Transportation Agencies for Traffic Incident Management. Transportation Research Board of the National Academies, Washington, D.C. BTS. 2011. “Clarus.” Bureau of Transportation Statistics. Webpage: https://ntl.bts.gov/lib/44000/44300/44374/ FHWA-JP0-11-154_Clarus_Overview_final.pdf (accessed February 2017). Burt, M., M. Cuddy, and M. 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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.

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