Travel time estimation is a critical task, useful to many urban applications at the individual citizen and the stakeholder level. This paper presents a novel hybrid algorithm for travel time estimation that leverages historical and sparse real-time trajectory data. Given a path and a departure time we estimate the travel time taking into account the historical information, the real-time trajectory data and the correlations among different road segments. We detect similar road segments using historical trajectories, and use a latent representation to model the similarities. Our experimental evaluation demonstrates the effectiveness of our approach.

翻译：旅行时间估算是一项关键任务,对公民个人和利益攸关方层面的许多城市应用非常有用。本文件介绍了一种新型的旅行时间估算混合算法,它利用历史和稀少的实时轨迹数据。根据一条路径和一段离开时间,我们估计旅行时间时会考虑到历史信息、实时轨迹数据以及不同路段之间的相互关系。我们利用历史轨迹探测类似的路段,并使用潜在代表来模拟相似之处。我们的实验性评估显示了我们的方法的有效性。