Taxi-demand prediction is an important application of machine learning that enables taxi-providing facilities to optimize their operations and city planners to improve transportation infrastructure and services. However, the use of sensitive data in these systems raises concerns about privacy and security. In this paper, we propose the use of federated learning for taxi-demand prediction that allows multiple parties to train a machine learning model on their own data while keeping the data private and secure. This can enable organizations to build models on data they otherwise would not be able to access. Evaluation with real-world data collected from 16 taxi service providers in Japan over a period of six months showed that the proposed system can predict the demand level accurately within 1\% error compared to a single model trained with integrated data.

翻译：出租车需求预测是机器学习领域的一项重要应用，它能够帮助出租车服务机构优化运营，并助力城市规划者改善交通基础设施与服务。然而，这类系统所使用的敏感数据引发了隐私与安全问题。本文提出将联邦学习应用于出租车需求预测，使多方能够在各自数据上训练机器学习模型，同时保障数据的私密性与安全性。这一方法使组织机构能够在原本无法获取的数据上构建模型。基于对日本16家出租车服务机构提供的六个月真实数据进行的评估表明，与基于整合数据训练的单一模型相比，所提系统能够以低于1%的误差准确预测需求水平。