- 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.