Rights statement: This is the author’s version of a work that was accepted for publication in Transportation Research Part B: Methodological. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Physics Reports, 130, 82-104, 2019 DOI: 10.1016/j.trb.2019.10.004
Accepted author manuscript, 2.17 MB, PDF document
Available under license: CC BY-NC-ND
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Dynamic prediction-based relocation policies in one-way station-based carsharing systems with complete journey reservations
AU - Repoux, Martin
AU - Kaspi, Mor
AU - Boyacı, Burak
AU - Geroliminis, Nikolas
N1 - This is the author’s version of a work that was accepted for publication in Transportation Research Part B: Methodological. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Physics Reports, 130, 82-104, 2019 DOI: 10.1016/j.trb.2019.10.004
PY - 2019/12/1
Y1 - 2019/12/1
N2 - In this paper, we study the operations of a one-way station-based carsharing system implementing a complete journey reservation policy. We consider the percentage of served demand as a primary performance measure and analyze the effect of several dynamic staff-based relocation policies. Specifically, we introduce a new proactive relocation policy based on Markov chain dynamics that utilizes reservation information to better predict the future states of the stations. This policy is compared to a state-of-the art staff-based relocation policy and a centralistic relocation model assuming full knowledge of the demand. Numerical results from a real-world implementation and a simulation analysis demonstrate the positive impact of dynamic relocations and highlight the improvement in performance obtained with the proposed proactive relocation policy.
AB - In this paper, we study the operations of a one-way station-based carsharing system implementing a complete journey reservation policy. We consider the percentage of served demand as a primary performance measure and analyze the effect of several dynamic staff-based relocation policies. Specifically, we introduce a new proactive relocation policy based on Markov chain dynamics that utilizes reservation information to better predict the future states of the stations. This policy is compared to a state-of-the art staff-based relocation policy and a centralistic relocation model assuming full knowledge of the demand. Numerical results from a real-world implementation and a simulation analysis demonstrate the positive impact of dynamic relocations and highlight the improvement in performance obtained with the proposed proactive relocation policy.
KW - carsharing
KW - simulation
KW - Markov chain
KW - prediction
KW - operations
U2 - 10.1016/j.trb.2019.10.004
DO - 10.1016/j.trb.2019.10.004
M3 - Journal article
VL - 130
SP - 82
EP - 104
JO - Transportation Research Part B: Methodological
JF - Transportation Research Part B: Methodological
SN - 0191-2615
ER -