Estimating the travel time of a path is an essential topic for intelligent transportation systems. It serves as the foundation for real-world applications, such as traffic monitoring, route planning, and taxi dispatching. However, building a model for such a data-driven task requires a large amount of users' travel information, which directly relates to their privacy and thus is less likely to be shared. The non-Independent and Identically Distributed (non-IID) trajectory data across data owners also make a predictive model extremely challenging to be personalized if we directly apply federated learning. Finally, previous work on travel time estimation does not consider the real-time traffic state of roads, which we argue can significantly influence the prediction. To address the above challenges, we introduce GOF-TTE for the mobile user group, Generative Online Federated Learning Framework for Travel Time Estimation, which I) utilizes the federated learning approach, allowing private data to be kept on client devices while training, and designs the global model as an online generative model shared by all clients to infer the real-time road traffic state. II) apart from sharing a base model at the server, adapts a fine-tuned personalized model for every client to study their personal driving habits, making up for the residual error made by localized global model prediction. % III) designs the global model as an online generative model shared by all clients to infer the real-time road traffic state. We also employ a simple privacy attack to our framework and implement the differential privacy mechanism to further guarantee privacy safety. Finally, we conduct experiments on two real-world public taxi datasets of DiDi Chengdu and Xi'an. The experimental results demonstrate the effectiveness of our proposed framework.
翻译:估算一条路径的旅行时间是智能运输系统的一个基本主题。它作为现实应用的基础,例如交通监测、路线规划和出租车调度。然而,建立这样一个数据驱动任务的模式需要大量的用户旅行信息,这与其隐私直接相关,因此不太可能共享。数据所有人之间非独立和同义分布的(非IID)轨迹数据也使一个预测模型变得极具挑战性,如果我们直接应用离子化学习。最后,以往的旅行时间估算工作并不考虑道路的实时交通状况,我们认为这可能大大影响预测。为了应对上述挑战,我们为移动用户群体引入GOF-TTE,即在线在线旅行时间估计框架,这与其隐私直接相关,因此不太可能被共享。非独立和同义分布(非IID)数据在数据所有数据所有数据都能够保存在客户模式上,同时允许进一步将私人数据保存在客户模式上,并将全球模型设计为在线离子化州安全模型。我们所有客户共享的离子化框架,将实时道路交通模式推高。此外,我们称,道路的实时交通状况会显著影响预测状态。除了在每次分享客户基础模型外,我们还引入了个人服务器模型,还进行了个人成本模型。