Differential impacts of autonomous and connected-autonomous vehicles on household residential location

AAA, 2018, Your Driving Costs: How Much Are You Really Paying to Drive?, Technical Report American Automobile Association Adebisi, 2020 Akbari, 2020, Home relocation and mobility tool ownership: Econometric investigations in the context of rising fuel prices in the Greater Toronto Area, Travel Behav. Soc., 19, 8, 10.1016/j.tbs.2019.10.005 Anderson, 2014 Bansal, 2018, Are we ready to embrace connected and self-driving vehicles? A case study of Texans, Transportation, 45, 10.1007/s11116-016-9745-z Bardaka, E., Frey, C., Hajbabaie, A., List, G., Rouphail, N., Williams, B., & Cummings, M. (2021). Impacts of autonomous vehicle technology on transportation systems. URL:https://connect.ncdot.gov/projects/research/RNAProjDocs/RP2019-11. Bardaka, 2021, Impacts of connected and autonomous vehicles on transportation demand and land use, vol. 1 Berglund, 2020, Smart infrastructure: A vision for the role of the civil engineering profession in smart cities, J. Infrastruct. Syst., 26, 10.1061/(ASCE)IS.1943-555X.0000549 Bhat, 2004, A mixed spatially correlated logit model: Formulation and application to residential choice modeling, Transp. Res. Part B: Methodol., 38, 147, 10.1016/S0191-2615(03)00005-5 Bholowalia, P., & Kumar, A. (2014). EBK-Means: A Clustering Technique based on Elbow Method and K-Means in WSN. Technical Report 9. Bierstedt, J., Gooze, A., Gray, C., Peterman, J., Raykin, L., & Walters, J. (2014). Effects of Next-Generation Vehicles on Travel Demand and Highway Capacity. In FP Think Working Group 8 (pp. 1–10). Bösch, 2018, Cost-based analysis of autonomous mobility services, Transp. Policy, 64, 76, 10.1016/j.tranpol.2017.09.005 Brier, 1950, Verification of forecasts expressed in terms of probability, Mon. Weather Rev., 78, 1, 10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2 Bruns, 2019, Moving into and within cities – Interactions of residential change and the travel behavior and implications for integrated land use and transport planning strategies, Travel Behav. Soc., 17, 46, 10.1016/j.tbs.2019.06.002 Carrese, S., Nigro, M., Patella, S.M., & Toniolo, E. (2019). A preliminary study of the potential impact of autonomous vehicles on residential location in Rome. Res. Transp. Econ., (pp. 1–7). URL: doi: 10.1016/j.retrec.2019.02.005. DOI: 10.1016/j.retrec.2019.02.005. Chang, 1997, Analysis of characteristics of mixed traffic flow of autopilot vehicles and manual vehicles, Transp. Res. Part C: Emerg. Technol., 5, 333, 10.1016/S0968-090X(97)00020-X Childress, 2015, Using an Activity-based Model to Explore Possible Impacts of Automated Vehicles, Transp. Res. Rec.: J. Transp. Res. Board, 99, 10.3141/2493-11 Choudhary, 2016, Improving K-means through better initialization and normalization, 2415 CommunityViz (2018). Triangle CommunityViz 2.0 Technical Overview. Technical Report. Cordera, R., Nogués, S., González-González, E., & Moura, J.L. (2021). Modeling the Impacts of Autonomous Vehicles on Land Use Using a LUTI Model. Sustainability, 13, 1608. URL:https://www.mdpi.com/2071-1050/13/4/1608/htmhttps://www.mdpi.com/2071-1050/13/4/1608. DOI: 10.3390/SU13041608. Danaf, 2019, Online discrete choice models: Applications in personalized recommendations, Decis. Support Syst., 119, 35, 10.1016/j.dss.2019.02.003 Dubljevic, 2021, Toward a rational and ethical sociotechnical system of autonomous vehicles: A novel application of multi-criteria decision analysis, PLoS ONE, 16, 10.1371/journal.pone.0256224 Durand, 2018 Eliasson, 2010, The influence of accessibility on residential location, 137 Fagnant, 2014, The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios, Transp. Res. Part C: Emerg. Technol., 40, 1, 10.1016/j.trc.2013.12.001 Gehrke, 2019, Understanding stated neighborhood preferences: The roles of lifecycle stage, mobility style, and lifestyle aspirations, Travel Behav. Soc., 17, 62, 10.1016/j.tbs.2019.07.001 Gelauff, 2019, Spatial and welfare effects of automated driving: Will cities grow, decline or both?, Transp. Res. Part A: Policy Practice, 121, 277 General Motors (2022). Why All AVs Should be EVs. URL:https://www.gm.com/stories/all-avs-should-be-evs. Gneiting, 2007, Strictly Proper Scoring Rules, Prediction, and Estimation, J. Am. Stat. Assoc., 102, 359, 10.1198/016214506000001437 Greenacre, 2013 Guo, 2007, Operationalizing the concept of neighborhood: Application to residential location choice analysis, J. Transp. Geogr., 15, 31, 10.1016/j.jtrangeo.2005.11.001 Guo, 2020, Impacts of personalized accessibility information on residential location choice and travel behavior, Travel Behav. Soc., 19, 99, 10.1016/j.tbs.2019.12.007 Hajbabaie, 2021, Impacts of connected and autonomous vehicles on transportation capacity -, vol. 2 Han, 2012, Data Mining, 1 Haque, 2019, Modelling residential location choices with implicit availability of alternatives, J. Transp. Land Use, 12, 597, 10.5198/jtlu.2019.1450 Hasnat, 2021, Impacts of Private Autonomous and Connected Vehicles on Transportation Network Demand in the Triangle Region, North Carolina, J. Urban Plann. Develop., 147, 13, 10.1061/(ASCE)UP.1943-5444.0000649 Hjorthol, R. (2013). Attitudes, ownership and use of Electric Vehicles-a review of literature. URL: www.toi.no. Hossain, 2022, Modelling the adoption of autonomous vehicle: How historical experience inform the future preference, Travel Behav. Soc., 26, 57, 10.1016/j.tbs.2021.09.003 Kim, 2020, Will autonomous vehicles change residential location and vehicle ownership? Glimpses from Georgia, Transp. Res. Part D: Transp. Environ., 82, 10.1016/j.trd.2020.102291 Kroesen, 2019, Residential self-selection and the reverse causation hypothesis: Assessing the endogeneity of stated reasons for residential choice, Travel Behav. Soc., 16, 108, 10.1016/j.tbs.2019.05.002 Krueger, 2021, Evaluating the predictive abilities of mixed logit models with unobserved inter- and intra-individual heterogeneity, J. Choice Modell., 41, 10.1016/j.jocm.2021.100323 Krueger, 2019, Autonomous driving and residential location preferences: Evidence from a stated choice survey, Transp. Res. Part C: Emerg. Technol., 108, 255, 10.1016/j.trc.2019.09.018 Larson, 2020, Self-driving cars and the city: Effects on sprawl, energy consumption, and housing affordability, Regional Sci. Urban Econ., 81, 10.1016/j.regsciurbeco.2019.103484 Likas, 2003, The global k-means clustering algorithm, Pattern Recogn., 32, 451, 10.1016/S0031-3203(02)00060-2 Litman, T. (2020). Autonomous Vehicle Implementation Predictions. Implications for Transport Planning. Technical Report Victoria Transport Policy Institute. URL: www.vtpi.org. Liu, 2021, The suburbanization of poverty and changes in access to public transportation in the Triangle Region, nc, J. Transp. Geogr., 90, 10.1016/j.jtrangeo.2020.102930 Liu, 2021, Effect of autonomous vehicles on travel and urban characteristics, Transp. Res. Part B: Methodol., 153, 128, 10.1016/j.trb.2021.08.014 Lloyd, 1982, Least Squares Quantization in PCM, IEEE Trans. Inform. Theory, 28, 129, 10.1109/TIT.1982.1056489 McFadden, D. (1978). Modeling the Choice of Residential Location. Transp. Res. Rec., (pp. 72–77). McFadden, 2000, Mixed MNL Models for Discrete Response, J. Appl. Econometr., 15, 447, 10.1002/1099-1255(200009/10)15:5<447::AID-JAE570>3.0.CO;2-1 McKinsey & Company (2021). Why the future automotive future is electric. URL:https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/why-the-automotive-future-is-electric. Meyer, 2017, Autonomous vehicles: The next jump in accessibilities?, Res. Transp. Econ., 62, 80, 10.1016/j.retrec.2017.03.005 Millard-Ball, 2019, The autonomous vehicle parking problem, Transp. Policy, 75, 99, 10.1016/j.tranpol.2019.01.003 Moore, 2020, On investigating the potential effects of private autonomous vehicle use on home/work relocations and commute times, Transp. Res. Part C: Emerg. Technol., 110, 166, 10.1016/j.trc.2019.11.013 Mullaney, T. (2020). Tesla and the science behind low-cost, next-gen million-mile EV battery. URL:https://www.cnbc.com/2020/06/30/tesla-and-the-science-of-low-cost-next-gen-ev-million-mile-battery.html. NC General Assembly (2017). North Carolina House Bill 469 Session Law 2017–166. Technical Report General Assembly of North Carolina. URL:https://www.ncleg.gov/BillLookup/2017/h469. Oh, 2020, Assessing the impacts of automated mobility-on-demand through agent-based simulation: A study of Singapore, Transp. Res. Part A: Policy Practice, 138, 367 Petersen, 2006, Intrahousehold Car-Type Choice for Different Travel Needs, Transp. Res. Rec.: J. Transp. Res. Board, 1985, 207, 10.1177/0361198106198500123 Pinjari, 2011, Modeling the choice continuum: An integrated model of residential location, auto ownership, bicycle ownership, and commute tour mode choice decisions, Transportation, 38, 933, 10.1007/s11116-011-9360-y Rokach, 2008 RSG (2016). 2016 Triangle Region Household Travel Survey. Technical Report. SAE (2021). SAE Levels of Driving Automation - Refined for Clarity and International Audience. URL:https://www.sae.org/blog/sae-j3016-update. Schirmer, P.M., Van Eggermond, M.A., & Axhausen, K.W. (2014). The role of location in residential location choice models: a review of literature. Journal of Transport and Land Use, 7, 3. URL:https://www.jtlu.org/index.php/jtlu/article/view/740. 10.5198/jtlu.v7i2.740. Shen, Y., Zhang, H., & Zhao, J. (2017). Embedding Autonomous Vehicle Sharing in Public Transit System: An Example of Last-Mile Problem. In 96th Annual Meeting of Transportation Research Board. Shladover, 2012, Impacts of Cooperative Adaptive Cruise Control on Freeway Traffic Flow, Transp. Res. Rec.: J. Transp. Res. Board, 2324, 63, 10.3141/2324-08 Singleton, 2019, Discussing the Positive utilities of autonomous vehicles: will travelers really use their time productively?, Transport Reviews, 39, 50, 10.1080/01441647.2018.1470584 Stantec (2015). CommunityViz Land Suitability Factor Statistical Analysis for the Triangle Regional Model. Technical Report DCHC MPO, CAMPO, TJCOG. Stephens, 2016, Estimated Bounds and Important Factors for Fuel Use and Consumer Costs of Connected and Automated Vehicles, Technical Report National Renewable Energy Laboratory Tientrakool, 2011, Highway Capacity Benefits from Using Vehicle-to-Vehicle Communication and Sensors for Collision Avoidance, 1 Train, 2009 Truong, 2017, Estimating the trip generation impacts of autonomous vehicles on car travel in Victoria, Australia, Transportation, 44, 1279, 10.1007/s11116-017-9802-2 Uhlemann, 2015, Introducing Connected Vehicles [Connected Vehicles], IEEE Veh. Technol. Mag. US Census Bureau (2019). American Community Survey 2014–2018 5-Year Estimates. URL:https://www.census.gov/newsroom/press-releases/2019/acs-5-year.html. Vyas, 2019, Incorporating features of autonomous vehicles in activity-based travel demand model for Columbus, OH, Transportation, 46, 2081, 10.1007/s11116-019-10030-w Waddell, 1992, A multinomial logit model of race and urban structures, Urban Geography, 13, 127, 10.2747/0272-3638.13.2.127 Waddell, P. (2002). UrbanSim: Modeling Urban Development for Land Use, Transportation and Environmental Planning. Journal of the American planning association, 68(3), pp. 297–314, 68, 297–314. URL: www.urbansim.org. Wadud, 2016, Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles, Transp. Res. Part A: Policy Practice, 86, 1 Wali, 2021, Modeling consumer affinity towards adopting partially and fully automated vehicles-The role of preference heterogeneity at different geographic levels, Transp. Res. Part C, 129, 10.1016/j.trc.2021.103276 Wu, 2012 Xiao, L., Wang, M., & Arem, B. v. (2017). Realistic Car-Following Models for Microscopic Simulation of Adaptive and Cooperative Adaptive Cruise Control Vehicles: doi: 10.3141/2623-01, 2623, 1–9. URL:https://journals.sagepub.com/doi/abs/10.3141/2623-01. DOI: 10.3141/2623-01. Xiao, 2018, Unravelling effects of cooperative adaptive cruise control deactivation on traffic flow characteristics at merging bottlenecks, Transp. Res. Part C: Emerging Technol., 96, 380, 10.1016/j.trc.2018.10.008 Xie, 2020, TRR Personalized Choice Model for Managed Lane Travel Behavior, Transp. Res. Rec., 2020, 442, 10.1177/0361198120923355 Yan, 2020, Evaluating household residential preferences for walkability and accessibility across three U.S. regions, Transp. Res. Part D: Transp. Environ., 80, 10.1016/j.trd.2020.102255 Zakharenko, 2016, Self-driving cars will change cities, Regional Sci. Urban Econ., 61, 26, 10.1016/j.regsciurbeco.2016.09.003 Zhang, 2018, Residential Location Choice in the Era of Shared Autonomous Vehicles, J. Plann. Educ. Res. Zhang, 2018, The impact of private autonomous vehicles on vehicle ownership and unoccupied VMT generation, Transp. Res. Part C: Emerg. Technol., 90, 156, 10.1016/j.trc.2018.03.005 Zhao, 2018, Anticipating the Regional Impacts of Connected and Automated Vehicle Travel in Austin, Texas, J. Urban Plann. Develop., 144, 10.1061/(ASCE)UP.1943-5444.0000463 Zhou, 2009, Microsimulation of Residential Land Development and Household Location Choices, Transp. Res. Rec.: J. Transp. Res. Board, 2077, 106, 10.3141/2077-14