Publication Type

Conference Proceeding Article

Version

submittedVersion

Publication Date

8-2022

Abstract

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

Keywords

Ride Hailing, Order Dispatching, Federated Learning

Discipline

Operations Research, Systems Engineering and Industrial Engineering | Software Engineering | Transportation

Research Areas

Software and Cyber-Physical Systems

Publication

KDD '22: Proceedings 28th ACM SIGKDD Conference On Knowledge Discovery And Data Mining, Washington, DC, August 14-18

First Page

4079

Last Page

4089

ISBN

9781450393850

Identifier

10.1145/3534678.3539047

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3534678.3539047

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