Non-myopic relocation of idle mobility-on-demand vehicles as a dynamic location-allocation-queueing problem

Hamid R. Sayarshad; Joseph Y.J. Chow

doi:10.1016/j.tre.2017.08.003

Non-myopic relocation of idle mobility-on-demand vehicles as a dynamic location-allocation-queueing problem

Research output: Contribution to journal › Article › peer-review

Abstract

Operation of on-demand services like taxis, dynamic ridesharing services, or vehicle sharing depends significantly on the positioning of idle vehicles to anticipate future demand and operational states. A new queueing-based formulation is proposed for the problem of relocating idle vehicles in an on-demand mobility service. The approach serves as a decision support tool for future studies in urban transport informatics and design of new types of urban mobility systems like carsharing, ridesharing, and smart taxis. A Lagrangian Decomposition heuristic is developed and compared with a relaxed lower bound solution. Using New York taxicab data, the proposed algorithm reduces the cost by up to 27% compared to the myopic case.

Original language	English (US)
Pages (from-to)	60-77
Number of pages	18
Journal	Transportation Research Part E: Logistics and Transportation Review
Volume	106
DOIs	https://doi.org/10.1016/j.tre.2017.08.003
State	Published - Oct 2017

Keywords

Lagrangian decomposition
Preposition of idle vehicles
Relocation costs
p-median problem

ASJC Scopus subject areas

Business and International Management
Civil and Structural Engineering
Transportation

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10.1016/j.tre.2017.08.003

Cite this

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abstract = "Operation of on-demand services like taxis, dynamic ridesharing services, or vehicle sharing depends significantly on the positioning of idle vehicles to anticipate future demand and operational states. A new queueing-based formulation is proposed for the problem of relocating idle vehicles in an on-demand mobility service. The approach serves as a decision support tool for future studies in urban transport informatics and design of new types of urban mobility systems like carsharing, ridesharing, and smart taxis. A Lagrangian Decomposition heuristic is developed and compared with a relaxed lower bound solution. Using New York taxicab data, the proposed algorithm reduces the cost by up to 27% compared to the myopic case.",

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AB - Operation of on-demand services like taxis, dynamic ridesharing services, or vehicle sharing depends significantly on the positioning of idle vehicles to anticipate future demand and operational states. A new queueing-based formulation is proposed for the problem of relocating idle vehicles in an on-demand mobility service. The approach serves as a decision support tool for future studies in urban transport informatics and design of new types of urban mobility systems like carsharing, ridesharing, and smart taxis. A Lagrangian Decomposition heuristic is developed and compared with a relaxed lower bound solution. Using New York taxicab data, the proposed algorithm reduces the cost by up to 27% compared to the myopic case.

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Non-myopic relocation of idle mobility-on-demand vehicles as a dynamic location-allocation-queueing problem

Abstract

Keywords

ASJC Scopus subject areas

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