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STDR: A Deep Learning Method for Travel Time Estimation

机译：STDR：一种用于行程时间估计的深度学习方法

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With the booming traffic developments, estimating the travel time for a trip on road network has become an important issue, which can be used for driving navigation, traffic monitoring, route planning, and ride sharing, etc. However, it is a challenging problem mainly due to the complicate spatial-temporal dependencies, external weather conditions, road types and so on. Most traditional approaches mainly fall into the sub-segments or sub-paths categories, in other words, divide a path into a sequence of segments or sub-paths and then sum up the sub-time, yet which don't fit the real-world situations such as the continuously dynamical changing route or the waiting time at the intersections. To address these issues, in this paper, we propose an end to end Spatial Temporal Deep learning network with Road type named STDR to estimate the travel time based on historical trajectories and external factors. The model jointly leverages CNN and LSTM to capture the complex nonlinear spatial-temporal characteristics, more specifically, the convolutional layer extracts the spatial characteristics and the LSTM with attention mechanism extracts the time series characteristics. In addition, to better discover the influence of the road type, we introduce a road segmentation approach which is capable of dividing the trajectory based on the shape of trajectory. We conduct extensive verification experiments for different settings, and the results demonstrate the superiority of our method.
机译：随着交通的蓬勃发展，估计道路网络上的旅行时间已成为一个重要问题，可用于驾驶导航，交通监控，路线规划和乘车共享等。然而，这主要是一个具有挑战性的问题由于复杂的时空依赖性，外部天气条件，道路类型等。大多数传统方法主要属于子段或子路径类别，换言之，将路径划分为段或子路径的序列，然后对子时间进行汇总，但与实际时间不符。世界各地的情况，例如不断变化的路线或交叉路口的等候时间。为了解决这些问题，在本文中，我们提出了一种端到端的道路时空深度学习网络，其道路类型为STDR，以根据历史轨迹和外部因素来估计旅行时间。该模型联合利用CNN和LSTM捕获复杂的非线性时空特征，更具体地讲，卷积层提取空间特征，而LSTM具有注意力机制提取时间序列特征。另外，为了更好地发现道路类型的影响，我们引入了一种道路分割方法，该方法能够基于轨迹的形状来划分轨迹。我们针对不同的设置进行了广泛的验证实验，结果证明了我们方法的优越性。

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