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.

介绍了 GOF-TTE 移动用户群的生成式联邦学习框架，旨在利用联邦学习方法建立实时道路交通状态的模型，保护用户隐私并训练个性化的模型来预测路程的时间估计。