Anticipating land-use impacts of self-driving vehicles in the Austin, Texas, region

Authors

  • Tyler Wellik
  • Kara Kockelman University of Texas

DOI:

https://doi.org/10.5198/jtlu.2020.1717

Keywords:

Autonomous vehicles, land use, integrated transportation land use modeling, urban sprawl, agent-based simulation

Abstract

This paper used an implementation of the land-use model SILO in Austin, Texas, over a 27-year period with an aim to understand the impacts of the full adoption of self-driving vehicles on the region’s residential land use. SILO was integrated with MATSim for the Austin region. Land-use and travel results were generated for a business-as-usual case (BAU) of 0% self-driving or “autonomous” vehicles (AVs) over the model timeframe versus a scenario in which households’ value of travel time savings (VTTS) was reduced by 50% to reflect the travel-burden reductions of no longer having to drive. A third scenario was also compared and examined against BAU to understand the impacts of rising vehicle occupancy (VO) and/or higher roadway capacities due to dynamic ride-sharing (DRS) options in shared AV (SAV) fleets. Results suggested an 8.1% increase in average work-trip times when VTTS fell by 50% and VO remained unaffected (the 100% AV scenario) and a 33.3% increase in the number of households with “extreme work-trips” (over 1 hour, each way) in the final model year (versus BAU of 0% AVs). When VO was raised to 2.0 and VTTS fell instead by 25% (the “Hi-DRS” SAV scenario), average work-trip times increased by 3.5% and the number of households with “extreme work-trips” increased by 16.4% in the final model year (versus BAU of 0% AVs). The model also predicted 5.3% fewer households and 19.1% more available, developable land in the city of Austin in the 100% AV scenario in the final model year relative to the BAU scenario’s final year, with 5.6% more households and 10.2% less developable land outside the city. In addition, the model results predicted 5.6% fewer households and 62.9% more available developable land in the city of Austin in the Hi-DRS SAV scenario in the final model year relative to the BAU scenario’s final year, with 6.2% more households and 9.9% less developable land outside the city.

References

City of Austin. (2019). New residential units summary by calendar year and building type. Retrieved from https://data.austintexas.gov/Building-and-Development/New-Residential-Units-Summary-by-Calendar-Year-and/2y79-8diw

Cohen, T., & Cavoli, C. (2019). Automated vehicles: Exploring possible consequences of government (non)intervention for congestion and accessibility. Transport Reviews, 39(1), 129–151.

Department of Numbers. (2017). Austin, Texas, residential rent and rental statistic. Retrieved from https://www.deptofnumbers.com/rent/texas/austin/#vacancy_rate

Dowling, R., & Morgan, A. (2019). Foreseeing the impact of transformational technologies on land use and transportation. Washington, DC: National Academy of Sciences.

Edmonds, E. (2017). AAA reveals true cost of vehicle ownership. Retrieved from https://newsroom.aaa.com/tag/driving-cost-per-mile/

Frank, P. (2019). City of Austin land-use inventory detailed. Retrieved from https://data.austintexas.gov/Locations-and-Maps/Land-Use-Inventory-Detailed/fj9m-h5qy

Hawkins, J., & Habib, K. (2018). Integrated models of land use and transportation for the autonomous vehicle revolution. Transport Reviews, 39(1), 66–83.

Horni, A., Nagel, K., & Axhausen, K. W. (2016). The multi-agent transport simulation MATSim. London: Ubiquity Press.

Huang, Y., Kockelman, K., & Quarles, N. (2019). How will self-driving vehicles affect U.S. megaregion traffic? The case of the Texas triangle. Paper presented at the 98th Annual Meeting of the Transportation Research Board, Washington, D.C., and submitted for publication in Journal of Transport Geography.

Moeckel, R. (2019a). SILO Model Java Code, GitHub. Retrieved from https://github.com/msmobility/silo/

Moeckel, R. (2018a). Simple integrated land-use orchestrator. Retrieved from silo.zone

Moeckel, R. (2018b). NCHRP synthesis 520: Integrated transportation and land use models: A synthesis of highway practice. Washington, DC: National Academy of Sciences.

Moeckel, R. (2015). Modeling constraints versus modeling utility maximization: Improving policy sensitivity for integrated land-use/transportation models. Proceedings of the 94th Annual Meeting of the Transportation Research Board, January 11–15, Washington, DC.

Muller, P. (2017). Transportation and urban form: Stages in the spatial evolution of the American metropolis. In S. Hanson & G. Giuliano (Eds.), The geography of urban transportation (pp. 57– 85). New York: Guilford Press.

National Conference of State Legislatures. (2019). Autonomous vehicles: Self-driving vehicles enacted legislation. Retrieved from http://www.ncsl.org/research/transportation/autonomous-vehicles-self-driving-vehicles-enacted-legislation.aspx

Novak, S. (2019, January 22). 2018 another record year for Austin-area housing market, Statesman. Retrieved from https://www.statesman.com/news/20190122/2018-another-record-year-for-austin-area-housing-market

Open Street Maps. (2019). Retrieved from https://www.openstreetmap.org/#map=9/30.3752/-98.3194

Quarles, N., & Kockelman K. (2019). Americans’ plans for acquiring and using electric, shared and self-driving vehicles. Under review for publication in Research in Transportation Economics.

SAE International. (2018, December 11). SAE international releases updated visual chart for its Levels of driving automation standard for self-driving vehicles. Retrieved from https://www.sae.org/news/press-room/2018/12/sae-international-releases-updated-visual-chart-for-its-%E2%80%9Clevels-of-driving-automation%E2%80%9D-standard-for-self-driving-vehicles

Simoni, M. D., Kockelman, K. M., Gurumurthy, K. M., & Bischoff, J. (2018). Congestion pricing in a world of self-driving vehicles: An analysis of different strategies in alternative future scenarios. Transportation Research Part C: Emerging Technologies 98, 167–185. Retrieved from http://www.caee.utexas.edu/prof/kockelman/public_html/TRB19CBCP_with_AVs.pdf

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

Steck, F., Kolarova, V., Bahamonde-Birke, F., Trommer, S., & Lenz, S. (2018). How autonomous driving may affect the value of travel time savings for commuting. Transportation Research Record, 2672(46), 11–20.

United States Census Bureau. (2018). American Community Survey 2017 ACS 1-year PUMS. Retrieved from https://www.census.gov/programs-surveys/acs/data/pums.html

United States Census Bureau. (2017). American fact finder mean travel time to work of workers 16 years and over who did not work at home. Retrieved from https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=bkmk

United States Department of Labor. (2019). Bureau of Labor Statistics consumer expenditure surveys. Retrieved from https://www.bls.gov/cex/tables.htm

United States Department of Transportation. (2018). Summary of travel trends: 2017 National Household Travel Survey. Retrieved from https://nhts.ornl.gov/assets/2017_nhts_summary_travel_trends.pdf

USA Today. (2019, May 9). America’s fastest growing cities. Retrieved from https://www.usatoday.com/picture-gallery/money/2019/05/03/americas-fastest-growing-cities/39442563/

Ziemke, D., Nagel, K., & Moeckel, R. (2016). Towards an agent-based, integrated land-use transport modeling system. Procedia Computer Science, 83, 958–963.

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Published

2020-08-06

How to Cite

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

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Articles