Travel time estimation (TTE) is an important research topic in many geographic applications for smart city research. However, existing approaches either ignore the impact of transportation modes, or assume the mode information is known for each training trajectory and the query input. In this paper, we propose a multi-task learning model for travel time estimation called MTLM, which recommends the appropriate transportation mode for users, and then estimates the related travel time of the path. It integrates transportation-mode recommendation task and travel time estimation task to capture the mutual influence between them for more accurate TTE results. Furthermore, it captures spatio-temporal dependencies and transportation mode effect by learning effective representations for TTE. It combines the transportation-mode recommendation loss and TTE loss for training. Extensive experiments on real datasets demonstrate the effectiveness of our proposed methods.

旅行时间估计（TTE）是智慧城市研究的许多地理应用程序中的重要研究主题。但是，现有方法要么忽略运输模式的影响，要么假定对于每个训练轨迹和查询输入来说，模式信息都是已知的。在本文中，我们提出了一种用于旅行时间估计的多任务学习模型MTLM，它为用户推荐了合适的运输方式，然后估计了路径的相关旅行时间。它集成了运输模式推荐任务和旅行时间估计任务，以捕获它们之间的相互影响，以获得更准确的TTE结果。此外，它通过学习TTE的有效表示来捕获时空依赖性和运输方式的影响。它结合了运输模式推荐损失和TTE损失进行训练。在真实数据集上的大量实验证明了我们提出的方法的有效性。