Advertisement

Transportation

, Volume 44, Issue 6, pp 1261–1278 | Cite as

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

  • Jun Liu
  • Kara M. KockelmanEmail author
  • Patrick M. Boesch
  • Francesco Ciari
Article

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.

Keywords

Shared autonomous vehicles Car-sharing Agent-based simulation Mode choice Travel demand modeling 

Notes

Acknowledgements

The authors would like to thank Texas Department of Transportation (TxDOT) for financially supporting this research (under research project 6838, “Bringing Smart Transport to Texans: Ensuring the Benefits of a Connected and Autonomous Transport System in Texas”). The authors are grateful to the developers of MATSim for their consistent efforts in improving this toolkit (available at http://www.matsim.org/). Special thanks to Scott Schauer-West, for his constructive comments, edits, and administrative support.

Authors’ contribution

JL Defining the Topic, Setting-up the Method, Literature Search and Review, Data Preparation, Performing Simulations and Analysis, and Manuscript Writing; KK Defining the Topic, Setting-up the Method, Literature Search and Review, Manuscript Writing and Editing, and Research Outreach/Correspondence; and PB & FC Simulation Design and Manuscript Editing.

References

  1. AAA.: Annual Cost to Own and Operate a Vehicle Falls to $8,698, Finds AAA (2015). http://newsroom.aaa.com/2015/04/annual-cost-operate-vehicle-falls-8698-finds-aaa-archive/
  2. Anderson, J.M., Nidhi, K. Stanley, K.D. Sorensen, P. Samaras, C., Oluwatola, O.A.: Autonomous Vehicle Technology: A Guide for Policymakers. Rand Corporation (2014). http://www.rand.org/pubs/research_briefs/RB9755.html?utm_source=t.co&utm_medium=rand_social
  3. Bansal, P., Kockelman, K.M., Wang, Y.: Hybrid electric vehicle ownership and fuel economy across Texas: an application of spatial models. Transp. Res. Rec. J. Transp. Res. Board 2495, 53–64 (2015)CrossRefGoogle Scholar
  4. Barter, P.: Cars are parked 95% of the time. Let’s check! (2013). http://www.reinventingparking.org/2013/02/cars-are-parked-95-of-time-lets-check.html
  5. Bösch, P.M., Ciari, F., Axhausen, K.W.: Required autonomous vehicle fleet sizes to serve different levels of demand. In: Transportation Research Board 95th Annual Meeting, (2016). https://www.ethz.ch/content/dam/ethz/special-interest/baug/ivt/ivt-dam/vpl/reports/ab1089.pdf
  6. CAMPO.: CAMPO 2010 Planning Model Guide. Capital Area Metropolitan Planning Organization (CAMPO), Austin (2015). http://www.campotexas.org/plans-programs/
  7. Capital Metropolitan Transportation Authority.: ServicePlan2020 [Draft Final Report], Capital Metropolitan Transportation Authority (2010). http://www.capmetro.org/uploadedFiles/Capmetroorg/Future_Plans/Service_Plan_2020/ServicePlan2020%20-%20Final%20Report.pdf
  8. Capital Metropolitan Transportation Authority.: Capital metro of Austin (2016). http://www.capmetro.org/
  9. Chapin, D., Brodd, R., Cowger, G., Decicco, J., Eads, G., Espino, R., German, J., Greene, D., Greenwald, J., Hegedus, L.: Transitions to Alternative Vehicles and Fuels. National Academies Press, Washington (2013)Google Scholar
  10. Chen, D., Kockelman, K.M.: Management of a shared, autonomous, electric vehicle fleet: charging and pricing strategies. Transp. Res. Rec. J. Transp. Res. Board 2572, 37–46 (2016)CrossRefGoogle Scholar
  11. Chen, D., Kockelman, K.M., Hanna, J.: Operations of a shared, autonomous, electric vehicle (SAEV) fleet: implications of vehicle & charging infrastructure decisions. In: Transportation Research Board 95th Annual Meeting (2016). http://www.caee.utexas.edu/prof/kockelman/public_html/TRB16SAEVs100mi.pdf
  12. Chester, M., Horvath, A.: Life-cycle energy and emissions inventories for motorcycles, diesel automobiles, school buses, electric buses, Chicago rail, and New York City rail. UC Berkeley Center for Future Urban Transport: A Volvo Center of Excellence (2009). http://www.its.berkeley.edu/sites/default/files/publications/UCB/2009/VWP/UCB-ITS-VWP-2009-2.pdf
  13. Ciari, F., Balac, M., Axhausen, K.W.: Modeling car sharing with the agent-based simulation MATSim: state of the art, applications and future developments. In: Transportation Research Board 95th Annual Meeting (2016). http://e-citations.ethbib.ethz.ch/view/pub:161004
  14. Fagnant, D.J., Kockelman, K.: Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations for capitalizing on self-driven vehicles. Transp. Res. Part A 77(167–181), 2015 (2014a)Google Scholar
  15. Fagnant, D.J., Kockelman, K.M.: The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios. Transp. Res. Part C Emerg. Technol. 40, 1–13 (2014b)CrossRefGoogle Scholar
  16. Fagnant, D.J., Kockelman, K.M.: Dynamic ride-sharing and optimal fleet sizing for a system of shared autonomous vehicles. Forthcoming in Transportation (2015). http://www.caee.utexas.edu/prof/kockelman/public_html/TRB15SAVswithDRSinAustin.pdf
  17. Folsom, T.: Energy and autonomous urban land vehicles. IEEE Technol. Soc. Mag. 2, 28–38 (2012)CrossRefGoogle Scholar
  18. Gagnier, S.: Car sharing users to reach 12 million by 2020, report says (2013). http://www.autonews.com/article/20130916/OEM06/130919868/car-sharing-users-to-reach-12-million-by-2020-report-says
  19. Google.: Google self-driving car project (2016). https://www.google.com/selfdrivingcar/
  20. Greene, D.L., Liu, J., Khattatk, A.J., Whali, B., Hopson, J.L., Goeltz, R.: How does on-road fuel economy vary with vehicle cumulative mileage and daily use? Transp. Res. Part D Transp. Environ. 55, 142–161 (2017)Google Scholar
  21. Horni, A., Scott, D., Balmer, M., Axhausen, K.: Location choice modeling for shopping and leisure activities with MATSim: combining microsimulation and time geography. Transp. Res. Rec. J. Transp. Res. Board 2135, 87–95 (2009)CrossRefGoogle Scholar
  22. Horni, A., Nagel, K., Axhausen, K.W.: The Multi-Agent Transport Simulation MATSim. Ubiquity, London (2016). http://www.matsim.org/the-book
  23. Kockelman, K. M., Li, T.: Valuing the safety benefits of connected and automated vehicle technologies. In: Transportation Research Board 95th Annual Meeting (2016). http://www.caee.utexas.edu/prof/kockelman/public_html/TRB16CAVSafety.pdf
  24. Kornhauser, A.L.: Uncongested mobility for all - New Jersey’s area-wide aTaxi system, Technical Report, Operations Research and Financial Engineering, Princeton University, Princeton (2013)Google Scholar
  25. Leob, B., Kockelman K., Liu, J.: Shared autonomous electric vehicles (SAEV) operations across the Austin, Texas network with a focus on charging infrastructure decisions. In: Transportation Research Board 96th Annual Meeting (2016). http://www.caee.utexas.edu/prof/kockelman/public_html/TRB17SAEVOperations.pdf
  26. Liu, J., Khattak, A.J.: Delivering improved alerts, warnings, and control assistance using basic safety messages transmitted between connected vehicles. Transp. Res. Part C Emerg. Tech. 68, 83–100 (2016)CrossRefGoogle Scholar
  27. Liu, J., Khattak, A., Wang, X.: The role of alternative fuel vehicles: using behavioral and sensor data to model hierarchies in travel. Transp. Res. Part C Emerg. Technol. 55, 379–392 (2015)CrossRefGoogle Scholar
  28. Liu, J., Kockelman, K., Nichols, A.: Anticipating the emissions impacts of smoother driving by connected and autonomous vehicles, using the MOVES model. In: Transportation Research Board 96th Annual Meeting (2016). http://www.caee.utexas.edu/prof/kockelman/public_html/TRB17emissionsAVsmoothedcycle.pdf
  29. Maciejewski, M., Nagel, K.: Simulation and dynamic optimization of taxi services in MATSim, VSP Working Paper 13-05, TU Berlin, Transport Systems Planning and Transport Telematics (2013). http://svn.vsp.tu-berlin.de/repos/public-svn/publications/vspwp/2013/13-05/2013-06-03_Maciejewski_Nagel.pdf
  30. NCHRP.: Travel demand forecasting: parameters and techniques. National Cooperative Highway Research Program, Transportation Research Board (2012). http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_rpt_716.pdf
  31. Novosel, T., Perković, L., Ban, M., Keko, H., Pukšec, T., Krajačić, G., Duić, N.: Agent based modelling and energy planning–Utilization of MATSim for transport energy demand modelling. Energy 92, 466–475 (2015)CrossRefGoogle Scholar
  32. Paul, B., Kockelman, K., Musti, S.: Evolution of the light-duty vehicle fleet: anticipating adoption of plug-In hybrid electric vehicles and greenhouse gas emissions across the US fleet. Transp. Res. Rec. J. Transp. Res. Board 2252, 107–117 (2011)CrossRefGoogle Scholar
  33. Reiter, M.S., Kockelman, K.M.: Emissions and exposure costs of electric versus conventional vehicles: a case study for Texas. In: Transportation Research Board 95th Annual Meeting (2016). http://www.caee.utexas.edu/prof/kockelman/public_html/TRB16Emissions&Exposure.pdf
  34. Santos, A., McGuckin, N., Nakamoto, H.Y., Gray, D., Liss, S.: Summary of travel trends: 2009 national household travel survey (2011). http://nhts.ornl.gov/2009/pub/stt.pdf
  35. Schrank, D., Eisele, B., Lomax, T., Bak, J.: 2015 Urban Mobility Scorecard Texas A&M Transportation Institute & INRIX, Inc., Colleage Station, TX (2015). http://d2dtl5nnlpfr0r.cloudfront.net/tti.tamu.edu/documents/mobility-scorecard-2015.pdf
  36. Uber.: Steel City’s new wheels (2016). https://newsroom.uber.com/us-pennsylvania/new-wheels/

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Jun Liu
    • 1
  • Kara M. Kockelman
    • 2
    Email author
  • Patrick M. Boesch
    • 3
  • Francesco Ciari
    • 3
  1. 1.Center for Transportation ResearchThe University of Texas at AustinAustinUSA
  2. 2.Department of Civil, Architectural and Environmental EngineeringThe University of Texas at AustinAustinUSA
  3. 3.IVT, ETH ZurichZurichSwitzerland

Personalised recommendations