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Page 64
Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2019. Practices on Acquiring Proprietary Data for Transportation Applications. Washington, DC: The National Academies Press. doi: 10.17226/25519.
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Page 64
Page 65
Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2019. Practices on Acquiring Proprietary Data for Transportation Applications. Washington, DC: The National Academies Press. doi: 10.17226/25519.
×
Page 65
Page 66
Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2019. Practices on Acquiring Proprietary Data for Transportation Applications. Washington, DC: The National Academies Press. doi: 10.17226/25519.
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Page 66

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.

64 Amin-Naseri, M., P. Chakraborty, A. Sharma, S. B. Gilbert, and M. Hong. Evaluating the Reliability, Coverage, and Added Value of Crowdsourced Traffic Incident Reports from Waze. Transportation Research Record: Journal of the Transportation Research Board, No. 2627, 2018, pp. 34–43. http://dx.doi.org/ 10.1177/036119818790619. Arizona Revised Statutes § 39–121. https://www.azleg.gov/viewdocument/?docName=https://www.azleg.gov/ ars/39/00121.htm. Accessed Aug. 30, 2018. Arnold, B. A Survey of Public Record Laws: Issues Affecting State and Local Contracts, Bidders, and Contractors. American Bar Association, 2010. http://apps.americanbar.org/dch/thedl.cfm?filename=/PC500000/ relatedresources/A_SURVEY_OF_OPEN_GOVERNMENT_LAWS.pdf. Accessed Aug. 30, 2018. Athey Creek Consultants. Assess Speed Data for Traffic Management. FHWA, U.S. Department of Transportation, 2017. Bauer, J., R. Margiotta, and M. Pack. Applying Archived Operations Data in Transportation Planning: A Primer. Report FHWA-HOP-16-082. FHWA, U.S. Department of Transportation, 2016. Bayen, A. M., M. Sharafsaleh, and A. D. Patire. Hybrid Traffic Data Collection Roadmap: Pilot Procurement of Third-Party Traffic Data. Partners for Advanced Transportation Technology, University of California, Berkeley, 2013. Bernardin, V. Now That You’ve Downloaded Some StreetLight Data, What Should You Do First? Data Representativeness and Expansion Considerations. Presented at Ohio Travel Demand Model Users Group Fall Meeting, Columbus, 2017. https://www.otdmug.org/wordpress/wp-content/uploads/2017/09/ OHMUG_RSG_BigData-Part2.pdf. Accessed Aug. 30, 2018. Cambridge Systematics, Inc., Vanasse Hangen Brustlin, Inc., Gallop Corporation, C. R. Bhat, Shapiro Trans- portation Consulting, LLC., and Martin/Alexiou/Bryson, PLLC. NCHRP Report 716: Travel Demand Forecasting: Parameters and Techniques. Transportation Research Board of the National Academies, Washington, D.C., 2012. Chen, M., and X. Zhang. Collection and Analysis of 2013–2014 Travel Time Data. Kentucky Transportation Center, University of Kentucky, Lexington, 2017. Coates, A. M. Application of Streetlight Data for Miami University Circulation Study and Wisconsin DOT. Presented at Ohio Travel Demand Model Users Group Fall Meeting, Columbus, 2017. https://www.otdmug. org/wordpress/wp-content/uploads/2017/09/Baker-OTDMUG-Presentation-09-08-17.pdf. Accessed Aug. 30, 2018. Crowson, G., and D. Deeter. Understanding Utilization of Third Party Data and Information. Report ENT-2012-1. Michigan Department of Transportation, Lansing, 2012. Daneshgar, F., K. Farokhi Sadabadi, and A. Haghani. 2018. A Conflation Methodology for Two GIS Roadway Networks and Its Application in Performance Measurements. Transportation Research Record: Journal of the Transportation Research Board, No. 2672, 2018, pp. 284–293. http://dx.doi.org/10.1177/0361198118793000. Donnelly, R., and R. Moeckel. NCHRP Synthesis of Highway Practice 514: Statewide and Megaregional Travel Forecasting Models: Freight and Passenger. Transportation Research Board, Washington, D.C., 2017. FHWA. Technical Support and Data-Sharing Support for the National Performance Management Research Data Set. U.S. Department of Transportation, 2016. FHWA. Urban Congestion Report. U.S. Department of Transportation, 2017. https://ops.fhwa.dot.gov/perf_ measurement/ucr/. Accessed November 26, 2018. Gharaibeh, N., I. Oti, D. Schrank, and J. Zmud. NCHRP Synthesis of Highway Practice 508: Data Management and Governance Practices. Transportation Research Board, Washington, D.C., 2017. Giaimo, G. Use of StreetLight OD Data for Travel Demand Models User Guide. Ohio Department of Transporta- tion, Columbus, 2017A. References

References 65 Giaimo, G. Comparison of Model to StreetLight Internal-Internal TAZ Trip Tables in Toledo Area. Presented at Ohio Travel Demand Model Users Group Fall Meeting, Columbus, 2017B. https://www.otdmug.org/ wordpress/wp-content/uploads/2017/09/Giaimo_fall17_MUG.pdf. Accessed Aug. 30, 2018. Granato, S. Various Uses for INRIX/StreetLight Data: Ohio Plus Border Area. Presented at Ohio Travel Demand Model Users Group Fall Meeting, Columbus, 2017. https://www.otdmug.org/wordpress/wp-content/ uploads/2017/09/Streetlight4MUG_Sept2017sg.pdf. Accessed Aug. 30, 2018. Green, E. R., J. Ripy, M. Chen, and X. Zhang. Conflation Methodologies to Incorporate Consumer Travel Data into State HPMS Data Sets. Presented at 92nd Annual Meeting of the Transportation Research Board, Washington, D.C., 2013. Haghani, A., M. Hamedi, and K. Farokhi Sadabadi. Summary Report for I-95 Corridor Coalition Vehicle Probe Project: Validation of INRIX Data July–September 2008. I-95 Corridor Coalition, 2009. https://i95coalition. org/wp-content/uploads/2015/02/I-95-CC-VP-EX-SUMM-FINAL-JAN-22-09-W-COVER.pdf?x70560. Accessed Dec. 19, 2018. HERE. Product Specifications, 2013. Hochmair, H. H., E. Bardin, and A. Ahmouda. 2017. Estimating Bicycle Trip Volume for Miami–Dade County from Strava Tracking Data. Presented at 96th Annual Meeting of the Transportation Research Board, Washington, D.C., 2017. I-95 Corridor Coalition. I-95 Corridor Coalition Traffic Flow Data Program Agreement for Use of Data. 2015. https://www.i95coalition.org/wp-content/uploads/2015/02/VPPII_DUAv9_signed.pdf. Accessed Dec. 19, 2018. I-95 Corridor Coalition. http://i95coalition.org/. Accessed Nov. 26, 2018. I-95 Corridor Coalition Traffic View. http://i95coalition.org/trafficview. Accessed Aug. 30, 2018. iPeMS. iPeMS: Empower Your Agency to Improve System Performance and Demonstrate Success, Automati- cally. 2017. https://www.iteris.com/system/files/content/resource/pub/Files/37/iPeMS_FINAL_2017.pdf. Accessed Nov. 26, 2018. Kentucky Revised Statutes § 61–870–61.884. http://www.lrc.ky.gov/statutes/chapter.aspx?id=37294. Accessed Aug. 30, 2018. Kim, S., and B. Coifman. Comparing INRIX Speed Data Against Concurrent Loop Detector Stations over Several Months. Transportation Research Part C: Emerging Technologies, Vol. 49, 2014, pp. 59–72. Kressner, J. D. NCHRP IDEA Project 184: Synthetic Household Travel Data Using Consumer and Mobile Phone Data. Transportation Research Board, Washington, D.C., 2017. Lemp, J. Southeast Michigan Council of Governments Peer Review. FHWA, U.S. Department of Transportation, 2017A. Lemp, J. Metropolitan Council Peer Review. FHWA, U.S. Department of Transportation, 2017B. Lemp, J. Mid-America Regional Council Peer Review. FHWA, U.S. Department of Transportation, 2017C. Li, H., S. M. Remias, C. M. Day, M. M. Mekker, J. R. Sturdevant, and D. M. Bullock. Shock Wave Boundary Identification Using Cloud-Based Probe Data. Transportation Research Record: Journal of the Transportation Research Board, No. 2526, 2015, pp. 51–60. http://dx.doi.org/10.3141/2526-06. Mahapatra, S., K. Farokhi Sadabadi, M. Wolniak. Maryland State Highway Mobility Report. Maryland State Highway Administration, Baltimore, 2017. Mekker, M. M., S. M. Remias, M. L. McNamara, W. A. Bunnell, D. W. Krohn, J. R. Sturdevant, E. D. Cox, and D. M. Bullock. Identifying Effects and Applications of Fixed and Variable Speed Limits. Report FHWA/IN/ JTRP-2016/31. Indiana Department of Transportation, Indianapolis, 2016. Miller, H. J., M. E. O’Kelly, Y. Jaegal, W. Bachman, L. Huntsinger, and G. J. MacFarlane. Progress Report: Estimating External Travel Using Purchased Third-Party Data. Ohio Department of Transportation, Columbus, 2016. Minnesota Department of Transportation. Data Business Plan. 2011. https://www.dot.state.mn.us/tda/data businessplan.docx. Accessed Dec. 19, 2018. Official Code of Georgia Annotated § 50-18-70. https://advance.lexis.com/container?config=00JAAzZDgzNz U2ZC05MDA0LTRmMDItYjkzMS0xOGY3MjE3OWNlODIKAFBvZENhdGFsb2fcIFfJnJ2IC8XZi1AYM4Ne &crid=212f81b4-ab09-4d66-b58f-7e4783e6ea52&prid=c11faf78-5bf2-4c14-bc70-23563f9426a3. Accessed Nov. 26, 2018. Ohio Revised Code § 149.43. http://codes.ohio.gov/orc/149.43v1. Accessed Aug. 30, 2018. Oregon Department of Transportation. ODOT Strategic Data Business Plan. Salem, 2016. Pack, M., and N. Ivanov. Are You Gonna Go My Waze? Practical Advice for Working with 3rd Party Data Providers. ITE Journal, Vol. 87, No. 2, 2017, pp. 28–35. Parikh, A. StreetLight AADT Comparison. Presented at Ohio Travel Demand Model Users Group Fall Meeting, Columbus, 2017. https://www.otdmug.org/wordpress/wp-content/uploads/2017/09/Parikh_OTDMUG_ Streetlight.pdf. Accessed Aug. 30, 2018. Pinjari, A. R., A. B. Zanjani, A. Thakur, A. N. Irmania, and M. Kamali. 2014. Using Truck Fleet Data in Combi- nation with Other Data Sources for Freight Modeling and Planning. Florida Department of Transportation, Tallahassee, 2014.

66 Practices on Acquiring Proprietary Data for Transportation Applications RITIS. An Introduction to RITIS. https://www.ritis.org/intro. Accessed June 21, 2018. Sharma, A., V. Ahsani, and S. Rawat. Evaluation of Opportunities and Challenges of Using INRIX Data for Real-Time Performance Monitoring and Historical Trend Assessment. Report SPR-P1(14) M007. Nebraska Department of Roads, Lincoln, 2017. Smith, B. L., M. D. Fontaine, J. Beaton, E. Dejarnette, A. Jendricks, and L. L. Tennant. NCHRP Project 70-01: Private-Sector Provision of Congestion Data. Transportation Research Board, Washington, D.C., 2007. Stabler, B. Development of the Idaho Statewide Travel Demand Model Trip Matrices Using Cell Phone OD Data and Origin Destination Matrix Estimation. Presented at Transportation Research and Education Center Friday Seminar Series, Portland State University, Transportation Research and Education Center, Portland, OR, 2014. Turner, S. Using Mobile Device Samples to Estimate Traffic Volume. Report MN/RC 2017-49. Minnesota Depart- ment of Transportation, Saint Paul, 2017. Turner, S., K. Farokhi Sadabadi, A. Haghani, M. Hamedi, R. Brydia, S. Santiago, and E. Kussy. Private Sector Data for Performance Management: Final Report. Report FHWA-HOP-11-029. FHWA, U.S. Department of Transportation, 2011. University of Maryland College Park. Request for Proposal (RFP) No. 83794: To Provide Real-Time Traffic Data and Associated Products to I-95 Coalition Organizations and Affiliates. 2013. https://i95coalition.org/ wp-content/uploads/2015/02/RFP-83794N-FINAL-2.pdf?x70560. Accessed Dec. 19, 2018. Venkatanarayana, R., and M. D. Fontaine. Assessing the Quality of Private Sector Origin–Destination Data. Presented at 97th Annual Meeting of the Transportation Research Board, Washington, D.C., 2018. Watkins, K., R. Ammanamanchi, J. LaMondia, and C. A. Le Dantec. Comparison of Smartphone-Based Cyclist GPS Data Sources. Presented at 95th Annual Meeting of the Transportation Research Board, Washington, D.C., 2016. Wisconsin Statutes § 19.31–19.39. https://docs.legis.wisconsin.gov/statutes/statutes/19/II/31. Accessed Aug. 30, 2018. Young, S. Real-Time Traffic Operations Data Using Vehicle Probe Technology. Proceedings of the 2007 Mid-Continent Transportation Research Symposium, Ames IA, 2007.

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TRB’s National Cooperative Highway Research Program (NCHRP) Synthesis 541 explores how state departments of transportation (DOTs) and metropolitan planning organizations (MPOs) acquire proprietary data for transportation applications.

Recent technological advancements have led to new types of transportation data with characteristics that include improved quality and greater temporal and wider geographical coverage than traditional data sets. State DOTs and MPOs face challenges associated with obtaining new proprietary data.

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