Fed-LTD: Towards cross-platform ride hailing via federated learning to dispatch
Learning based order dispatching has witnessed tremendous success in ride hailing. However, the success halts within individual ride hailing platforms because sharing raw order dispatching data across platforms may leak user privacy and business secrets. Such data isolation not only impairs user exp...
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sg-smu-ink.sis_research-82582023-10-10T08:40:01Z Fed-LTD: Towards cross-platform ride hailing via federated learning to dispatch WANG, Yansheng TONG, Yongxin ZHOU, Zimu REN, Ziyao XU, Yi WU, Guobin LV, Weifeng Learning based order dispatching has witnessed tremendous success in ride hailing. However, the success halts within individual ride hailing platforms because sharing raw order dispatching data across platforms may leak user privacy and business secrets. Such data isolation not only impairs user experience but also decreases the potential revenues of the platforms. In this paper, we advocate federated order dispatching for cross-platform ride hailing, where multiple platforms collaboratively make dispatching decisions without sharing their local data. Realizing this concept calls for new federated learning strategies that tackle the unique challenges on effectiveness, privacy and efficiency in the context of order dispatching. In response, we devise Federated Learning-to-Dispatch (Fed-LTD), a framework that allows effective order dispatching by sharing both dispatching models and decisions while providing privacy protection of raw data and high efficiency. We validate Fed-LTD via large-scale trace-driven experiments with Didi GAIA dataset. Extensive evaluations show that Fed-LTD outperforms single-platform order dispatching by 10.24% to 54.07% in terms of total revenue 2022-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7255 info:doi/10.1145/3534678.3539047 https://ink.library.smu.edu.sg/context/sis_research/article/8258/viewcontent/kdd22_wang.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Ride Hailing Order Dispatching Federated Learning Operations Research, Systems Engineering and Industrial Engineering Software Engineering Transportation |
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Ride Hailing Order Dispatching Federated Learning Operations Research, Systems Engineering and Industrial Engineering Software Engineering Transportation WANG, Yansheng TONG, Yongxin ZHOU, Zimu REN, Ziyao XU, Yi WU, Guobin LV, Weifeng Fed-LTD: Towards cross-platform ride hailing via federated learning to dispatch |
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Learning based order dispatching has witnessed tremendous success in ride hailing. However, the success halts within individual ride hailing platforms because sharing raw order dispatching data across platforms may leak user privacy and business secrets. Such data isolation not only impairs user experience but also decreases the potential revenues of the platforms. In this paper, we advocate federated order dispatching for cross-platform ride hailing, where multiple platforms collaboratively make dispatching decisions without sharing their local data. Realizing this concept calls for new federated learning strategies that tackle the unique challenges on effectiveness, privacy and efficiency in the context of order dispatching. In response, we devise Federated Learning-to-Dispatch (Fed-LTD), a framework that allows effective order dispatching by sharing both dispatching models and decisions while providing privacy protection of raw data and high efficiency. We validate Fed-LTD via large-scale trace-driven experiments with Didi GAIA dataset. Extensive evaluations show that Fed-LTD outperforms single-platform order dispatching by 10.24% to 54.07% in terms of total revenue |
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text |
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WANG, Yansheng TONG, Yongxin ZHOU, Zimu REN, Ziyao XU, Yi WU, Guobin LV, Weifeng |
author_facet |
WANG, Yansheng TONG, Yongxin ZHOU, Zimu REN, Ziyao XU, Yi WU, Guobin LV, Weifeng |
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WANG, Yansheng |
title |
Fed-LTD: Towards cross-platform ride hailing via federated learning to dispatch |
title_short |
Fed-LTD: Towards cross-platform ride hailing via federated learning to dispatch |
title_full |
Fed-LTD: Towards cross-platform ride hailing via federated learning to dispatch |
title_fullStr |
Fed-LTD: Towards cross-platform ride hailing via federated learning to dispatch |
title_full_unstemmed |
Fed-LTD: Towards cross-platform ride hailing via federated learning to dispatch |
title_sort |
fed-ltd: towards cross-platform ride hailing via federated learning to dispatch |
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Institutional Knowledge at Singapore Management University |
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2022 |
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https://ink.library.smu.edu.sg/sis_research/7255 https://ink.library.smu.edu.sg/context/sis_research/article/8258/viewcontent/kdd22_wang.pdf |
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