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Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2024. New Mobility Options in Travel Demand Forecasting and Modeling: A Guide. Washington, DC: The National Academies Press. doi: 10.17226/27827.
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Page 24

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24 References ARC Model Specification, 2015. Retrieved July 07, 2023, from https://atlregional.github.io/ARC_Model/index .html#specification. Caliper Projections, 2021. Transportation Newsletter – Winter 2020–2021. Retrieved February 2, 2022, from https://www.caliper.com/transcad/newsletter/winter-2021-2022.htm. Childress, S., Nichols, B., Charlton, B., and Coe, S., 2015. Using an Activity-Based Model to Explore the Potential Impacts of Automated Vehicles, 99–106. https://doi.org/10.3141/2493-11. Dey, B. K., Tirtha, S. D., Eluru, N., and Konduri, K. C., 2021. Transformation of Ridehailing in New York City: A Quantitative Assessment. Transportation Research Part C: Emerging Technologies, 129, 103235. Household Travel Survey, Atlanta, Georgia, 2011. Retrieved July 07, 2023, from https://www.nrel.gov/transportation /secure-transportation-data/tsdc-atlanta-regional-travel-survey.html. James, O., Swiderski, J. I., Hicks, J., Teoman, D., and Buehler, R., 2019. Pedestrians and e-scooters: An Initial Look at e-Scooter Parking and Perceptions by Riders and Non-Riders. Sustainability, 11(20), 5591. Milakis, D., Gedhardt, L., Ehebrecht, D., and Lenz, B., 2020. Is Micro-Mobility Sustainable? An Overview of Implications for Accessibility, Air Pollution, Safety, Physical Activity, and Subjective Wellbeing. Handbook of Sustainable Transport. Narayanan, S., Chaniotakis, E., and Antoniou, C., 2020. Factors Affecting Traffic Flow Efficiency Implications of Connected and Autonomous Vehicles: A Review and Policy Recommendations. Advances in Transport Policy and Planning, 5, 1–50. National Association of City Transportation Officials (NACTO), 2023. Bike Share and Shared Micromobility Initiative. Retrieved August 25, 2023, from https://nacto.org/program/bike-share-initiative/. NCHRP Project 20-102(29) web page. https://apps.trb.org/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=4866, accessed April 9, 2024. NHTS, 2023. National Household Travel Survey data. Retrieved November 4, 2023, from https://nhts.ornl.gov /downloads. Noland, R. B., 2019. Trip Patterns and Revenue of Shared e-scooters in Louisville, Kentucky. Findings, 7747. The New York City Taxi and Limousine Commission (TLC), 2023. NYC State of Industry. Retrieved August 25, 2023, from https://analytics-tlc.github.io/state_of_industry/. Parvez, D. A., Tirtha, S. D., Bhowmik, T., and Eluru, N., 2023. Joint Econometric Model Framework for Trans- portation Network Company Users’ Trip Fare and Destination Choice Analysis. Transportation Research Record, Journal of the Transportation Research Board, Volume 2677, Issue 7, pp. 545–557. San Diego Association of Governments, 2021. San Diego Forward: The 2021 Regional Plan, Appendix S: Travel Demand Modeling Tools. Retrieved October 28, 2022, from https://www.sandag.org/-/media/SANDAG /Documents/PDF/regional-plan/2021-regional-plan/final-2021-regional-plan/2021-regional-plan-appendix -s-2021-05-01.pdf. Shaheen, S., Cohen, A., Chan, N., and Bansal, A., 2020. Sharing Strategies: Carsharing, Shared Micromobility (Bikesharing and Scooter Sharing), Transportation Network Companies, Microtransit, and Other Innovative Mobility Modes. In Transportation, Land Use, and Environmental Planning (pp. 237–262). Elsevier. Virginia Department of Transportation, 2020. Travel Demand Modeling Policies and Procedures. https://www .vdot.virginia.gov/doing-business/technical-guidance-and-support/technical-guidance-documents/virginia -travel-demand-modeling-policies-and-procedures-manual-version-30/, accessed April 9, 2024. Vyas, G., Famili, P., Vovsha, P., Fay, D., Kulshrestha, A., Giaimo, G., and Anderson, R., 2019. Incorporating Features of Autonomous Vehicles in Activity-Based Travel Demand Model for Columbus, OH. Transportation (Amst). 46, 2081–2102. https://doi.org/10.1007/s11116-019-10030-w, accessed April 9, 2024.

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 New Mobility Options in Travel Demand Forecasting and Modeling: A Guide
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Emerging transportation technologies and shared mobility services, or new mobility options (NMOs), are affecting travel behavior and demand. NMOs may include shared micromobility, transportation networking companies (TNCs), and connected and autonomous vehicles (CAVs). As NMOs grow in availability and use, transportation planners and decision-makers need to be able to understand how to harness positive and mitigate negative impacts.

NCHRP Research Report 1113: New Mobility Options in Travel Demand Forecasting and Modeling: A Guide, from TRB's National Cooperative Highway Research Program, provides travel demand modeling practitioners with ways to consider NMOs in travel demand forecasting models (TDFMs) - one of the primary tools available to understand potential impacts and future uncertainties.

Supplemental to the report are NCHRP Web-Only Document 399: Developing a Guide for New Mobility Options in Travel Demand Forecasting and Modeling; datasets of Use Case 1: Data, Code, and Tutorials for Household Vehicle Ownership Use Case; Use Case 2: Data, Code, and Tutorials for Household Trip Rates Use Case; Use Case 3: Data, Code, and Tutorials for Mode Choice Use Case; an Implementation of Research Findings and Products document; and a PowerPoint presentation of the research.

Any software included is offered as is, without warranty or promise of support of any kind either expressed or implied. Under no circumstance will the National Academy of Sciences or the Transportation Research Board (collectively “TRB”) be liable for any loss or damage caused by the installation or operation of this product. TRB makes no representation or warranty of any kind, expressed or implied, in fact or in law, including without limitation, the warranty of merchantability or the warranty of fitness for a particular purpose, and shall not in any case be liable for any consequential or special damages.

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