Detours in Shared Rides

References

• Afèche P, Liu Z, Maglaras C (2023) Ride-hailing networks with strategic drivers: The impact of platform control capabilities on performance. Manufacturing Service Oper. Management 25(5):1890–1908.Google Scholar
• Alonso-Mora J, Samaranayake S, Wallar A, Frazzoli E, Rus D (2017) On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment. Proc. Natl. Acad. Sci. USA 114(3):462–467.Crossref, Google Scholar
• Aouad A, Saritaç Ö (2022) Dynamic stochastic matching under limited time. Oper. Res. 70(4):2349–2383.Google Scholar
• Ashlagi I, Burq M, Dutta C, Jaillet P, Saberi A, Sholley C (2022) Edge weighted online windowed matching. Math. Oper. Res. 48(2):999–1016.Google Scholar
• Berbeglia G, Cordeau J-F, Laporte G (2010) Dynamic pickup and delivery problems. Eur. J. Oper. Res. 202(1):8–15.Crossref, Google Scholar
• Bertsimas D, Jaillet P, Martin S (2019) Online vehicle routing: The edge of optimization in large-scale applications. Oper. Res. 67(1):143–162.Link, Google Scholar
• Besbes O, Castro F, Lobel I (2021) Surge pricing and its spatial supply response. Management Sci. 67(3):1350–1367.Link, Google Scholar
• Besbes O, Castro F, Lobel I (2022) Spatial capacity planning. Oper. Res. 70(2):1271–1291.Link, Google Scholar
• Bimpikis K, Candogan O, Saban D (2019) Spatial pricing in ride-sharing networks. Oper. Res. 67(3):744–769.Link, Google Scholar
• Biswas A, Gopalakrishnan R, Tulabandhula T, Metrewar A, Mukherjee K, Thangaraj RS (2018) Impact of detour-aware policies on maximizing profit in ridesharing. Proc. Internat. Workshop Agents Traffic Transportation.Google Scholar
• Boeing G (2017) Osmnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks. Comput. Environ. Urban Systems 65:126–139.Crossref, Google Scholar
• Cachon GP, Daniels KM, Lobel R (2017) The role of surge pricing on a service platform with self-scheduling capacity. Manufacturing Service Oper. Management 19(3):368–384.Link, Google Scholar
• Castillo JC, Knoepfle D, Weyl G (2017) Surge pricing solves the wild goose chase. Proc. ACM Conf. Econom. Comput. (ACM, New York), 241–242.Google Scholar
• Daganzo SF, Ouyang Y, Yang H (2020) Analysis of ride-sharing with service time and detour guarantees. Transportation Res. Part B Methodological 140:130–150.Crossref, Google Scholar
• Garg N, Nazerzadeh H (2022) Driver surge pricing. Management Sci. 68(5):3219–3235.Google Scholar
• Gopalakrishnan R, Mukherjee K, Tulabandhula T (2016) The costs and benefits of sharing: Sequential individual rationality and sequential fairness. Working paper, Queen’s University, Kingston, ON.Google Scholar
• Griswold A (2018) The future of Lyft is more carpooling. Quartz (June 7), https://qz.com/1299055/lyft-wants-half-its-rides-to-be-shared-by-2020.Google Scholar
• Gurvich I, Lariviere M, Moreno A (2019) Operations in the on-demand economy: Staffing services with self-scheduling capacity. Hu M, ed. Sharing Economy, Springer Series in Supply Chain Management, vol. 6 (Springer, Cham, Switzerland), 249–278.Crossref, Google Scholar
• Kann V (1991) Maximum bounded 3-dimensional matching is MAX SNP-complete. Inform. Processing Lett. 37(1):27–35.Crossref, Google Scholar
• Lo J, Morseman S (2018) The perfect UberPOOL: A case study on trade-offs. Ethnographic Praxis Indust. Conf. Proc. (Wiley, Hoboken, NJ), 195–223.Google Scholar
• Lu A, Frazier PI, Kislev O (2018) Surge pricing moves Uber’s driver-partners. Proc. ACM Conf. Econom. Comput (ACM, New York), 3.Google Scholar
• Lyft (2018) Lyft’s new app creates positive change for passengers and cities. Lyft Blog (June 7), https://blog.lyft.com/posts/new-app.Google Scholar
• Ma H, Fang F, Parkes DC (2021) Spatio-temporal pricing for ridesharing platforms. Oper. Res. 70(2):1025–1041.Google Scholar
• Middleton S, Schroeckenthaler K, Papayannoulis V, Gopalakrishna D (2021) Analysis of travel choices and scenarios for sharing rides. Technical report, Federal Highway Administration, U.S. Department of Transportation, Washington, DC.Google Scholar
• OpenStreetMap Contributors (2020) Planet dump retrieved from Planet OSM. Accessed September 24, 2020, https://www.openstreetmap.org.Google Scholar
• Ozkan E, Ward AR (2020) Dynamic matching for real-time ridesharing. Stochastic Systems 10(1):29–70.Link, Google Scholar
• Santi P, Resta G, Szell M, Sobolevsky S, Strogatz SH, Ratti C (2014) Quantifying the benefits of vehicle pooling with shareability networks. Proc. Natl. Acad. Sci. USA 111(37):13290–13294.Crossref, Google Scholar
• Taylor TA (2018) On-demand service platforms. Manufacturing Service Oper. Management 20(4):704–720.Link, Google Scholar
• Wang H, Yang H (2019) Ridesourcing systems: A framework and review. Transportation Res. Part B Methodological 129:122–155.Crossref, Google Scholar
• Wong K-I, Bell MGH (2006) The optimal dispatching of taxis under congestion: A rolling horizon approach. J. Advanced Transportation 40(2):203–220.Crossref, Google Scholar
• Yan C, Zhu H, Korolko N, Woodard D (2019) Dynamic pricing and matching in ride-hailing platforms. Naval Res. Logist. 67(8):705–724.Google Scholar