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Page 106
Suggested Citation:"Works Cited." National Academies of Sciences, Engineering, and Medicine. 2020. Guidebook for Managing Data from Emerging Technologies for Transportation. Washington, DC: The National Academies Press. doi: 10.17226/25844.
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Page 106
Page 107
Suggested Citation:"Works Cited." National Academies of Sciences, Engineering, and Medicine. 2020. Guidebook for Managing Data from Emerging Technologies for Transportation. Washington, DC: The National Academies Press. doi: 10.17226/25844.
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Page 107
Page 108
Suggested Citation:"Works Cited." National Academies of Sciences, Engineering, and Medicine. 2020. Guidebook for Managing Data from Emerging Technologies for Transportation. Washington, DC: The National Academies Press. doi: 10.17226/25844.
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Page 108
Page 109
Suggested Citation:"Works Cited." National Academies of Sciences, Engineering, and Medicine. 2020. Guidebook for Managing Data from Emerging Technologies for Transportation. Washington, DC: The National Academies Press. doi: 10.17226/25844.
×
Page 109
Page 110
Suggested Citation:"Works Cited." National Academies of Sciences, Engineering, and Medicine. 2020. Guidebook for Managing Data from Emerging Technologies for Transportation. Washington, DC: The National Academies Press. doi: 10.17226/25844.
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Page 110

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

106 Big Data. (2019). Retrieved from Dictionary.com: https://www.dictionary.com/browse/big-data. Burt, M., Cuddy, M., and Razo, M. (2014). Big Data’s Implications for Transportation Operations: An Exploration. U.S. Department of Transportation, Washington, D.C. Retrieved January 2019 from https://rosap.ntl.bts. gov/view/dot/3542. Cavanillas, J. M., Curry, E., and Wahlster, W. (2016). New Horizons for a Data­Driven Economy: A Roadmap for Usage and Exploitation of Big Data in Europe. Springer. Chitkara, A., Deloison, T., Kelkar, M., Pandey, P., and Pankratz, D. (2020). Enabling Data­Sharing: Emerging Principles for Transforming Urban Mobility. World Business Council for Sustainable Development. Retrieved March 2020 from https://docs.wbcsd.org/2020/01/WBCSD_Enabling_data_sharing_Emerging_principles_ for_transforming_urban_mobility.pdf. Dam, R. F. (2019). The Pareto Principle and How to Be More Effective. Retrieved March 24, 2020, from Inter- action Design Foundation: https://www.interaction-design.org/literature/article/the-pareto-principle-and- how-to-be-more-effective. DAMA International. (2011). The DAMA Dictionary of Data Management, 2nd Edition: Over 2,000 Terms Defined for IT and Business Professionals. Bradley Beach, NJ: Technics Publications, LLC. DAMA International. (2017, March 6). DAMA Data Management Body of Knowledge Framework 2nd Edition (DAMA­DMBOK2). Basking Ridge, NJ: Technics Publications. Retrieved from DAMA International: https://dama.org/sites/default/files/download/DAMA-DMBOK2-Framework-V2-20140317-FINAL.pdf. de Ternay, G. (2018, December 30). Convince Your Boss: 11 Tips to Make Them Say “Yes!”. Retrieved November 2019 from Guerric: https://guerric.co.uk/convince-your-boss/. Ellingwood, J. (2016, September 28). An Introduction to Big Data Concepts and Terminology. Retrieved November 2019 from DigitalOcean: https://www.digitalocean.com/community/tutorials/an-introduction-to-big-data- concepts-and-terminology. Gandomi, A., and Haider, M. (2015, April). Beyond the Hype: Big Data Concepts, Methods, and Analytics. Inter­ national Journal of Information Management, 35(2), 137–144. Retrieved from https://www.sciencedirect.com/ science/article/pii/S0268401214001066. Gettman, D., Toppen, A., Hales, K., Voss, A., Engel, S., and El Azhari, D. (2017). Integrating Emerging Data Sources into Operational Practice—Opportunities for Integration of Emerging Data for Traffic Management and TMCs. U.S. Department of Transportation, Washington, D.C. Hand, A. Z. (2016, August). Urban Mobility in a Digital Age: A Transportation Technology Strategy for Los Angeles. Los Angeles Department of Transportation, CA. https://static1.squarespace.com/static/ 57c864609f74567457be9b71/t/57c9059b9de4bb1598eeee49/1472793280502/Transportation+Technology+ Strategy_2016.pdf. Kim, H. Y., and Cho, J.-S. (2018). Data Governance Framework for Big Data Implementation with NPS Case Analysis in Korea. Journal of Business and Retail Management Research. Retrieved April 2020 from https://jbrmr.com/cdn/article_file/content_24232_18-04-20-02-28-48.pdf. Lexico Powered by Oxford. (2019). Retrieved November 2019 from Lexico Powered by Oxford: https:// www.lexico.com/en/definition/big_data. Llewellyn, R. (2015, September 15). 20 Ways to Create a Sense of Urgency. Retrieved November 2019 from The Enterprisers Project: https://enterprisersproject.com/article/2014/8/20-ways-create-sense-urgency? page=1. Marr, B. (2017, January 23). Really Big Data At Walmart: Real­Time Insights From Their 40+ Petabyte Data Cloud. Retrieved November 2019 from Forbes: https://www.forbes.com/sites/bernardmarr/2017/01/23/ really-big-data-at-walmart-real-time-insights-from-their-40-petabyte-data-cloud/#1b22eb76c105. Works Cited

Works Cited 107 Mukherjee, S., and Shaw, R. (2016). Big Data-Concepts, Applications, Challenges, and Future Scope. Inter­ national Journal of Advanced Research in Computer and Communication Engineering, 66–74. OECD/ITF. (2015). Big Data and Transport Understanding and Assessing Options. Paris: OECD Publishing. Retrieved January 2019, from https://www.itf-oecd.org/sites/default/files/docs/15cpb_bigdata_0.pdf. Pecheux, B., Shah, V., and Miller, S. (2019). NCHRP Research Report 865: Guidance for Development and Manage­ ment of Sustainable Enterprise Information Portals. Transportation Research Board, Washington, D.C. Pecheux, K., Pecheux, B., and Carrick, G. (2019). NCHRP Research Report 904: Leveraging Big Data to Improve Traffic Incident Management. Transportation Research Board, Washington, D.C. Portland Urban Data Lake (PUDL). (n.d.). Retrieved November 2019 from Portland Bureau of Transportation: https://www.portlandoregon.gov/transportation/article/681572. Press, G. (2014, September 3). 10 Big Data Definitions: What’s Yours? Retrieved November 2019 from Forbes: https://www.forbes.com/sites/gilpress/2014/09/03/12-big-data-definitions-whats-yours/#63db4d4413ae. Roe, C. (2017, December 18). What is Data Governance? Retrieved from Dataversity: http://www.dataversity.net/ what-is-data-governance/. Rouse, M. (2013, October). Big Data Management. Retrieved from Search Data Management: https:// searchdatamanagement.techtarget.com/definition/big-data-management. Soares, S. (2018, August 15). Big Data Governance: A Framework to Assess Maturity. Retrieved from IBM Corpora- tion: https://www.ibmbigdatahub.com/blog/big-data-governance-framework-assess-maturity. Swaney, R. (2019, August 22). Evolution of Data Management: The Role of Streaming Data and IoT Data Architecture. Retrieved from Cloudera: https://blog.cloudera.com/evolution-of-data-management-the- role-of-streaming-data-and-iot-data-architecture/. Taylor, C. (2017, June 8). Big Data Architecture. Retrieved January 22, 2019, from Datamation: https:// www.datamation.com/big-data/big-data-architecture.html. Turner, P. (2019, February 18). A Data Scientific Method. Retrieved November 2019 from Towards Data Science: https://towardsdatascience.com/a-data-scientific-method-80caa190dbd4. Veracity: The Most Important “V” of Big Data. (2019, August 29). Retrieved November 2019 from Gut Check: https://www.gutcheckit.com/blog/veracity-big-data-v/. Wells, D. (2017, January 17). The Next Generation of Data Governance. Retrieved January 22, 2019, from Eckerson Group: https://www.eckerson.com/articles/the-next-generation-of-data-governance. Wells, D. (2019, August 14). The Path to Modern Data Governance. Retrieved from Eckerson Group: https:// www.eckerson.com/articles/modern-data-governance-problems. What is Big Data. (2019). Retrieved November 2019 from SAS: https://www.sas.com/en_us/insights/big-data/ what-is-big-data.html. Yang, C., Huang, Q., Li, Z., Liu, K., and Hu, F. (2017). Big Data and Cloud Computing: Innovation Oppor- tunities and Challenges. International Journal of Digital Earth, 13–53. Note: Additional references reviewed and cited as part of the research can be found in an associated online-only document, NCHRP Web­Only Document 282: Framework for Managing Data from Emerging Transportation Technologies to Support Decision­Making. The resources within this document may be of interest to the reader.

Abbreviations and acronyms used without definitions in TRB publications: A4A Airlines for America AAAE American Association of Airport Executives AASHO American Association of State Highway Officials AASHTO American Association of State Highway and Transportation Officials ACI–NA Airports Council International–North America ACRP Airport Cooperative Research Program ADA Americans with Disabilities Act APTA American Public Transportation Association ASCE American Society of Civil Engineers ASME American Society of Mechanical Engineers ASTM American Society for Testing and Materials ATA American Trucking Associations CTAA Community Transportation Association of America CTBSSP Commercial Truck and Bus Safety Synthesis Program DHS Department of Homeland Security DOE Department of Energy EPA Environmental Protection Agency FAA Federal Aviation Administration FAST Fixing America’s Surface Transportation Act (2015) FHWA Federal Highway Administration FMCSA Federal Motor Carrier Safety Administration FRA Federal Railroad Administration FTA Federal Transit Administration HMCRP Hazardous Materials Cooperative Research Program IEEE Institute of Electrical and Electronics Engineers ISTEA Intermodal Surface Transportation Efficiency Act of 1991 ITE Institute of Transportation Engineers MAP-21 Moving Ahead for Progress in the 21st Century Act (2012) NASA National Aeronautics and Space Administration NASAO National Association of State Aviation Officials NCFRP National Cooperative Freight Research Program NCHRP National Cooperative Highway Research Program NHTSA National Highway Traffic Safety Administration NTSB National Transportation Safety Board PHMSA Pipeline and Hazardous Materials Safety Administration RITA Research and Innovative Technology Administration SAE Society of Automotive Engineers SAFETEA-LU Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (2005) TCRP Transit Cooperative Research Program TDC Transit Development Corporation TEA-21 Transportation Equity Act for the 21st Century (1998) TRB Transportation Research Board TSA Transportation Security Administration U.S. DOT United States Department of Transportation

TRA N SPO RTATIO N RESEA RCH BO A RD 500 Fifth Street, N W W ashington, D C 20001 A D D RESS SERV ICE REQ U ESTED G uidebook for M anaging D ata from Em erging Technologies for Transportation N CH RP Research Report 952 TRB ISBN 978-0-309-67349-5 9 7 8 0 3 0 9 6 7 3 4 9 5 9 0 0 0 0

Guidebook for Managing Data from Emerging Technologies for Transportation Get This Book
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With increased connectivity between vehicles, sensors, systems, shared-use transportation, and mobile devices, unexpected and unparalleled amounts of data are being added to the transportation domain at a rapid rate, and these data are too large, too varied in nature, and will change too quickly to be handled by the traditional database management systems of most transportation agencies.

The TRB National Cooperative Highway Research Program's NCHRP Research Report 952: Guidebook for Managing Data from Emerging Technologies for Transportation provides guidance, tools, and a big data management framework, and it lays out a roadmap for transportation agencies on how they can begin to shift – technically, institutionally, and culturally – toward effectively managing data from emerging technologies.

Modern, flexible, and scalable “big data” methods to manage these data need to be adopted by transportation agencies if the data are to be used to facilitate better decision-making. As many agencies are already forced to do more with less while meeting higher public expectations, continuing with traditional data management systems and practices will prove costly for agencies unable to shift.

Supplemental materials include an Executive Summary, a PowerPoint presentation on the Guidebook, and NCHRP Web-Only Document 282: Framework for Managing Data from Emerging Transportation Technologies to Support Decision-Making.

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