TY - JOUR
T1 - Incentive-compatible mechanisms for online resource allocation in Mobility-as-a-Service systems
AU - Xi, Haoning
AU - Liu, Wei
AU - Waller, S. Travis
AU - Hensher, David A.
AU - Kilby, Philip
AU - Rey, David
N1 - Funding Information:
This research was partially supported by iMOVE CRC Project, Australia ( 3-020 ), the Australian Government through the Australian Research Council’s Discovery Projects funding scheme ( DP190102873 ) and Discovery Early Career Researcher Award, Australia ( DE200101793 ).
Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/4
Y1 - 2023/4
N2 - In the context of Mobility-as-a-Service (MaaS), the transportation sector has been evolving towards user-centric business models, which put the user experience and tailored mobility solutions at the center of the offer. The emerging concept of MaaS emphasizes that users value experience-relevant factors, e.g., service time, inconvenience cost, and travel delay, over segmented travel modes choices. This study proposes an auction-based mechanism and tractable optimization models for the demand-side management of MaaS systems wherein users’ trip requests are represented as mode-agnostic mobility resources. Users’ requests arrive dynamically in the MaaS system and users compete for mobility resources by bidding for mobility services based on their willingness to pay and experience-relevant preferences. We take the perspective of a MaaS platform regulator who aims to maximize social welfare by optimally allocating mobility resources to users in real-time. The MaaS regulator first decides whether to offer each user a MaaS bundle and identifies the optimal allocation of mobility resources for the selected users. Users have the possibility to accept or reject offered MaaS bundles by comparing the associated utility obtained from MaaS with a reserve utility obtained from other travel options. We introduce mixed-integer programming formulations for this online mobility resource allocation problem. We show that the proposed MaaS mechanism is incentive-compatible, individually rational, budget balanced, and computationally efficient. We propose a polynomial-time online algorithm and derive its competitive ratio relative to an offline algorithm. We also explore rolling horizon configurations with varying look-ahead policies to implement the proposed mechanism. Extensive numerical simulations conducted on large-scale instances generated from realistic mobility data highlight the benefits of the proposed mechanism.
AB - In the context of Mobility-as-a-Service (MaaS), the transportation sector has been evolving towards user-centric business models, which put the user experience and tailored mobility solutions at the center of the offer. The emerging concept of MaaS emphasizes that users value experience-relevant factors, e.g., service time, inconvenience cost, and travel delay, over segmented travel modes choices. This study proposes an auction-based mechanism and tractable optimization models for the demand-side management of MaaS systems wherein users’ trip requests are represented as mode-agnostic mobility resources. Users’ requests arrive dynamically in the MaaS system and users compete for mobility resources by bidding for mobility services based on their willingness to pay and experience-relevant preferences. We take the perspective of a MaaS platform regulator who aims to maximize social welfare by optimally allocating mobility resources to users in real-time. The MaaS regulator first decides whether to offer each user a MaaS bundle and identifies the optimal allocation of mobility resources for the selected users. Users have the possibility to accept or reject offered MaaS bundles by comparing the associated utility obtained from MaaS with a reserve utility obtained from other travel options. We introduce mixed-integer programming formulations for this online mobility resource allocation problem. We show that the proposed MaaS mechanism is incentive-compatible, individually rational, budget balanced, and computationally efficient. We propose a polynomial-time online algorithm and derive its competitive ratio relative to an offline algorithm. We also explore rolling horizon configurations with varying look-ahead policies to implement the proposed mechanism. Extensive numerical simulations conducted on large-scale instances generated from realistic mobility data highlight the benefits of the proposed mechanism.
KW - Auctions
KW - Incentive-compatibility
KW - Mobility-as-a-Service
KW - Online resource allocation
UR - http://www.scopus.com/inward/record.url?scp=85149058116&partnerID=8YFLogxK
U2 - 10.1016/j.trb.2023.02.011
DO - 10.1016/j.trb.2023.02.011
M3 - Journal article
AN - SCOPUS:85149058116
SN - 0191-2615
VL - 170
SP - 119
EP - 147
JO - Transportation Research, Series B: Methodological
JF - Transportation Research, Series B: Methodological
ER -