An On-Demand Autonomous Modular Ridesharing Service

Abstract

Traffic congestion significantly impacts urban environments, costing billions annually and contributing notably to greenhouse gas emissions and air pollution. Ridesharing services, facilitated by widespread smartphone adoption, have emerged as a promising mobility-on-demand (MoD) solution to alleviate these issues. With the advent of Autonomous Modular Vehicle Technology (AMVT), characterized by autonomy and modularity, these vehicles (also known as pods) can dynamically connect to form a pod train (platooning) and consolidate passengers en-route, potentially enhancing energy efficiency beyond conventional ridesharing. This study introduces and formulates an AMVT-based autonomous modular ridesharing system (AMRS) that employs a shareability hypergraph approach and integer linear programming (ILP) to optimally match passenger requests and minimize energy consumption. Numerical experiments show that AMRS reduces energy consumption by up to 34% compared to non-ridesharing scenarios, with modular coordination contributing incremental yet scalable savings (up to approximately 1.4% additional reduction over basic ridesharing at high demand levels). Sensitivity analysis indicates AMRS benefits are most pronounced in denser networks and with higher numbers of requests, highlighting conditions under which modular operations effectively complement ridesharing efficiency.

Keywords

autonomous modular vehicle technology (AMVT) / autonomous modular ridesharing system (AMRS) / in-motion joining and disjoining / shareability hypergraph

References

[1] Ngo, N.S.; Zou, Z.; Yang, Y.; et al. The impact of urban form on the relationship between vehicle miles traveled and air pollution. Transp. Res. Interdiscip. Perspect. 2024, 28, 101288. https://doi.org/10.1016/j.trip.2024.101288.

[2] Texas A&M Transportation Institute. 2021 Urban Mobility Report. Available online: http://tti.tamu.edu/documents/mobility-report-2021.pdf (accessed on 10 May 2025).

[3] United States Environmental Protection Agency, Fast Facts: U.S. Transportation Sector GHG Emissions 1990-2021. Available online: https://www.epa.gov/system/files/documents/2023-06/420f23016.pdf (accessed on 10 May 2025).

[4] Caiazzo, F.; Ashok, A.; Waitz, I.A.; et al. Air pollution and early deaths in the United States. Part I: Quantifying the impact of major sectors in 2005. Atmos. Environ. 2013, 79, 198-208. https://doi.org/10.1016/j.atmosenv.2013.05.081.

[5] Morency, C. The ambivalence of ridesharing. Transportation 2007, 34, 239-253. https://doi.org/10.1007/s11116-006-9101-9.

[6] McKinsey & Company. Cracks in the Ridesharing Market—And How to Fill Them. Available online: https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/cracks-in-the-ridesharing-market-and-how-to-fill-them#0 (accessed on 10 May 2025).

[7] Furuhata, M.; Dessouky, M.; Ordóñez, F.; et al. Ridesharing: The state-of-the-art and future directions. Transp. Res. Part B Methodol. 2013, 57, 28-46. https://doi.org/10.1016/j.trb.2013.08.012.

[8] Fortune Business Insights. Ride Sharing Market Size, Share & COVID-19 Impact Analysis. Available online: https://www.fortunebusinessinsights.com/ride-sharing-market-103336 (accessed on 10 May 2025).

[9] NEXT Future Transportation—Modular Autonomous Vehicles. Available online: https://www.next-future-mobility.com/ (accessed on 10 May 2025).

[10] CNN. Dubai’s Futuristic Transport Concepts. Available online: https://www.cnn.com/2017/11/29/middleeast/gallery/dubai-future-transport/index.html (accessed on 10 May 2025).

[11] Lin, J.; Nie, Y.M.; Kawamura, K. An Autonomous Modular Mobility Paradigm. IEEE Intell. Transp. Syst. Mag. 2023, 15, 378-386. https://doi.org/10.1109/MITS.2022.3159484.

[12] Shafiee, A.; Moghaddam, H.R.; Lin, J. Using Autonomous Modular Vehicle Technology as an Alternative for Last-Mile Delivery. In Proceedings of the 2024 Forum for Innovative Sustainable Transportation Systems (FISTS), Riverside, CA, USA, 26-28 February 2024; pp. 1-6. https://doi.org/10.1109/FISTS60717.2024.10485532.

[13] Handke, V.; Jonuschat, H. Flexible Ridesharing; Springer: Berlin/Heidelberg, Germany, 2013.

[14] Shawe-Taylor, J.; Cristianini, N. Kernel Methods for Pattern Analysis; Cambridge University Press: Cambridge, UK, 2004.

[15] Toth, P.D. Vehicle Routing. Public Transp. 2014, 1, 6-7.

[16] Berbeglia, G.; Cordeau, J.F.; Laporte, G. Dynamic pickup and delivery problems. Eur. J. Oper. Res. 2010, 202, 8-15. https://doi.org/10.1016/j.ejor.2009.04.024.

[17] Mahmoudi, M.; Zhou, X. Finding optimal solutions for vehicle routing problem with pickup and delivery services with time windows: A dynamic programming approach based on state-space-time network representations. Transp. Res. Part B Methodol. 2016, 89, 19-42. https://doi.org/10.1016/j.trb.2016.03.009.

[18] Cordeau, J.F. A branch-and-cut algorithm for the dial-a-ride problem. Oper. Res. 2006, 54, 573-586. https://doi.org/10.1287/opre.1060.0283.

[19] Marín, Á.G. Airport management: Taxi planning. Ann. Oper. Res. 2006, 143, 191-202. https://doi.org/10.1007/s10479-006-7381-2.

[20] Hsieh, F.S.; Zhan, F.M.; Guo, Y.H. A solution methodology for carpooling systems based on double auctions and cooperative coevolutionary particle swarms. Appl. Intell. 2019, 49, 741-763. https://doi.org/10.1007/s10489-018-1288-x.

[21] Ma, S.; Zheng, Y.; Wolfson, O. T-share: A large-scale dynamic taxi ridesharing service. In Proceedings of the 2013 IEEE 29th International Conference on Data Engineering (ICDE), Brisbane, QLD, Australia, 8-12 April 2013; pp. 410-421. https://doi.org/10.1109/ICDE.2013.6544843.

[22] Santi, P.; Resta, G.; Szell, M.; et al. Quantifying the benefits of vehicle pooling with shareability networks. Proc. Natl. Acad. Sci. USA 2014, 111, 13290-13294. https://doi.org/10.1073/pnas.1403657111.

[23] Alonso-Mora, J.; Samaranayake, S.; Wallar, A.; et al. On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment. Proc. Natl. Acad. Sci. USA 2017, 114, 462-467. https://doi.org/10.1073/pnas.1611675114.

[24] Simonetto, A.; Monteil, J.; Gambella, C. Real-time city-scale ridesharing via linear assignment problems. Transp. Res. Part C Emerg. Technol. 2019, 101, 208-232. https://doi.org/10.1016/j.trc.2019.01.019.

[25] Tafreshian, A.; Masoud, N. Trip-based graph partitioning in dynamic ridesharing. Transp. Res. Part C Emerg. Technol. 2020, 114, 532-553. https://doi.org/10.1016/j.trc.2020.02.008.

[26] Lin, J.; Sasidharan, S.; Ma, S.; et al. A model of multimodal ridesharing and its analysis. In Proceedings of the 2016 17th IEEE International Conference on Mobile Data Management (MDM), Porto, Portugal, 13-16 June 2016; pp. 164-173. https://doi.org/10.1109/MDM.2016.34.

[27] Ma, S.; Zheng, Y.; Wolfson, O. Real-Time City-Scale Taxi Ridesharing. IEEE Trans. Knowl. Data Eng. 2015, 27, 1782-1795. https://doi.org/10.1109/TKDE.2014.2334313.

[28] Martinez, J.M.; M-Correia, L.; HA-Viegas, G. An agent-based simulation model to assess the impacts of introducing a shared-taxi system: An application to Lisbon (Portugal). J. Adv. Transp. 2015, 49, 475-495. https://doi.org/10.1002/atr.

[29] Shafiee, A.; Asgharpour, S.; Askari, S.; et al. Understanding Characteristics of Crowdshipping Trip Production: Evidence from Atlanta. In Proceedings of the International Conference on Transportation and Development 2024, Atlanta, Georgia, 15-18 June 2024; pp. 62-71. https://doi.org/10.1061/9780784485521.006.

[30] Guarino, M. Uber, Lyft Worsen Traffic in Chicago’s Loop. Available online: https://www.chicagobusiness.com/article/20180316/ISSUE01/180319945/uber-lyft-worsen-traffic-in-chicago-s-loop (accessed on 10 May 2025).

[31] Chen, P.W.; Nie, Y.M. Analysis of an idealized system of demand adaptive paired-line hybrid transit. Transp. Res. Part B Methodol. 2017, 102, 38-54. https://doi.org/10.1016/j.trb.2017.05.004.

[32] U.S. Department of Energy. eGallon Methodology. Available online: https://www.energy.gov/sites/prod/files/2013/06/f1/eGallon-methodology-final.pdf (accessed on 10 May 2025).

[33] Lammert, M.P.; Duran, A.; Diez, J.; et al. Effect of Platooning on Fuel Consumption of Class 8 Vehicles Over a Range of Speeds, Following Distances, and Mass 2014-01-2438; SAE International: Warrendale, PA, USA, 2014. https://doi.org/10.4271/2014-01-2438.