Estimating the travel time of a path is an essential topic for the intelligent transportation system. 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 closely relates to their privacy and, thus, is less likely to be shared. The not 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 (TTE) 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 TTE, which 1) 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 and 2) 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. We also employ a simple privacy attack to our framework and implement the differential privacy mechanism to guarantee privacy safety further. Finally, we conduct experiments on two real-world public taxi data sets of DiDi Chengdu and Xi’an. The experimental results demonstrate the effectiveness of our proposed framework.

中文翻译：

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GOF-TTE：用于旅行时间估计的生成式在线联合学习框架

估计一条路径的行程时间是智能交通系统的一个重要课题。它是现实世界应用的基础，例如交通监控、路线规划和出租车调度。然而，为这种数据驱动的任务建立模型需要大量用户的旅行信息，这些信息与他们的隐私密切相关，因此不太可能被共享。如果我们直接应用联邦学习，那么跨数据所有者的非独立和同分布 (Non-IID) 轨迹数据也会使预测模型变得非常难以个性化。最后，以前关于旅行时间估计 (TTE) 的工作没有考虑道路的实时交通状态，我们认为这会对预测产生重大影响。为应对上述挑战，我们为移动用户组引入了 GOF-TTE，TTE 的生成在线联邦学习框架，它 1) 利用联邦学习方法，允许在训练时将私有数据保存在客户端设备上，并将全局模型设计为在线生成模型由所有客户端共享以推断实时道路交通状态和 2) 除了在服务器上共享基础模型外，为每个客户端调整微调的个性化模型以研究他们的个人驾驶习惯，弥补所做的残留错误通过局部全局模型预测。我们还对我们的框架进行了简单的隐私攻击，并实施了差分隐私机制以进一步保证隐私安全。最后，我们对滴滴成都和西安两个真实世界的公共出租车数据集进行了实验。