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Tracking a system of shared autonomous vehicles across the Austin, Texas network using agent-based simulation

Author

Listed:
  • Jun Liu

    (The University of Texas at Austin)

  • Kara M. Kockelman

    (The University of Texas at Austin)

  • Patrick M. Boesch

    (IVT, ETH Zurich)

  • Francesco Ciari

    (IVT, ETH Zurich)

Abstract

This study provides a large-scale micro-simulation of transportation patterns in a metropolitan area when relying on a system of shared autonomous vehicles (SAVs). The six-county region of Austin, Texas is used for its land development patterns, demographics, networks, and trip tables. The agent-based MATSim toolkit allows modelers to track individual travelers and individual vehicles, with great temporal and spatial detail. MATSim’s algorithms help improve individual travel plans (by changing tour and trip start times, destinations, modes, and routes). Here, the SAV mode requests were simulated through a stochastic process for four possible fare levels: $0.50, $0.75, $1, and $1.25 per trip-mile. These fares resulted in mode splits of 50.9, 12.9, 10.5, and 9.2% of the region’s person-trips, respectively. Mode choice results show longer-distance travelers preferring SAVs to private, human-driven vehicles (HVs)—thanks to the reduced burden of SAV travel (since one does not have to drive the vehicle). For travelers whose households do not own an HV, SAVs (rather than transit, walking and biking) appear preferable for trips under 10 miles, which is the majority of those travelers’ trip-making. It may be difficult for traditional transit services and operators to survive once SAVs become available in regions like Austin, where dedicated rail lines and bus lanes are few. Simulation of SAV fleet operations suggest that higher fare rates allow for greater vehicle replacement (ranging from 5.6 to 7.7 HVs per SAV, assuming that the average SAV serves 17–20 person-trips per day); when fares rise, travel demands shift away from longer trip distances. Empty vehicle miles traveled by the fleet of SAVs ranged from 7.8 to 14.2%, across the scenarios in this study. Implications of mobility and sustainability benefits of SAVs are also discussed in the paper.

Suggested Citation

  • Jun Liu & Kara M. Kockelman & Patrick M. Boesch & Francesco Ciari, 2017. "Tracking a system of shared autonomous vehicles across the Austin, Texas network using agent-based simulation," Transportation, Springer, vol. 44(6), pages 1261-1278, November.
    • Handle: RePEc:kap:transp:v:44:y:2017:i:6:d:10.1007_s11116-017-9811-1
    DOI: 10.1007/s11116-017-9811-1
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    References listed on IDEAS

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    Cited by:

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    8. Mo, Dong & Chen, Xiqun (Michael) & Zhang, Junlin, 2022. "Modeling and Managing Mixed On-Demand Ride Services of Human-Driven Vehicles and Autonomous Vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 157(C), pages 80-119.
    9. Kassens-Noor, Eva & Dake, Dana & Decaminada, Travis & Kotval-K, Zeenat & Qu, Teresa & Wilson, Mark & Pentland, Brian, 2020. "Sociomobility of the 21st century: Autonomous vehicles, planning, and the future city," Transport Policy, Elsevier, vol. 99(C), pages 329-335.
    10. Shi, Yuji & Blainey, Simon & Sun, Chao & Jing, Peng, 2020. "A literature review on accessibility using bibliometric analysis techniques," Journal of Transport Geography, Elsevier, vol. 87(C).
    11. Peer, Stefanie & Müller, Johannes & Naqvi, Asjad & Straub, Markus, 2024. "Introducing shared, electric, autonomous vehicles (SAEVs) in sub-urban zones: Simulating the case of Vienna," Transport Policy, Elsevier, vol. 147(C), pages 232-243.
    12. Li, Li & Pantelidis, Theodoros & Chow, Joseph Y.J. & Jabari, Saif Eddin, 2021. "A real-time dispatching strategy for shared automated electric vehicles with performance guarantees," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    13. Cui, Hongjun & Yang, Yizhe & Zhu, Minqing & Ma, Xinwei & Chen, Xiuyong & Qie, Binghui, 2023. "The scheduling methods with different demand priorities for shared autonomous vehicle system in hybrid demands mode considering dynamic travel time," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    14. Loeb, Benjamin & Kockelman, Kara 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, Elsevier, vol. 121(C), pages 374-385.
    15. Becker, Henrik & Becker, Felix & Abe, Ryosuke & Bekhor, Shlomo & Belgiawan, Prawira F. & Compostella, Junia & Frazzoli, Emilio & Fulton, Lewis M. & Guggisberg Bicudo, Davi & Murthy Gurumurthy, Krishna, 2020. "Impact of vehicle automation and electric propulsion on production costs for mobility services worldwide," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 105-126.
    16. Wali, Behram & Santi, Paolo & Ratti, Carlo, 2023. "Are californians willing to use shared automated vehicles (SAV) & renounce existing vehicles? An empirical analysis of factors determining SAV use & household vehicle ownership," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    17. Kaddoura, Ihab & Bischoff, Joschka & Nagel, Kai, 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, Elsevier, vol. 136(C), pages 48-63.
    18. Nahmias-Biran, Bat-hen & Oke, Jimi B. & Kumar, Nishant, 2021. "Who benefits from AVs? Equity implications of automated vehicles policies in full-scale prototype cities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 92-107.
    19. Talebian, Ahmadreza & Mishra, Sabyasachee, 2022. "Unfolding the state of the adoption of connected autonomous trucks by the commercial fleet owner industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    20. Iacobucci, Riccardo & McLellan, Benjamin & Tezuka, Tetsuo, 2018. "Modeling shared autonomous electric vehicles: Potential for transport and power grid integration," Energy, Elsevier, vol. 158(C), pages 148-163.
    21. Kim, Sung Hoo & Circella, Giovanni & Mokhtarian, Patricia L., 2019. "Identifying latent mode-use propensity segments in an all-AV era," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 192-207.
    22. Richter, Maximilian A. & Hagenmaier, Markus & Bandte, Oliver & Parida, Vinit & Wincent, Joakim, 2022. "Smart cities, urban mobility and autonomous vehicles: How different cities needs different sustainable investment strategies," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    23. Marletto, Gerardo, 2019. "Who will drive the transition to self-driving? A socio-technical analysis of the future impact of automated vehicles," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 221-234.

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