Abstract
Fleet operators rely on forecasts of future user requests to reposition empty vehicles and efficiently operate their vehicle fleets. In the context of an on-demand shared-use autonomous vehicle (AV) mobility service (SAMS), this study analyzes the trade-off that arises when selecting a spatio-temporal demand forecast aggregation level to support the operation of a SAMS fleet. In general, when short-term forecasts of user requests are intended for a finer space–time discretization, they tend to become less reliable. However, holding reliability constant, more disaggregate forecasts provide more valuable information to fleet operators. To explore this trade-off, this study presents a flexible methodological framework to evaluate and quantify the impact of spatio-temporal demand forecast aggregation on the operational efficiency of a SAMS fleet. At the core of the methodological framework is an agent-based simulation that requires a demand forecasting method and a SAMS fleet operational strategy. This study employs an offline demand forecasting method, and an online joint AV-user assignment and empty AV repositioning strategy. Using this forecasting method and fleet operational strategy, as well as Manhattan, NY taxi data, this study simulates the operations of a SAMS fleet across various spatio-temporal aggregation levels. Results indicate that as demand forecasts (and subregions) become more spatially disaggregate, fleet performance improves, in terms of user wait time and empty fleet miles. This finding comes despite demand forecast quality decreasing as subregions become more spatially disaggregate. Additionally, results indicate the SAMS fleet significantly benefits from higher quality demand forecasts, especially at more disaggregate levels.
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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. U. S. A. 114, 462–467 (2017). https://doi.org/10.1073/pnas.1611675114
Boesch, P.M., Ciari, F., Axhausen, K.W.: Autonomous vehicle fleet sizes required to serve different levels of demand. Transp. Res. Rec. J. Transp. Res. Board 2542, 111–119 (2016). https://doi.org/10.3141/2542-13
Brownell, C., Kornhauser, A.: A driverless alternative: fleet size and cost requirements for a statewide autonomous taxi network in New Jersey. Transp. Res. Rec. J. Transp. Res. Board 2416, 73–81 (2014). https://doi.org/10.3141/2416-09
Chaniotakis, E., Antoniou, C., Pereira, F.: Mapping social media for transportation studies. IEEE Intell. Syst. 31, 64–70 (2016). https://doi.org/10.1109/MIS.2016.98
Dandl, F., Bogenberger, K.: Comparing future autonomous electric taxis with an existing free-floating carsharing system. IEEE Trans. Intell. Transp. Syst. 7, 1–11 (2018)
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 (2014). https://doi.org/10.1016/j.trc.2013.12.001
Fagnant, D.J., Kockelman, K.M., Bansal, P.: Operations of shared autonomous vehicle fleet for Austin, Texas. Market. Transp. Res. Rec. J. Transp. Res. Board 2536, 98–106 (2015). https://doi.org/10.3141/2536-12
Hörl, S., Ruch, C., Becker, F., Frazzoli, E., Axhausen, K.W.: Fleet control algorithms for automated mobility: a simulation assessment for Zurich. In: TRB 2018 Annual Meeting (2017)
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
Hyland, M.F., Mahmassani, H.S.: Taxonomy of shared autonomous vehicle fleet management problems to inform future transportation mobility. Transp. Res. Rec. J. Transp. Res. Board 2653, 26–34 (2017). https://doi.org/10.3141/2653-04
IBM: Descriptive, predictive, prescriptive: transforming asset and facilities management with analytics. IBM Corporation, Somers, NY (2017). https://www.ibm.com/downloads/cas/3V9AA9Y5
Ichoua, S., Gendreau, M., Potvin, J.-Y.: Exploiting knowledge about future demands for real-time vehicle dispatching. Transp. Sci. 40, 211–225 (2006). https://doi.org/10.1287/trsc.1050.0114
Ihler, A., Hutchins, J., Smyth, P.: Adaptive event detection with time-varying poisson processes. Proc. 12th ACM SIGKDD Int. Conf. Knowl. Discov. data Min. KDD’06 (2006). https://doi.org/10.1145/1150402.1150428
Internal Revenue Service: Standard mileage rates (2018). https://www.irs.gov/tax-professionals/standard-mileage-rates. Accessed 17 Feb 2019
Jaillet, P., Wagner, M.R.: Online routing problems: value of advanced information as improved competitive ratios. Transp. Sci. 40, 200–210 (2006)
Krueger, R., Rashidi, T.H., Rose, J.M.: Preferences for shared autonomous vehicles. Transp. Res. Part C Emerg. Technol. 69, 343–355 (2016). https://doi.org/10.1016/j.trc.2016.06.015
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
Maciejewski, M., Bischoff, J., Nagel, K.: An assignment-based approach to efficient real-time city-scale taxi dispatching. IEEE Intell. Syst. 31, 68–77 (2016). https://doi.org/10.1109/MIS.2016.2
Makridakis, S.: Metaforecasting: ways of improving forecasting accuracy and usefulness. Int. J. Forecast. 4, 467–491 (1988)
Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., Damas, L.: Predicting taxi-passenger demand using streaming data. IEEE Trans. Intell. Transp. Syst. 14, 1393–1402 (2013). https://doi.org/10.1109/TITS.2013.2262376
Müller, J., Bogenberger, K.: Time series analysis of booking data of a free-floating carsharing system in Berlin. Transp. Res. Procedia 10, 345–354 (2015). https://doi.org/10.1016/j.trpro.2015.09.084
NYC Taxi & Limousine Commission: TLC trip record data (2017). http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml. Accessed 17 Feb 2019
Pavone, M., Smith, S.L., Frazzoli, E., Rus, D.: Robotic load balancing for mobility-on-demand systems. Int. J. Rob. Res. 31, 839–854 (2012). https://doi.org/10.1177/0278364912444766
Powell, W.B., Simao, H.P., Bouzaiene-Ayari, B.: Approximate dynamic programming in transportation and logistics: a unified framework. EURO J. Transp. Logist. 1, 237–284 (2012). https://doi.org/10.1007/s13676-012-0015-8
Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm (2014). http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A746893&dswid=1021. Accessed 17 Feb 2019
Sayarshad, H.R., Chow, J.Y.J.: Survey and empirical evaluation of nonhomogeneous arrival process models with taxi data. J. Adv. Transp. 50, 1275–1294 (2016). https://doi.org/10.1002/atr.1401
Sayarshad, H.R., Chow, J.Y.J.: Non-myopic relocation of idle mobility-on-demand vehicles as a dynamic location-allocation-queueing problem. Transp. Res. Part E Logist. Transp. Rev. 106, 60–77 (2017). https://doi.org/10.1016/j.tre.2017.08.003
Spieser, K., Samaranayake, S., Gruel, W., Frazzoli, E.: Shared-vehicle mobility-on-demand systems: a fleet operator’s guide to rebalancing empty vehicles. In: 95th Annual Meeting of the Transportation Research Board., Washington D.C. (2016)
Spieser, K., Treleaven, K., Zhang, R., Frazzoli, E., Pavone, M.: Toward a systematic approach to the design and evaluation of automated mobility-on-demand systems: a case study in Singapore. In: Meyer, G., Beiker, S. (eds.) Road Vehicle Automation, pp. 229–245. Springer, Berlin (2014)
Tjokroamidjojo, D., Kutanoglu, E., Taylor, G.D.: Quantifying the value of advance load information in truckload trucking. Transp. Res. Part E Logist. Transp. Rev. 42, 340–357 (2006). https://doi.org/10.1016/j.tre.2005.01.001
Tong, Y., Chen, Y., Zhou, Z., Chen, L., Wang, J., Yang, Q., Ye, J., Lv, W.: The simpler the better. Proc. 23rd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. KDD’17 (2017). https://doi.org/10.1145/3097983.3098018
Vazifeh, M.M., Santi, P., Resta, G., Strogatz, S.H., Ratti, C.: Addressing the minimum fleet problem in on-demand urban mobility. Nature (2018). https://doi.org/10.1038/s41586-018-0095-1
Weikl, S., Bogenberger, K.: A practice-ready relocation model for free-floating carsharing systems with electric vehicles - Mesoscopic approach and field trial results. Transp. Res. Part C Emerg. Technol. 57, 206–223 (2015). https://doi.org/10.1016/j.trc.2015.06.024
Yang, J., Jaillet, P., Mahmassani, H.: Real-time multivehicle truckload pickup and delivery problems. Transp. Sci. 38, 135–148 (2004)
Zhong, C., Batty, M., Manley, E., Wang, J., Wang, Z., Chen, F., Schmitt, G.: Variability in regularity: mining temporal mobility patterns in London, Singapore and Beijing using smart-card data. PLoS ONE (2016). https://doi.org/10.1371/journal.pone.0149222
Zotteri, G., Kalchschmidt, M., Caniato, F.: The impact of aggregation level on forecasting performance. Int. J. Prod. Econ. 93–94, 479–491 (2005). https://doi.org/10.1016/J.IJPE.2004.06.044
Acknowledgements
Partial funding for the first author is provided by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety through the project “City2Share”. Partial funding for this work is provided through an Eisenhower Fellowship awarded by the US Department of Transportation to the second author. Additional funding is provided by the Northwestern University Transportation Center. The authors remain responsible for all findings and opinions presented in the paper.
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Dandl, F., Hyland, M., Bogenberger, K. et al. Evaluating the impact of spatio-temporal demand forecast aggregation on the operational performance of shared autonomous mobility fleets. Transportation 46, 1975–1996 (2019). https://doi.org/10.1007/s11116-019-10007-9
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DOI: https://doi.org/10.1007/s11116-019-10007-9