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Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2018. Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 2: Guidance. Washington, DC: The National Academies Press. doi: 10.17226/25332.
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Page 79
Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2018. Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 2: Guidance. Washington, DC: The National Academies Press. doi: 10.17226/25332.
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Page 80
Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2018. Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 2: Guidance. Washington, DC: The National Academies Press. doi: 10.17226/25332.
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Page 81
Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2018. Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 2: Guidance. Washington, DC: The National Academies Press. doi: 10.17226/25332.
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78 Abuelhiga, A. 2013. U.S. DOT Connected Vehicle PlugFests. Webinar. Washington, D.C.: Research and Inno- vative Technology Administration, U.S. Department of Transportation. https://pdfs.semanticscholar.org/ presentation/c43c/0fde417f9a964d70d3d1fe289b51e3271ad6.pdf. Adler, T., M. Doherty, J. Klodzinski, and R. Tillman. 2014. Methods for Quantitative Risk Analysis for Travel Demand Model Forecasts. Transportation Research Record: Journal of the Transportation Research Board, No. 2429, pp. 1–7. Amadeo, R. 2014. Google’s Self-Driving Cars Hit 700,000 Miles, Learn City Navigation. Ars Technica. http:// arstechnica.com/gadgets/2014/04/googles-self-driving-cars-hit-700000-miles-learn-city-navigation/. Accessed June 13, 2016. Anderson, J., N. Kalra, K. Stanley, P. Sorensen, C. Samaras, and O. Oluwatola. 2014. Autonomous Vehicle Technol­ ogy: A Guide for Policymakers. Santa Monica, Calif.: RAND. Ange, K., C. Dwyer, K. Rooney, and E. Mierzejewski. 2017. Next Generation Scenario Planning: A Trans­ portation Practitioner’s Guide. Report FHWA-HEP-17-099. Washington, D.C.: FHWA, U.S. Department of Transportation. Automated Vehicles Task Force. 2014. Restating the National Highway Transportation Safety Administration’s National Motor Vehicle Crash Causation Survey for Automated Vehicles. Casualty Actuarial Society E­Forum, Fall, Vol. 1. https://www.casact.org/pubs/forum/14fforum/CAS%20AVTF_Restated_NMVCCS.pdf. Accessed January 10, 2016. Ben-Haim, Y. 2006. Info­Gap Decision Theory: Decisions Under Severe Uncertainty. Cambridge, Mass.: Academic Press. Bezzina, D., and J. Sayer. 2015. Safety Pilot Model Deployment: Test Conductor Team Report. (Report No. DOT HS 812 171). Washington, D.C.: National Highway Traffic Safety. Administration. Bonzanigo, L., and N. Kalra. 2014. Making Informed Investment Decisions in an Uncertain World: A Short Dem­ onstration. Policy Research Working Paper 6765. Washington, D.C.: World Bank. Burchell, R., G. Lowenstein, W. R. Dolphin, C. C. Galley, A. Downs, S. Seskin, K. Still, and T. Moore. 2002. TCRP Report 74: Costs of Sprawl–2000. Washington, D.C.: TRB, National Research Council. Bureau of Labor Statistics. n.d. American Time Use Survey. https://www.bls.gov/tus/tables.htm. Accessed April 10, 2018. Castiglione, J., M. Bradley, and J. Gliebe. 2015. SHRP 2 Report S2­C46­RR­1: Activity­Based Travel Demand Models: A Primer. Washington, D.C.: Transportation Research Board. http://www.trb.org/Main/Blurbs/ 170963.aspx. Accessed June 13, 2016. Cervero, R. 2003. Road Expansion, Urban Growth and Induced Travel: A Path Analysis. Journal of the American Planning Association, Vol. 69, No. 2, pp. 145–163. Chiu, Y.-C., J. Bottom, M. Mahut, A. Paz, R. Balakrishna, T. Waller, and J. Hicks. 2011. Transportation Research Circular E­C153: Dynamic Traffic Assignment: A Primer. Washington, D.C.: Transportation Research Board of the National Academies. https://onlinepubs.trb.org/onlinepubs/circulars/ec153.pdf. Accessed June 13, 2016. Dessai, S., and M. Hulme. 2007. Assessing the Robustness of Adaptation Decisions to Climate Change Uncertain- ties: A Case Study on Water Resources Management in the East of England. Global Environmental Change, Vol. 17, No. 1, pp. 59–72. doi:10.1016/j.gloenvcha.2006.11.005. Dewar, J. 2002. Assumption­Based Planning: A Tool for Reducing Avoidable Surprises. RAND Corporation. New York: Cambridge University Press. Dewar, J. A., and M. Wachs. 2008. Transportation Planning, Climate Change, and Decisionmaking Under Uncertainty. Washington, D.C.: Transportation Research Board. http://onlinepubs.trb.org/onlinepubs/sr/ sr290DewarWachs.pdf. Accessed June 13, 2016. References

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Operations of Shared Autonomous Vehicle Fleet for Austin, Texas, Market. Transportation Research Record: Journal of the Transportation Research Board, No. 2536, pp. 98–106. FHWA. 2016. Transportation Scenario Planning for Connected and Automated Vehicles. Task Order Proposal Request 31-10-17001. Galgano, S., M. Talas, D. Benevelli, R. Rausch, S. Sim, K. Opie, M. Jensen, and C. Stanley. 2016. Connected Vehicle Pilot Deployment Program Phase I, Concept of Operations (ConOps)—New York City. Report FHWA- JPO-16-299. Washington, D.C.: Intelligent Transportation Systems Joint Program Office, U.S. Department of Transportation. Garcia, D., C. Hill, and J. Wagner. 2015. Cybersecurity Considerations for Connected and Automated Vehicle Policy. Revolutionizing our Roadways Series. College Station, Tex.: Texas A&M Transportation Institute. Glaeser, E. L., and M. E. Kahn. 2003. Sprawl and Urban Growth. NBER Working Paper 9733. Cambridge, Mass.: National Bureau of Economic Research. Gopalakrishna, D., V. Garcia, A. Ragan, T. English, S. Zumpf, R. Young, M. Ahmed, F. Kitchener, N. Ureña Serulle, and E. Hsu. 2015. Connected Vehicle Pilot Deployment Program Phase 1, Concept of Operations (ConOps), ICF/Wyoming. Report FHWA-JPO-16-287. Washington, D.C.: Intelligent Transportation Systems Joint Program Office, U.S. Department of Transportation. Griggs, T., and D. Wakabayashi. 2018. How a Self-Driving Uber Killed a Pedestrian in Arizona. New York Times, March 21. https://www.nytimes.com/interactive/2018/03/20/us/self-driving-uber-pedestrian-killed.html. Grosse-Ophoff, A., S. Hausler, K. Heineke, and T. Moller. 2017. How Shared Mobility Will Change the Auto- motive Industry. McKinsey & Company. https://www.mckinsey.com/industries/automotive-and-assembly/ our-insights/how-shared-mobility-will-change-the-automotive-industry. Accessed June 14, 2018. Haasnoot, M., J. H. Kwakkel, W. E. Walker, and J. ter Maat. 2013. Dynamic Adaptive Policy Pathways: A Method for Crafting Robust Decisions for a Deeply Uncertain World. Global Environmental Change, Vol. 23, No. 2, pp. 485–498. Harb, M., Y. Xiao, G. Circella, P. L. Mokhtarian, and J. L. Walker. 2017. Projecting Travelers into a world of Self-Driving Vehicles: Estimating Travel Behavior Implications via a Naturalistic Experiment. Presented at 97th Annual Meeting of the Transportation Research Board, Washington, D.C. http://www.joanwalker.com/ uploads/3/6/9/5/3695513/harb_et_al_chauffeur_-_nov_2017_working_paper.pdf. Accessed June 30, 2018. Hong, Q., R. Wallace, and G. Krueger. 2014. Connected v. Automated Vehicles as Generators of Useful Data. Michigan Department of Transportation and the Center for Automotive Research. Lansing, Mich.: Michigan Department of Transportation. Hughes-Cromwick, E. Do Ride Hailing Platforms Increase Congestion? 2018. 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80 Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles Levin, A. 2017. Tesla Automation Faulted in by NTSA in 2016 Fatal Florida Crash. Bloomberg, Sept. 12. https://www. bloomberg.com/news/articles/2017-09-12/tesla-probe-focuses-on-restricting-autopilot-to-certain-roads. Levinson, D. 2015. Climbing Mount Next: The Effects of Autonomous Vehicles on Society. Minnesota Journal of Law, Science & Technology, Vol. 16, No. 2, pp. 787–808. Morris, M. 2014. Gasoline Prices Tend to Have Little Effect on Demand for Travel. Washington, D.C.: U.S. Energy Information Administration. http://www.eia.gov/todayinenergy/detail.cfm?id=19191. Accessed March 13, 2018. Najm, W., J. Koopmann, J. Smith, and J. Brewer. 2010. Frequency of Target Crashes for Intellidrive Safety System. Report DOT HS 811 381. Washington, D.C.: NHTSA, U.S. Department of Transportation. NCHRP. n.d. Transportation Pooled Fund Program. https://pooledfund.org/. 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References 81 U.S. DOT. 2015. Dynamic Mobility Applications (DMA) Program. https://www.its.dot.gov/research_archives/ dma/dma_faqs.htm. Accessed April 1, 2018. Waggoner, J., B. Frey, S. Novosad, S. Johnson, V. Blue, D. Miller, and S. Bahler. 2016. Connected Vehicle Pilot Deploy­ ment Program Phase 1: Concept of Operations (ConOps)—Tampa (THEA). Report FHWA-JPO-16-311, THEA. Washington, D.C.: U.S. Department of Transportation. Zmud, J., V. P. Barrabba, M. Bradley, J. R. Kuzmyak, M. Zmud, and D. Orrell. 2014. NCHRP Report 750: Strategic Issues Facing Transportation. Volume 6: The Effects of Socio­Demographics on Future Travel Demand. Wash- ington, D.C.: Transportation Research Board. Zmud, J., G. Goodin, M. Moran, N. Kalra, and E. Thorn. 2017. NCHRP Research Report 845: Advancing Auto­ mated and Connected Vehicles: Policy and Planning Strategies for State and Local Transportation Agencies. Washington, D.C.: Transportation Research Board. Zmud, J., M. Tooley, T. Baker, and J. Wagner. 2015. Paths of Automated and Connected Vehicle Deployment: Stra­ tegic Roadmap for State and Local Transportation Agencies. Strategic Research Program Report 161504-1. College Station, Tex.: Texas A&M Transportation Institute. Zmud, J., J. Wagner, R. T. Baker, G. Goodin, M. Moran, N. Kalra, and D. Fagnant. 2016. Policy and Planning Actions to Internalize Societal Impacts of CV and AV Systems in Market Decisions. Project NCHRP 20-102(1), Interim Deliverable to NCHRP. Washington, D.C.: Transportation Research Board.

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TRB’s National Cooperative Highway Research Program (NCHRP) Research Report 896: Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 2: Guidance provides detailed information and guidelines for state departments of transportation (DOTs) and metropolitan planning organizations (MPOs) to help update their modeling and forecasting tools. These tools address expected impacts of connected and automated vehicles (CAVs) on transportation supply, road capacity, and travel demand components. CAVs are likely to influence all personal and goods movement level of demand, travel modes, planning and investment decisions, physical transportation infrastructure, and geographic areas.

DOTs and regional MPOs are required to have a multimodal transportation plan with a minimum time horizon of 20 years under the requirements of the Moving Ahead for Progress in the 21st Century Act (MAP-21) requirements. This report explores ways to develop new planning and modeling processes that include CAVs in the transportation environment. The volume provides the details to NCHRP Research Report 896: Updating Regional Transportation Planning and Modeling Tools to Address Impacts of Connected and Automated Vehicles, Volume 1.

The research report is accompanied by a PowerPoint presentation that can be adapted for presentations to agency decision makers.

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