Chang, Yu WeiYu WeiChangTSUNG-NAN LIN2023-11-082023-11-082023-01-019798350333336https://scholars.lib.ntu.edu.tw/handle/123456789/636989Mobility-as-a-Service (MaaS) is a novel concept integrating multimodal transportation and various service providers (SP) to provide a seamless and efficient transportation experience. However, current MaaS solutions face several challenges, including a mismatch between users and SPs, a lack of cooperation between SPs, users' privacy, and the SPs' business secrets. To address these deficiencies, we present a Federated Dynamic Match-Making Algorithm (FDMMA), which provides multimodal transportation itinerary options with constraint satisfaction, federated multitask learning, and dynamic match-making. Considering SPs' different business goals in addition to users' preferences, FDMMA can encourage SPs to cooperate and compete (co-opetition) in MaaS. Experimental results on a real-world dataset show that the effectiveness of FDMMA can improve the business goals of SPs while maintaining a comparable performance of matching users' selection to other related works.Dynamic Match-Making | Federated Multitask Learning | Mobility-as-a-service | Multimodal transportationconference paper10.1109/MetaCom57706.2023.001082-s2.0-85174566291https://api.elsevier.com/content/abstract/scopus_id/85174566291