Abstract
This paper explores the effects of day of week and season of year demand variations for shared rides, along with realistic travel party sizes, on shared autonomous vehicle (SAV) services across the Austin, Texas region. Using the agent-based POLARIS program, synthetic person-trips that reflect travel-party size (from one to four persons) and demand variations over days and months, as evident in the National Household Travel Survey data were simulated in each scenario over a 24 h travel day. Results show that realistic party sizes can bring considerable changes to SAV fleet performance, including up to 8.5% higher service rates (number of requests accepted within 15 min), 5 min shorter journey times (wait time + travel time), 28% higher vehicle occupancies on weekends, and roughly 4% lower empty fleet VMT. Weekend travel is most impacted by season of year, with weekday travel patterns looking more uniform (thanks to work and school trips). Various performance metrics for the Austin network, like total and empty VMT, change by up to 30% when considering realistic variations in party size and time of year. This paper underscores the value of recognizing day-to-day and month-to-month variations in travel demand, and the importance of agent-based model equations to reflect travel-party size. Such realism can help quantify SAV seat occupancies more accurately, highlighting the importance of shared mobility. However, it also creates demand and supply issues for operators that now need more information on party size to manage dynamic ride-sharing, or those that may wish to shift their fleet vehicles to other regions for special events to protect profits while offering reasonable wait times to customers throughout the year.
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References
Alonso-Mora, J., Samaranayake, S., Wallar, A., Frazzoli, E., Rus, D.: On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment. Proc. Natl. Acad. Sci. 114, 462–467 (2017). https://doi.org/10.1073/pnas.1611675114
Auld, J., Mohammadian, A.: Efficient methodology for generating synthetic populations with multiple control levels. Transp. Res. Rec. 2175, 138–147 (2010). https://doi.org/10.3141/2175-16
Auld, J., Mohammadian, A.: Planning-constrained destination choice in activity-based model: Agent-based dynamic activity planning and travel scheduling. Transp. Res. Rec. 2254, 170–179 (2011). https://doi.org/10.3141/2254-18
Auld, J., Mohammadian, A.K.: Activity planning processes in the agent-based dynamic activity planning and travel scheduling (ADAPTS) model. Transp. Res. Part a Policy Pract. 46, 1386–1403 (2012). https://doi.org/10.1016/j.tra.2012.05.017
Auld, J., Rashidi, T., Javanmardi, M., Mohammadian, A.: Dynamic activity generation model using competing hazard formulation. Transp. Res. Rec. 2254, 28–35 (2011). https://doi.org/10.3141/2254-04
Auld, J., Hope, M., Ley, H., Sokolov, V., Xu, B., Zhang, K.: POLARIS: agent-based modeling framework development and implementation for integrated travel demand and network and operations simulations. Transp. Res. Part c Emerg. Technol. 64, 101–116 (2016). https://doi.org/10.1016/j.trc.2015.07.017
Ayebare, P.A., Gurumurthy, K.M., Kockelman, K.M. and Huang, Y.: Shared automous vehicle operations across distinct dallas-fort worth geofences. Presented at presented at the bridging transportation researchers conference (August 2022) and at the 102nd annual meeting of the transportation research board (January 2023) (2022)
Bar-On, R.R.: International tourism in 2001, and tracking trends in travel and tourism with seasonal adjustment. Tour. Econ. 8, 231–253 (2002). https://doi.org/10.5367/000000002101298089
Bischoff, J., Maciejewski, M.: Simulation of City-wide Replacement of Private Cars with Autonomous Taxis in Berlin. Procedia Computer Science, The 7th International Conference on Ambient Systems, Networks and Technologies (ANT 2016) / The 6th International Conference on Sustainable Energy Information Technology (SEIT-2016)/Affiliated Workshops 83, (2016), 237–244. https://doi.org/10.1016/j.procs.2016.04.121
Bösch, P.M., Becker, F., Becker, H., Axhausen, K.W.: Cost-based analysis of autonomous mobility services. Transp. Policy 64, 76–91 (2018). https://doi.org/10.1016/j.tranpol.2017.09.005
Bradley, M., Outwater, M.L., Ferdous, N.: Implementation of a practical model system to predict long-distance travel for the entire U.S. population. Transp. Res. Record 2563, 9–17 (2016). https://doi.org/10.3141/2563-02
California Department of Transportation: 2010–2012 California Household Travel Survey Final Report, (2013)
Chakrabarti, S.: Does telecommuting promote sustainable travel and physical activity? J. Transp. Health 9, 19–33 (2018). https://doi.org/10.1016/j.jth.2018.03.008
Chen, T.D., Kockelman, K.M.: Management of a shared autonomous electric vehicle fleet: Implications of pricing schemes. Transp. Res. Rec. 2572, 37–46 (2016). https://doi.org/10.3141/2572-05
Clements, L.M., Kockelman, K.M.: Economic effects of automated vehicles. Transp. Res. Rec. 2606, 106–114 (2017). https://doi.org/10.3141/2606-14
Dadashova, B., Griffin, G.: Guide for Seasonal Adjustment and Crowdsourced Data Scaling (Cooperative Research Program No. Technical Report 0–6927-P6, (2018)
Dean, M.D., Gurumurthy, K.M., de Souza, F., Auld, J., Kockelman, K.M.: Synergies between repositioning and charging strategies for shared autonomous electric vehicle fleets. Transp. Res. Part d: Transp. Environ. 108, 103314 (2022). https://doi.org/10.1016/j.trd.2022.103314
Elango, V.V., Guensler, R., Ogle, J.: Day-to-day travel variability in the commute Atlanta, Georgia, study. Transp. Res. Rec. 2014, 39–49 (2007). https://doi.org/10.3141/2014-06
Fagnant, D.J., Kockelman, K.M.: Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas. Transportation 45, 143–158 (2018). https://doi.org/10.1007/s11116-016-9729-z
Farhan, J., Chen, T.D.: Impact of ridesharing on operational efficiency of shared autonomous electric vehicle fleet. Transp. Res. Part c Emerg. Technol. 93, 310–321 (2018). https://doi.org/10.1016/j.trc.2018.04.022
Federal Highway Administration: 2017. 2017 National Household Travel Survey, U.S. Department of Transportation, Washington, DC. URL https://nhts.ornl.gov (accessed 12.26.22).
Federal Highway Administration: Foundational Knowledge to Support a Long-Distance Passenger Travel Demand Modeling Framework Final Report Exploratory Advanced Research Program. U.S Department of transportation, Federal Highway Administration. (2015}.
Grigolon, A., Kemperman, A., Timmermans, H.: Facet-based analysis of vacation planning processes: a binary mixed logit panel model. J. Travel Res. 52, 192–201 (2013). https://doi.org/10.1177/0047287512459107
Gurumurthy, K.M., Kockelman, K.M.: Dynamic ride-sharing impacts of greater trip demand and aggregation at stops in shared autonomous vehicle systems. Transp. Res. Part a Policy Pract. 160, 114–125 (2022). https://doi.org/10.1016/j.tra.2022.03.032
Gurumurthy, K.M., Kockelman, K.M., Simoni, M.D.: Benefits and costs of ride-sharing in shared automated vehicles across austin, texas: opportunities for congestion pricing. Transp. Res. Rec. 2673, 548–556 (2019). https://doi.org/10.1177/0361198119850785
Gurumurthy, K.M., de Souza, F., Enam, A., Auld, J.: Integrating supply and demand perspectives for a large-scale simulation of shared autonomous vehicles. Transp. Res. Rec. 2674, 181–192 (2020). https://doi.org/10.1177/0361198120921157
Hasnine, M.S., Hawkins, J., Habib, K.N.: Effects of built environment and weather on demands for transportation network company trips. Transp. Res. Part a Policy Pract. 150, 171–185 (2021). https://doi.org/10.1016/j.tra.2021.06.011
Hörl, S.: Agent-based simulation of autonomous taxi services with dynamic demand responses, in: Procedia Computer Science. pp. 899–904, (2017). https://doi.org/10.1016/j.procs.2017.05.418
Horl, S., Balac, M., Axhausen, K.W.: Dynamic demand estimation for an AMoD system in Paris, in: IEEE Intelligent Vehicles Symposium, Proceedings. IEEE, pp. 260–266, (2019). https://doi.org/10.1109/IVS.2019.8814051
Hsieh, S., O’Leary, J., Morrison, A.M.: Modelling the travel mode choice of australian outbound travellers. J. Tour. Stud. 4, 51–61 (1993)
Huang, Y., Kockelman, K.M., Garikapati, V.: Shared automated vehicle fleet operations for first-mile last-mile transit connections with dynamic pooling. Comput. Environ. Urban Syst. 92, 101730 (2022a). https://doi.org/10.1016/j.compenvurbsys.2021.101730
Huang, Y., Kockelman, K.M., Zuniga-Garcia, N.: Long-distance Travel Impacts of Automated Vehicles: A Survey of American Households. https://www.caee.utexas.edu/prof/kockelman/public_html/TRB22LD-AVUSSURVEY.pdf, (2022b)
Huang, Y., Kockelman, K.M., Truong, L.T.: SAV operations on a bus line corridor: travel demand, service frequency, and vehicle size. J. Adv. Transp. 2021, e5577500 (2021). https://doi.org/10.1155/2021/5577500
Hyland, M., Mahmassani, H.S.: Dynamic autonomous vehicle fleet operations: optimization-based strategies to assign AVs to immediate traveler demand requests. Transp. Res. Part c Emerg. Technol. 92, 278–297 (2018). https://doi.org/10.1016/j.trc.2018.05.003
Inturri, G., Giuffrida, N., Ignaccolo, M., Le Pira, M., Pluchino, A., Rapisarda, A., D’Angelo, R.: Taxi vs. demand responsive shared transport systems: an agent-based simulation approach. Transp. Policy 103, 116–126 (2021). https://doi.org/10.1016/j.tranpol.2021.01.002
Jing, P., Hu, H., Zhan, F., Chen, Y., Shi, Y.: Agent-based simulation of autonomous vehicles: a systematic literature review. IEEE Access 8, 79089–79103 (2020). https://doi.org/10.1109/ACCESS.2020.2990295
Kondor, D., Zhang, H., Tachet, R., Santi, P., Ratti, C.: Estimating savings in parking demand using shared vehicles for home-work commuting. IEEE Trans. Intell. Transp. Syst. 20, 2903–2912 (2019). https://doi.org/10.1109/TITS.2018.2869085
Levin, M.W., Kockelman, K.M., Boyles, S.D., Li, T.: A general framework for modeling shared autonomous vehicles with dynamic network-loading and dynamic ride-sharing application. Comput. Environ. Urban Syst. 64, 373–383 (2017). https://doi.org/10.1016/j.compenvurbsys.2017.04.006
Loeb, B., Kockelman, K.M., Liu, J.: Shared autonomous electric vehicle (SAEV) operations across the Austin, Texas network with charging infrastructure decisions. Transp. Res. Part c Emerg. Technol. 89, 222–233 (2018). https://doi.org/10.1016/j.trc.2018.01.019
Lokhandwala, M., Cai, H.: Dynamic ride sharing using traditional taxis and shared autonomous taxis: a case study of NYC. Transp. Res. Part c Emerg. Technol. 97, 45–60 (2018). https://doi.org/10.1016/j.trc.2018.10.007
Maciejewski, M., Bischoff, J.: Congestion effects of autonomous taxi fleets. Transport 33, 971–980 (2018). https://doi.org/10.3846/16484142.2017.1347827
Martínez, J.M.G., Martín, J.M.M., del Rey, M.S.O.: An analysis of the changes in the seasonal patterns of tourist behavior during a process of economic recovery. Technol. Forecast. Soc. Chang. 161, 120280 (2020). https://doi.org/10.1016/j.techfore.2020.120280
McGuckin, N., Fucci, A.: Summary of Travel Trends 2001 National Household Travel Survey (No. ORNL/TM-2004/297, 885762). (2018), https://doi.org/10.2172/885762
Milakis, D., Snelder, M., Arem, B., van Wee, B., van de Correia, G.H.A.: Development and transport implications of automated vehicles in the Netherlands: scenarios for 2030 and 2050. Eur. J. Transp. Infrastruct. Res. (2017). https://doi.org/10.18757/ejtir.2017.17.1.3180
Miller, E.J.: The trouble with intercity travel demand models. Transp. Res. Rec. 1895, 94–101 (2004). https://doi.org/10.3141/1895-13
Müller, S., Mejia-Dorantes, L., Kersten, E.: Analysis of active school transportation in hilly urban environments: a case study of Dresden. J. Transp. Geogr. 88, 102872 (2020). https://doi.org/10.1016/j.jtrangeo.2020.102872
Nahmias-Biran, B., Oke, J.B., Kumar, N., Lima Azevedo, C., Ben-Akiva, M.: Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach. Transportation 48, 1613–1638 (2021). https://doi.org/10.1007/s11116-020-10106-y
Narayanan, S., Chaniotakis, E., Antoniou, C.: Shared autonomous vehicle services: a comprehensive review. Transp. Res. Part c Emerg. Technol. 111, 255–293 (2020). https://doi.org/10.1016/j.trc.2019.12.008
Rashidi, T.H., Koo, T.T.R.: An analysis on travel party composition and expenditure: a discrete-continuous model. Ann. Tour. Res. 56, 48–64 (2016). https://doi.org/10.1016/j.annals.2015.10.003
Simoni, M.D., Kockelman, K.M., Gurumurthy, K.M., Bischoff, J.: Congestion pricing in a world of self-driving vehicles: an analysis of different strategies in alternative future scenarios. Transp. Res. Part c Emerg. Technol. 98, 167–185 (2019). https://doi.org/10.1016/j.trc.2018.11.002
Stamatiadis, N., Allen, D.L.: Seasonal factors using vehicle classification data. Transp. Res. Rec. 1593, 23–28 (1997). https://doi.org/10.3141/1593-04
Stefan, K.J., Brownlee, A.T., Hunt, J.D.: Unifying long- and short-distance personal travel in a statewide planning model. Transp. Res. Rec. 2563, 1–8 (2016). https://doi.org/10.3141/2563-01
Thrane, C., Farstad, E.: Domestic tourism expenditures: the non-linear effects of length of stay and travel party size. Tour. Manage. 32, 46–52 (2011). https://doi.org/10.1016/j.tourman.2009.11.002
Winter, K., Cats, O., Martens, K., van Arem, B.: Relocating shared automated vehicles under parking constraints: assessing the impact of different strategies for on-street parking. Transportation 48, 1931–1965 (2021). https://doi.org/10.1007/s11116-020-10116-w
Zhang, L., Lu, Y., Ghader, S., Carrion, C., Asadabadi, A., Yang, D.: Person-based micro-simulation demand model for national long-distance travel in the U.S.A. Transp. Res. Rec. 2674, 297–309 (2020). https://doi.org/10.1177/0361198120919119
Zhao, X., Lu, X., Liu, Y., Lin, J., An, J.: Tourist movement patterns understanding from the perspective of travel party size using mobile tracking data: a case study of Xi’an, China. Tour. Manage. 69, 368–383 (2018). https://doi.org/10.1016/j.tourman.2018.06.026
Acknowledgements
The authors acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC and database resources that have contributed to the research results reported within this paper. The authors thank the Texas Department of Transportation (TxDOT) for financially supporting this research, under research project 0-7081, “Understanding the Impact of Autonomous Vehicles on Long Distance Travel Mode and Destination Choice in Texas”. The authors also thank Jade (Maizy) Jeong and Aditi Bhaskar for editorial and submission supports.
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The work done in this paper was sponsored by the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO) under the Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Laboratory Consortium, an initiative of the Energy Efficient Mobility Systems (EEMS) Program. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government.
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The authors confirm their contributions to the paper as follows: study conception and design: YH, KK; Establishment of simulation models: YH and KMG; analysis and interpretation of results: YH; draft manuscript preparation: YH, KMG and KK. All authors reviewed the results and approved the final version of the manuscript.
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Huang, Y., Kockelman, K.M. & Gurumurthy, K.M. Agent-based simulations of shared automated vehicle operations: reflecting travel-party size, season and day-of-week demand variations. Transportation (2024). https://doi.org/10.1007/s11116-023-10454-5
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DOI: https://doi.org/10.1007/s11116-023-10454-5