Journal of Transport and Land Use

The value of scenario discovery in land-use modeling: An automated vehicle test case

Daniel Engelberg

Northeastern University

DOI: https://doi.org/10.5198/jtlu.2024.2401

Keywords: Automated and autonomous vehicles, land use, integrated land-use and transportation modeling, agent-based simulation, scenario discovery, decision making under deep uncertainty

---

Abstract

Long-range planning is an uncertain endeavor. This is especially true for urban regions, small ships in a global urban storm that are too small to influence macro policies and without the land-use powers of local governments. Exploratory scenarios, the established practice for planning under deep uncertainty, have inspired stakeholders to consider multiple futures but have fallen short of identifying robust and contingent policies. We need new tools to plan under conditions of deep uncertainty. Scenario discovery is a technique for using simulation models to explore the performance of policy options across uncertain scenarios. This paper presents an application of scenario discovery in land-use modeling and asks what this computationally intensive approach offers relative to a more circumscribed exploration of uncertainty space. The introduction of autonomous vehicles (AVs) and their associated impacts on land use provide a test case demonstrating this method, as well as a topic of substantive concern. This research concludes that scenario discovery is particularly valuable for identifying the conditions under which contingent policies are likely to succeed. In terms of AV policy, this research establishes that forward-thinking, transit-oriented-development strategies can mitigate spatial dispersion while also reducing overall housing costs. In addition, I find that AVs may blunt the impacts of some current policy tools if they extend the distance individuals are willing to travel to work.

---

References

Andrei, L., Luca, O., & Gaman, F. (2022). Insights from user preferences on automated vehicles: Influence of socio-demographic factors on value of time in Romania case. Sustainability, 14(17), 10828. https://doi.org/10.3390/su141710828

Avin, U. (2007). Using scenarios to make plans. In L. D. Hopkins & M. A. Zapata (Eds.), Engaging the future: Forecasts, scenarios, plans, and projects (pp. 103–134). Cambridge, MA: Lincoln Institute of Land Policy.

Avin, U., & Dembner, J. (2000). Getting scenario-building right. Planning, 67(11), 22–27.

Bankes, S. (1993). Exploratory modeling for policy analysis. Operations Research, 41(3), 435–449.

Basu, R., & Ferreira, J. (2020). A LUTI microsimulation framework to evaluate long-term impacts of automated mobility on the choice of housing-mobility bundles. Environment and Planning B: Urban Analytics and City Science, 47(8), 1397–1417. https://doi.org/10.1177/2399808320925278

Bootz, J.-P. (2010). Strategic foresight and organizational learning: A survey and critical analysis. Technological Forecasting and Social Change, 77(9), 1588–1594. https://doi.org/10.1016/j.techfore.2010.06.015

Börjeson, L., Höjer, M., Dreborg, K.-H., Ekvall, T., & Finnveden, G. (2006). Scenario types and techniques: Towards a user’s guide. Futures, 38(7), 723–739. https://doi.org/10.1016/j.futures.2005.12.002

Brand, D. (2009). Impacts of higher fuel costs. Washington, DC: Federal Highway Administration. https://www.fhwa.dot.gov/policy/otps/innovation/issue1/impacts.cfm#o

Brown, A., Gonder, J., & Repac, B. (2014). An analysis of possible energy impacts of automated vehicles. In G. Meyer & S. Beiker (Eds.), Road vehicle automation. New York: Springer. https://doi.org/10.1007/978-3-319-05990-7_13

Bryant, B. P., & Lempert, R. J. (2010). Thinking inside the box: A participatory, computer-assisted approach to scenario discovery. Technological Forecasting and Social Change, 77(1), 34–49. https://doi.org/10.1016/j.techfore.2009.08.002

Chakraborty, A., & McMillan, A. (2015). Scenario planning for urban planners: Toward a practitioner’s guide. Journal of the American Planning Association, 81(1), 18–29. https://doi.org/10.1080/01944363.2015.1038576

Childress, S., Nichols, B., Charlton, B., & Coe, S. (2015). Using activity based model to explore possible impacts of autonomous vehicles, 2493(1), 99–106.

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(4), 1608. https://doi.org/10.3390/su13041608

Dawkins, C., & Moeckel, R. (2016). Transit-induced gentrification: Who will stay, and who will go? Housing Policy Debate, 26(4–5), 801–818. https://doi.org/10.1080/10511482.2016.1138986

Friedman, J. H., & Fisher, N. I. (1999). Bump hunting in high-dimensional data. Statistics and Computing, 9(2), 123–143.

Gelauff, G., Ossokina, I., & Teulings, C. (2017). Spatial effects of automated driving: Dispersion, concentration or both? Unpublished paper. Retrieved from https://doi.org/10.13140/rg.2.2.32766.48965

Gross, E. (2018). Scenario discovery for a future of automated mobility on-demand in the urban environment (master’s thesis), Massachusetts Institute of Technology, Cambridge, MA.

Groves, D. G., & Lempert, R. J. (2007). A new analytic method for finding policy-relevant scenarios. Global Environmental Change, 17(1), 73–85.

Hall, J. W., Lempert, R. J., Keller, K., Hackbarth, A., Mijere, C., & McInerney, D. J. (2012). Robust climate policies under uncertainty: A comparison of robust decision making and info-gap methods. Risk Analysis, 32(10), 1657–1672. https://doi.org/10.1111/j.1539-6924.2012.01802.x

Jafino, B. A., & Kwakkel, J. H. (2021). A novel concurrent approach for multiclass scenario discovery using multivariate regression trees: Exploring spatial inequality patterns in the Vietnam Mekong Delta under uncertainty. Environmental Modelling & Software, 145, 105177. https://doi.org/10.1016/j.envsoft.2021.105177

Jafino, B. A., Kwakkel, J. H., Klijn, F., Dung, N. V., Van Delden, H., Haasnoot, M., & Sutanudjaja, E. H. (2021). Accounting for multisectoral dynamics in supporting equitable adaptation planning: A case study on the rice agriculture in the Vietnam Mekong Delta. Earth’s Future, 9(5), e2020EF001939. https://doi.org/10.1029/2020EF001939

Kim, K.-H., Yook, D.-H., Ko, Y.-S., & Kim, D.-H. (2015). An analysis of expected effects of autonomous vehicles on transport land ese in Korea. New York: NYU, Marron Institute of Urban Management.

Knaap, G.-J., Engelberg, D., Avin, U., Erdogan, S., Ducca, F., Welch, T. F., …, & Shahumyan, H. (2020). Modeling sustainability scenarios in the Baltimore–Washington (DC) region: Implications for methodology and policy. Journal of the American Planning Association, 86(2), 250–263. https://doi.org/10.1080/01944363.2019.1680311

Kolarova, V., Steck, F., & Bahamonde-Birke, F. J. (2019). Assessing the effect of autonomous driving on value of travel time savings: A comparison between current and future preferences. Transportation Research Part A: Policy and Practice, 129, 155–169. https://doi.org/10.1016/j.tra.2019.08.011

Kwakkel, J. H. (2017). The exploratory modeling workbench: An open source toolkit for exploratory modeling, scenario discovery, and (multi-objective) robust decision making. Environmental Modelling & Software, 96, 239–250. https://doi.org/10.1016/j.envsoft.2017.06.054

Kwakkel, J. H., Auping, W. L., & Pruyt, E. (2013). Dynamic scenario discovery under deep uncertainty: The future of copper. Technological Forecasting and Social Change, 80(4), 789–800. https://doi.org/10.1016/j.techfore.2012.09.012

Kwakkel, J. H., Haasnoot, M., & Walker, W. E. (2016). Comparing robust decision-making and dynamic adaptive policy pathways for model-based decision support under deep uncertainty. Environmental Modelling & Software, 86, 168–183. https://doi.org/10.1016/j.envsoft.2016.09.017

Kwakkel, J. H., Walker, W. E., & Marchau, V. A. W. J. (2010). Classifying and communicating uncertainties in model-based policy analysis. International Journal of Technology, Policy and Management, 10(4), 299–315. https://doi.org/10.1504/IJTPM.2010.036918

Lempert, R. J., & Groves, D. G. (2010). Identifying and evaluating robust adaptive policy responses to climate change for water management agencies in the American west. Technological Forecasting and Social Change, 77(6), 960–974.

Lempert, R. J., Groves, D. G., Popper, S. W., & Bankes, S. C. (2006). A general, analytic method for generating robust strategies and narrative scenarios. Management Science, 52(4), 514–528. https://doi.org/10.1287/mnsc.1050.0472

Lempert, R., Syme, J., Mazur, G., Knopman, D., Ballard-Rosa, G., Lizon, K., & Edochie, I. (2020). Meeting climate, mobility, and equity goals in transportation planning under wide-ranging scenarios: A demonstration of robust decision making. Journal of the American Planning Association, 86(3), 311–323. https://doi.org/10.1080/01944363.2020.1727766

Litman, T. (2018). Autonomous vehicle implementation predictions (pp. 1–39). Victoria, BC: Victoria Transport Policy Institute.

Litman, T. (2022). Understanding transport demands and elasticities how prices and other factors affect travel behavior. Victoria, BC: Victoria Transport Policy Institute.

Llorca, C., Kuehnel, N., & Moeckel, R. (2020). Agent-based integrated land use/transport models: A study on scale factors and transport model simulation intervals. Procedia Computer Science, 170, 733–738. https://doi.org/10.1016/j.procs.2020.03.163

Llorca, C., Moreno, A., Ammar, G., & Moeckel, R. (2022). Impact of autonomous vehicles on household relocation: An agent-based simulation. Cities, 126, 103692. https://doi.org/10.1016/j.cities.2022.103692

Marsden, G., & McDonald, N. C. (2019). Institutional issues in planning for more uncertain futures. Transportation, 46(4), 1075–1092. https://doi.org/10.1007/s11116-017-9805-z

Mckay, M. D., Beckman, R. J., & Conover, W. J. (2000). A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 42(1), 55–61.

McPhail, C., Maier, H. R., Westra, S., Kwakkel, J. H., & Van Der Linden, L. (2020). Impact of scenario selection on robustness. Water Resources Research, 56(9), e2019WR026515. https://doi.org/10.1029/2019WR026515

Meyer, J., Becker, H., Bösch, P. M., & Axhausen, K. W. (2017). Autonomous vehicles: The next jump in accessibilities? Research in Transportation Economics, 62, 80–91. https://doi.org/10.1016/j.retrec.2017.03.005

Milkovits, M., Copperman, R., Newman, J., Lemp, J., Rossi, T., & Sun, S. (2019). Exploratory modeling and analysis for transportation: An approach and support tool - TMIP-EMAT. Transportation Research Record: Journal of the Transportation Research Board, 2673(9), 407–418. https://doi.org/10.1177/0361198119844463

Moeckel, R. (2016). Constraints in household relocation: Modeling land-use/transport interactions that respect time and monetary budgets. Journal of Transport and Land Use, 10(1), 211–228.

Papa, E., & Ferreira, A. (2018). Sustainable accessibility and the implementation of automated vehicles: Identifying critical decisions. Urban Science, 2(5), 1–14.

Schwartz, P. (1991). The art of the long view. New York: Doubleday.

Soteropoulos, A., Berger, M., & Ciari, F. (2019). Impacts of automated vehicles on travel behaviour and land use: An international review of modelling studies. Transport Reviews, 39(1), 29–49. https://doi.org/10.1080/01441647.2018.1523253

Sperling, D. (Ed.). (2018). Three revolutions: Steering autonomous, shared, and electric vehicles to a better future. Washington, DC: Island Press.

Steck, F., Kolarova, V., Bahamonde-Birke, F., Trommer, S., & Lenz, B. (2018). How autonomous driving may affect the value of travel time savings for commuting. Transportation Research Record: Journal of the Transportation Research Board, 2672(46), 11–20. https://doi.org/10.1177/0361198118757980

Stephens, T. S., Gonder, J., Chen, Y., Lin, Z., Liu, C., & Gohlke, D. (2016). Estimated bounds and important factors for fuel use and consumer costs of connected and automated vehicles (NREL/TP--5400-67216, 1334242; p. NREL/TP--5400-67216, 1334242). Washington, DC: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V) https://doi.org/10.2172/1334242

Swartz, P. G., & Zegras, P. C. (2013). Strategically robust urban planning? A demonstration of concept. Environment and Planning B: Planning and Design, 40(5), 829–845. https://doi.org/10.1068/b38135

Tadayon, M., & Shemer, L. (2013). The Maryland statewide transportation model. Model documentation (version 1.0). Baltimore, MD: SHA State Highway Administration.

Taiebat, M., Stolper, S., & Xu, M. (2019). Forecasting the impact of connected and automated vehicles on energy use: A microeconomic study of induced travel and energy rebound. Applied Energy, 247, 297–308. https://doi.org/10.1016/j.apenergy.2019.03.174

Thakur, P., Kinghorn, R., & Grace, R. (2016). Urban form and function in the autonomous era. Melbourne, Australia: Australasian Transport Research Forum.

van Delden, H., McDonald, G., Shi, Y., Hurkens, J., & van Vliet, J. (2011). Integrating socio-economic and land-use models to support urban and regional planning. Paper presented at the AGILE conference, Aug. 7–13, Salt Lake City, UT.

Wack, P. (1985). How medium-term analysis illuminated the power of scenarios for Shell management. Harvard Business Review, November-December, 139–150.

Walker, W., Haasnoot, M., & Kwakkel, J. (2013). Adapt or perish: A review of planning approaches for adaptation under deep uncertainty. Sustainability, 5(3), 955–979. https://doi.org/10.3390/su5030955

Wegener, M., & Fuerst, F. (2004). Land-use transport interaction: State of the art. Dortmund, Germany: University of Dortmund, Institute for Spatial Planning. https://doi.org/10.2139/ssrn.1434678

Wellik, T., & Kockelman, K. (2020). Anticipating land-use impacts of self-driving vehicles in the Austin, Texas, region. Journal of Transport and Land Use, 13(1), 185–205. https://doi.org/10.5198/jtlu.2020.1717

White, R., Engelen, G., & Uljee, I. (2015). Modeling cities and regions as complex systems: From theory to planning application. Cambridge, MA: MIT Press.

Xiang, W.-N., & Clarke, K. C. (2003). The use of scenarios in land-use planning. Environment and Planning B: Planning and Design, 30(6), 885–909. https://doi.org/10.1068/b2945

Zegras, C., & Rayle, L. (2012). Testing the rhetoric: An approach to assess scenario planning’s role as a catalyst for urban policy integration. Futures, 44(4), 303–318. https://doi.org/10.1016/j.futures.2011.10.013

Zegras, C., Sussman, J., & Conklin, C. (2004). Scenario planning for strategic regional transportation planning. Journal of Urban Planning and Development, 130(1), 2–13. https://doi.org/10.1061/(ASCE)0733-9488(2004)130:1(2)

Zhang, W. (2017). The interaction between land use and transportation in the era of shared autonomous vehicles: A simulation model (PhD dissertation), Georgia Institute of Technology, Atlanta, GA.

Zhang, W., Guhathakurta, S., Fang, J., & Zhang, G. (2015). Exploring the impact of shared autonomous vehicles on urban parking demand: An agent-based simulation approach. Sustainable Cities and Society, 19, 34–45. https://doi.org/10.1016/j.scs.2015.07.006

Zhang, W., & Wang, K. (2020). Parking futures: Shared automated vehicles and parking demand reduction trajectories in Atlanta. Land Use Policy, 91, 103963. https://doi.org/10.1016/j.landusepol.2019.04.024

Zhong, H., Li, W., Burris, M. W., Talebpour, A., & Sinha, K. C. (2020). Will autonomous vehicles change auto commuters’ value of travel time? Transportation Research Part D: Transport and Environment, 83, 102303. https://doi.org/10.1016/j.trd.2020.102303