Supplier Menus for Dynamic Matching in Peer-to-Peer Transportation Platforms

Published Online:https://doi.org/10.1287/trsc.2022.1133

References

  • Agatz N, Erera A, Savelsbergh M, Wang X (2012) Optimization for dynamic ride-sharing: A review. Eur. J. Oper. Res. 223(2):295–303.CrossrefGoogle Scholar
  • Archetti C, Savelsbergh M, Speranza MG (2016) The vehicle routing problem with occasional drivers. Eur. J. Oper. Res. 254(2):472–480.CrossrefGoogle Scholar
  • Arslan AM, Agatz N, Kroon L, Zuidwijk R (2018) Crowdsourced delivery—A dynamic pickup and delivery problem with ad hoc drivers. Transportation Sci. 53(1):222–235.LinkGoogle Scholar
  • Ashkrof P, Correia GHdA, Cats O, van Arem B (2021) Ride acceptance behaviour of ride-sourcing drivers. Preprint, submitted July 16, https://arxiv.org/abs/2107.07864.Google Scholar
  • Ashkrof P, de Almeida Correia GH, Cats O, van Arem B (2020) Understanding ride-sourcing drivers’ behaviour and preferences: Insights from focus groups analysis. Res. Transportation Bus. Management 37:100516.CrossrefGoogle Scholar
  • Atasoy B, Ikeda T, Song X, Ben-Akiva ME (2015) The concept and impact analysis of a flexible mobility on demand system. Transportation Res. Part C Emerging Tech. 56:373–392.CrossrefGoogle Scholar
  • Bent RW, Van Hentenryck P (2004) Scenario-based planning for partially dynamic vehicle routing with stochastic customers. Oper. Res. 52(6):977–987.LinkGoogle Scholar
  • Cleophas C, Cottrill C, Ehmke JF, Tierney K (2019) Collaborative urban transportation: Recent advances in theory and practice. Eur. J. Oper. Res. 273(3):801–816.CrossrefGoogle Scholar
  • Dayarian I, Savelsbergh M (2020) Crowdshipping and same-day delivery: Employing in-store customers to deliver online orders. Production Oper. Management 29(9):2153–2174.CrossrefGoogle Scholar
  • Di Febbraro A, Gattorna E, Sacco N (2013) Optimization of dynamic ridesharing systems. Transp. Res. Record 2359(1):44–50.CrossrefGoogle Scholar
  • Ermagun A, Stathopoulos A (2018) To bid or not to bid: An empirical study of the supply determinants of crowd-shipping. Transportation Res. Part A Policy Practice 116:468–483.CrossrefGoogle Scholar
  • Furuhata M, Dessouky M, Ordóñez F, Brunet ME, Wang X, Koenig S (2013) Ridesharing: The state-of-the-art and future directions. Transportation Res. Part B Methodological 57:28–46.CrossrefGoogle Scholar
  • Horner H, Pazour JA, Mitchell JE (2021) Optimizing driver menus under stochastic selection behavior for ridesharing and crowdsourced delivery. Transportation Res. Part E Logist. Transportation Rev. 153:102419.CrossrefGoogle Scholar
  • Jacob J, Roet-Green R (2021) Ride solo or pool: Designing price-service menus for a ride-sharing platform. Eur. J. Oper. Res. 295(3):1008–1024.CrossrefGoogle Scholar
  • Kafle N, Zou B, Lin J (2017) Design and modeling of a crowdsource-enabled system for urban parcel relay and delivery. Transportation Res. Part B Methodological 99:62–82.CrossrefGoogle Scholar
  • Kullman ND, Cousineau M, Goodson JC, Mendoza JE (2021) Dynamic ride-hailing with electric vehicles. Transportation Sci., ePub ahead of print July 29, https://doi.org/10.1287/trsc.2021.1042. LinkGoogle Scholar
  • Lancsar E, Fiebig DG, Hole AR (2017) Discrete choice experiments: A guide to model specification, estimation and software. PharmacoEconom. 35:697–716.CrossrefGoogle Scholar
  • Le TV, Stathopoulos A, Van Woensel T, Ukkusuri SV (2019) Supply, demand, operations, and management of crowd-shipping services: A review and empirical evidence. Transportation Res. Part C Emerging Tech. 103:83–103.CrossrefGoogle Scholar
  • Lee A, Savelsbergh M (2015) Dynamic ridesharing: Is there a role for dedicated drivers? Transportation Res. Part B Methodological 81:483–497.CrossrefGoogle Scholar
  • Lei C, Jiang Z, Ouyang Y (2020) Path-based dynamic pricing for vehicle allocation in ridesharing systems with fully compliant drivers. Transportation Res. Part B Methodological 132:60–75.CrossrefGoogle Scholar
  • Li B, Krushinsky D, Reijers HA, Van Woensel T (2014) The share-a-ride problem: People and parcels sharing taxis. Eur. J. Oper. Res. 238(1):31–40.CrossrefGoogle Scholar
  • Liu Y, Li Y (2017) Pricing scheme design of ridesharing program in morning commute problem. Transportation Res. Part C Emerging Tech. 79:156–177.CrossrefGoogle Scholar
  • Liu Y, Bansal P, Daziano R, Samaranayake S (2019) A framework to integrate mode choice in the design of mobility-on-demand systems. Transportation Res. Part C Emerging Tech. 105:648–665.CrossrefGoogle Scholar
  • Mancini S, Gansterer M (2022) Bundle generation for last-mile delivery with occasional drivers. Omega 108:102582.CrossrefGoogle Scholar
  • Mofidi SS, Pazour JA (2019) When is it beneficial to provide freelance suppliers with choice? A hierarchical approach for peer-to-peer logistics platforms. Transportation Res. Part B Methodological 126:1–23.CrossrefGoogle Scholar
  • Mourad A, Puchinger J, Chu C (2019) A survey of models and algorithms for optimizing shared mobility. Transportation Res. Part B Methodological 123:323–346.CrossrefGoogle Scholar
  • Nourinejad M, Ramezani M (2020) Ride-sourcing modeling and pricing in non-equilibrium two-sided markets. Transportation Res. Part B Methodological 132:340–357.CrossrefGoogle Scholar
  • Nourinejad M, Roorda MJ (2016) Agent based model for dynamic ridesharing. Transportation Res. Part C Emerging Tech. 64:117–132.CrossrefGoogle Scholar
  • Özkan E, Ward AR (2020) Dynamic matching for real-time ride sharing. Stochastic Systems 10(1):29–70.LinkGoogle Scholar
  • Pelzer D, Xiao J, Zehe D, Lees MH, Knoll AC, Aydt H (2015) A partition-based match making algorithm for dynamic ridesharing. IEEE Trans. Intelligent Transportation Systems 16(5):2587–2598.CrossrefGoogle Scholar
  • Powell WB, Towns MT, Marar A (2000) On the value of optimal myopic solutions for dynamic routing and scheduling problems in the presence of user noncompliance. Transportation Sci. 34(1):67–85.LinkGoogle Scholar
  • Punel A, Stathopoulos A (2017) Modeling the acceptability of crowdsourced goods deliveries: Role of context and experience effects. Transportation Res. Part E Logist. Transportation Rev. 105:18–38.CrossrefGoogle Scholar
  • Qin Z, Tang X, Jiao Y, Zhang F, Xu Z, Zhu H, Ye J (2020) Ride-hailing order dispatching at DiDi via reinforcement learning. INFORMS J. Appl. Analytics 50(5):272–286.LinkGoogle Scholar
  • Rai HB, Verlinde S, Merckx J, Macharis C (2017) Crowd logistics: An opportunity for more sustainable urban freight transport? Eur. Transportation Res. Rev. 9(3):39.CrossrefGoogle Scholar
  • Reyes D, Erera A, Savelsbergh M, Sahasrabudhe S, O’Neil R (2018) The meal delivery routing problem. Preprint, submitted April 10, http://www.optimization-online.org/DB_HTML/2018/04/6571.html.Google Scholar
  • Sánchez D, Martínez S, Domingo-Ferrer J (2016) Co-utile P2P ridesharing via decentralization and reputation management. Transportation Res. Part C Emerging Tech. 73:147–166.CrossrefGoogle Scholar
  • Song Y, Ulmer MW, Thomas BW, Wallace SW (2020) Building trust in home services-stochastic team-orienteering with consistency constraints. Transportation Sci. 54(3):823–838.LinkGoogle Scholar
  • Soto Setzke D, Pflügler C, Schreieck M, Fröhlich S, Wiesche M, Krcmar H (2017) Matching drivers and transportation requests in crowdsourced delivery systems. Proc. 23rd Americas Conf. Inform. Systems (Curran Associates, Red Hook, NY), 1–10.Google Scholar
  • Tafreshian A, Masoud N, Yin Y (2020) Frontiers in service science: Ride matching for peer-to-peer ride sharing: A review and future directions. Service Sci. 12(2–3):44–60.LinkGoogle Scholar
  • Uber (2021) How does Uber match riders with drivers? Accessed April 27, https://www.uber.com/us/en/marketplace/matching/.Google Scholar
  • Ulmer MW, Goodson JC, Mattfeld DC, Thomas BW (2020) On modeling stochastic dynamic vehicle routing problems. Eur. J. Transportation Logist. 9(2):100008.CrossrefGoogle Scholar
  • U.S. Bureau of Public Roads (1964) Traffic Assignment Manual for Application with a Large, High Speed Computer (U.S. Department of Commerce, Washington, DC).Google Scholar
  • Visser T, Agatz N, Spliet R (2019) Simultaneous customer interaction in online booking systems for attended home delivery. Technical Report ERS-2019-011-LIS, Erasmus Research Institute of Management, Rotterdam, Netherlands.Google Scholar
  • Wang H, Yang H (2019) Ridesourcing systems: A framework and review. Transportation Res. Part B Methodological 129:122–155.CrossrefGoogle Scholar
  • Wang X, Agatz N, Erera A (2018) Stable matching for dynamic ride-sharing systems. Transportation Sci. 52(4):850–867.LinkGoogle Scholar
  • Yu X, Shen S (2019) An integrated decomposition and approximate dynamic programming approach for on-demand ride pooling. IEEE Trans. Intelligent Transportation Systems 21(9):3811–3820.CrossrefGoogle Scholar
  • Zhang H, Zhao J (2018) Mobility sharing as a preference matching problem. IEEE Trans. Intelligent Transportation Systems 20(7):2584–2592.CrossrefGoogle Scholar
  • Zhao B, Xu P, Shi Y, Tong Y, Zhou Z, Zeng Y (2019) Preference-aware task assignment in on-demand taxi dispatching: An online stable matching approach. Proc. AAAI Conf. Artificial Intelligence (AAAI, Menlo Park, CA), 2245–2252.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.