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- W3181028038 abstract "• Reservations give precise information that could be used to better manage supply. • Transient analysis of stochasticity in ridesourcing dynamics and the impact of reservations. • Analysis of an admission control policy for maintaining reach-time service levels. • Min cost flow reactive driver dispatching and rebalancing. • For a given quality of service, the number of idle drivers decreases with increasing reservations. Ridesourcing platforms recently introduced the “schedule a ride” service where passengers may reserve (book-ahead) a ride in advance of their trip. Reservations give platforms precise information that describes the start time and location of anticipated future trips; in turn, platforms can use this information to adjust the availability and spatial distribution of the driver supply. In this article, we propose a framework for modeling/analyzing reservations in time-varying stochastic ridesourcing systems. We consider that the driver supply is distributed over a network of geographic regions and that book-ahead rides have reach time priority over non-reserved rides. First, we propose a state-dependent admission control policy that assigns drivers to passengers; this policy ensures that the reach time service requirement would be attained for book-ahead rides. Second, given the admission control policy and reservations information in each region, we predict the “target” number of drivers that is required (in the future) to probabilistically guarantee the reach time service requirement for stochastic non-reserved rides. Third, we propose a reactive dispatching/rebalancing mechanism that determines the adjustments to the driver supply that are needed to maintain the targets across regions. For a specific reach time quality of service, simulation results using data from Lyft rides in Manhattan exhibit how the number of idle drivers decreases with the fraction of book-ahead rides. We also observe that the non-stationary demand (ride request) rate varies significantly across time; this rapid variation further illustrates that time-dependent models are needed for operational analysis of ridesourcing systems." @default.
- W3181028038 created "2021-07-19" @default.
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- W3181028038 date "2021-09-01" @default.
- W3181028038 modified "2023-10-12" @default.
- W3181028038 title "Book-ahead & supply management for ridesourcing platforms" @default.
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- W3181028038 doi "https://doi.org/10.1016/j.trc.2021.103266" @default.
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