A system of shared autonomous vehicles for Chicago: Understanding the effects of geofencing the service
Krishna Murthy Gurumurthy
University of Texas at Austin
Joshua Auld
Argonne National Laboratory
Kara Kockelman
University of Texas
DOI: https://doi.org/10.5198/jtlu.2021.1926
Keywords: shared autonomous vehicles, geofences, agent-based simulation, chicago, POLARIS, dynamic ride-sharing
Abstract
With autonomous vehicles (AVs) still in the testing phase, researchers and planners must resort to simulation techniques to explore possible futures regarding shared and automated mobility. An agent-based discrete-event transport simulator, POLARIS, is used in this study to simulate travel in the 20-county Chicago region with a shared AV (SAV) mobility option. Using this framework, the effect of an SAV fleet on system performance when constrained to serve within geofences is studied under four distinct scenarios: service restricted to the city, to the city plus suburban core, to the core plus exurban areas, and to the entire region — along with the choice of dynamic ridesharing (DRS) versus solo travel in an SAV. Results indicate that service areas need a balanced mix of trip generators and attractors, and an SAV fleet’s empty VMT (eVMT) can be noticeably reduced through suitable geofencing and DRS. Geofences can also help lower response times, reduce systemwide VMT across all modes, and ensure uniform access to SAVs. DRS is most useful in lowering VMT and %eVMT that arises from sprawled land development, but with insufficient demand to share rides, savings from the use of geofences is higher. Geofences targeting neighborhoods with high trip density bring about low response times and %eVMT, but fleet sizes in these regions need to be designed for uniformly low response times throughout a large region, as opposed to maximizing vehicle use in a 24-hour day.
References
Alonso-Mora, J., Samaranayake, S., Wallar, A., Frazzoli, E., & Rus, D. (2017). On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment. Proceedings of the National Academy of Sciences, 114, 462–467. https://doi.org/10.1073/pnas.1611675114
American Automobile Association. (2019). Your driving costs: How much are you really paying to drive? Heathrow, FL: American Automobile Association.
Auld, J., Hope, M., Ley, H., Sokolov, V., Xu, B., & Zhang, K. (2016). POLARIS: Agent-based modeling framework development and implementation for integrated travel demand and network and operations simulations. Transportation Research Part C: Emerging Technologies, 64, 101–116. https://doi.org/10.1016/j.trc.2015.07.017
Auld, J., & Mohammadian, A. (2012). Activity planning processes in the agent-based dynamic activity planning and travel scheduling (ADAPTS) model. Transportation Research Part A: Policy and Practice, 46, 1386–1403. https://doi.org/10.1016/j.tra.2012.05.017
Auld, J., & Mohammadian, A. (2009). Framework for the development of the agent-based dynamic activity planning and travel scheduling (ADAPTS) model. Transportation Letters 1, 245–255. https://doi.org/10.3328/TL.2009.01.03.245-255
Bansal, P., & Kockelman, K. M. (2017). Forecasting Americans’ long-term adoption of connected and autonomous vehicle technologies. Transportation Research Part A: Policy Practice, 95, 49–63. https://doi.org/10.1016/j.tra.2016.10.013
Becker, H., Becker, F., Abe, R., Bekhor, S., Belgiawan, P. F., Compostella, J., … & Axhausen, K. W. (2020). Impact of vehicle automation and electric propulsion on production costs for mobility services worldwide. Transportation Research Part A: Policy and Practice, 138, 105–126. https://doi.org/10.1016/j.tra.2020.04.021
Bilali, A., Dandl, F., Fastenrath, U., & Bogenberger, K. (2019). Impact of service quality factors on ride sharing in urban areas. Paper presented at the 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), Kraków, Poland. https://doi.org/10.1109/MTITS.2019.8883364
Bischoff, J., & Maciejewski, M. (2016). Simulation of city-wide replacement of private cars with autonomous taxis in Berlin. Procedia Computer Science, 83, 237–244. https://doi.org/10.1016/j.procs.2016.04.121
Bösch, P. M., Becker, F., Becker, H., & Axhausen, K. W. (2018). Cost-based analysis of autonomous mobility services. Transportation Policy, 64, 76–91. https://doi.org/10.1016/j.tranpol.2017.09.005
Bösch, P. M., Ciari, F., & Axhausen, K. W. (2016). Autonomous vehicle fleet sizes required to serve different levels of demand. Transportation Research Record, 2542, 111–119. https://doi.org/10.3141/2542-13
Brownell, C., & Kornhauser, A. (2014). A driverless alternative: Fleet size and cost requirements for a statewide autonomous taxi network in New Jersey. Transportation Research Record, 2416, 73–81. https://doi.org/10.3141/2416-09
de Souza, F., Verbas, O., & Auld, J. (2019). Mesoscopic traffic flow model for agent-based simulation. Procedia Computer Science, 151, 858–863. https://doi.org/10.1016/j.procs.2019.04.118
Fagnant, D. J., & Kockelman, K. (2015). Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice, 77, 167–181. https://doi.org/10.1016/j.tra.2015.04.003
Fagnant, D. J., & Kockelman, K. M. (2018). Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas. Transportation, 45, 143–158. https://doi.org/10.1007/s11116-016-9729-z
Fagnant, D. J., Kockelman, K. M., & Bansal, P. (2015). Operations of shared autonomous vehicle fleet for Austin, Texas, market. Transportaion Research Record, 2536, 98–106. https://doi.org/10.3141/2536-12
Gurumurthy, K. M., de Souza, F., Enam, A., & Auld, J. (2020). Integrating supply and demand perspectives for a large-scale simulation of shared autonomous vehicles. Transportation Research Record, 2674, 181–192. https://doi.org/10.1177/0361198120921157
Gurumurthy, K. M., & Kockelman, K. M. (2020a). Modeling Americans’ autonomous vehicle preferences: A focus on dynamic ride-sharing, privacy and long-distance mode choices. Technological Forecasting and Social Change, 150, 119792. https://doi.org/10.1016/j.techfore.2019.119792
Gurumurthy, K. M., & Kockelman, K. M. (2020b). Dynamic ride-sharing impacts of greater trip demand and aggregation at stops. Under review for publication in Transportation Research Part A’s special issue The Curb Lane.
Gurumurthy, K. M., & Kockelman, K. M. (2018). Analyzing the dynamic ride-sharing potential for shared autonomous vehicle fleets using cellphone data from Orlando, Florida. Computers, Environment and Urban Systems, 71, 177–185. https://doi.org/10.1016/j.compenvurbsys.2018.05.008
Gurumurthy, K. M., Kockelman, K. M., & Simoni, M .D. (2019). Benefits and costs of ride-sharing in shared automated vehicles across Austin, Texas: Opportunities for congestion pricing. Transportation Research Record, 2673, 548–556. https://doi.org/10.1177/0361198119850785
Heilig, M., Hilgert, T., Mallig, N., Kagerbauer, M., & Vortisch, P. (2017). Potentials of autonomous vehicles in a changing private transportation system – a case study in the Stuttgart region. Transporation Research Procedia, 26, 13–21. https://doi.org/10.1016/j.trpro.2017.07.004
Horni, A., Nagel, K., Axhausen, K. W. (Eds.). (2016). The multi-agent transport simulation MATSim. London: Ubiquity Press. Retrieved from https://doi.org/10.5334/baw
Juliussen, E., & Carlson, J. (2014). Emerging technologies: Autonomous cars–Not if, but when. IHS Automotive. http://press.ihs.com/press-release/automotive/self-driving-cars-movingindustrys-drivers-seat.
Kaddoura, I., Bischoff, J., & Nagel, K. (2020). Towards welfare optimal operation of innovative mobility concepts: External cost pricing in a world of shared autonomous vehicles. Transportation Research Part A: Policy and Practice, 136, 48–63. https://doi.org/10.1016/j.tra.2020.03.032
Krueger, R., Rashidi, T. H., & Rose, J. M. (2016). Preferences for shared autonomous vehicles. Transportation Research Part C: Emerging Technologies, 69, 343–355. https://doi.org/10.1016/j.trc.2016.06.015
Lavieri, P. S., & Bhat, C. R. (2019). Modeling individuals’ willingness to share trips with strangers in an autonomous vehicle future. Transportation Research Part A: Policy and Practice, 124, 242–261. https://doi.org/10.1016/j.tra.2019.03.009
Lavieri, P. S., Garikapati, V. M., Bhat, C. R., Pendyala, R. M., Astroza, S., & Dias, F. F. (2017). Modeling individual preferences for ownership and sharing of autonomous vehicle technologies. Transportation Research Record, 2665, 1–10. https://doi.org/10.3141/2665-01
Lee, J., & Kockelman, K. M. (2019). Energy and emissions implications of self-driving vehicles. Paper presented at the 98th Annual Meeting of the Transportation Research Board, Washington, DC.
Liu, J., Kockelman, K. M., Bösch, P. M., & Ciari, F. (2017). Tracking a system of shared autonomous vehicles across the Austin, Texas, network using agent-based simulation. Transportation, 44, 1261–1278. https://doi.org/10.1007/s11116-017-9811-1
Loeb, B., & Kockelman, K.M. (2019). Fleet performance and cost evaluation of a shared autonomous electric vehicle (SAEV) fleet: A case study for Austin, Texas. Transportation Research Part A: Policy and Practice, 121, 374–385. https://doi.org/10.1016/j.tra.2019.01.025
Loeb, B., Kockelman, K. M., & Liu, J. (2018). Shared autonomous electric vehicle (SAEV) operations across the Austin, Texas, network with charging infrastructure decisions. Transportation Research Part C: Emerging Technologies, 89, 222–233. https://doi.org/10.1016/j.trc.2018.01.019
Martinez, L. M., & Viegas, J. M. (2017). Assessing the impacts of deploying a shared self-driving urban mobility system: An agent-based model applied to the city of Lisbon, Portugal. International Journal of Transportation, Science and Technology, 6, 13–27. https://doi.org/10.1016/j.ijtst.2017.05.005
Menon, N., Barbour, N., Zhang, Y., Pinjari, A. R., & Mannering, F. (2019). Shared autonomous vehicles and their potential impacts on household vehicle ownership: An exploratory empirical assessment. International Journal of Sustainable Transportation, 13, 111–122. https://doi.org/10.1080/15568318.2018.1443178
Quarles, N. T., Kockelman, K. M., & Lee, J. (2020). Americans’ plans for acquiring and using electric, shared and self-driving vehicles. Paper presented at the 99th Annual Meeting of the Transportation Research Board, Washington, DC.
Quarles, N. T., Kockelman, K. M., & Lee, J. (2019). America’s fleet evolution in an automated future. Forthcoming in Research in Transportation Economics.
Ross, C., & Guhathakurta, S. (2017). Autonomous vehicles and energy impacts: A scenario analysis. Energy Procedia, 143, 47–52. https://doi.org/10.1016/j.egypro.2017.12.646
Simoni, M. D., Kockelman, K. M., Gurumurthy, K. M., & Bischoff, J. (2019). 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. https://doi.org/10.1016/j.trc.2018.11.002
Spieser, K., Treleaven, K., Zhang, R., Frazzoli, E., Morton, D., & Pavone, M. (2014). Toward a systematic approach to the design and evaluation of automated mobility-on-demand systems: A case study in Singapore. In G. Meyer & S. Beiker (Eds.), Road vehicle automation, lecture notes in mobility (pp. 229–245). New York: Springer International Publishing. https://doi.org/10.1007/978-3-319-05990-7_20
Stoiber, T., Schubert, I., Hoerler, R., & Burger, P. (2019). Will consumers prefer shared and pooled-use autonomous vehicles? A stated choice experiment with Swiss households. Transportation Research Part D: Transport and Environment, 17, 265–285. https://doi.org/10.1016/j.trd.2018.12.019
Verbas, Ö., Auld, J., Ley, H., Weimer, R., & Driscoll, S. (2018). Time-dependent intermodal A* algorithm: Methodology and implementation on a large-scale network. Transportation Research Record, 2672, 219–230. https://doi.org/10.1177/0361198118796402
Yan, H., Kockelman, K. M., & Gurumurthy, K. M. (2020). Shared autonomous vehicle fleet performance: Impacts of parking limitations and trip densities. Transportation Research Part D 89, 102577.