Travel time estimation (TTE) aims to predict travel duration and provide reliable planning for residential travel schedules. Trajectories naturally contain sequential features in form of GPS points with temporal precedence, which can be leveraged to improve prediction performance. Besides, the spatial information, i.e., the graph structure of the road network, can well represent the road highly and is commonly used to capture spatial information in traffic networks. However, extracting regional spatial information from trajectory data, in addition to its latitude and longitude information, poses a significant challenge due to the inherent format in which the trajectory data is recorded. In light of this, we propose a graph-transformer for TTE (GT-TTE) to utilize a Graph Transformer to adapt effectively to trajectories’ sequential and spatial characteristics for improved TTE performance. By traversing the trajectory nodes with GT-TTE, we construct a graph structure for all trajectory points, thereby obtaining the relative spatial information of each point. Further, we obtain a region adjacency empirically more feature-rich over the sequential data. We evaluate GT-TTE on three real-world representative data sets and observe improvement by approximately 17% compared to the state-of-the-art baselines.

中文翻译：

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GT-TTE：将轨迹建模为行程时间估计图


出行时间估算（TTE）旨在预测出行持续时间并为住宅出行时间表提供可靠的规划。轨迹自然包含具有时间优先级的 GPS 点形式的顺序特征，可用于提高预测性能。此外，空间信息，即道路网络的图结构，可以很好地高度表征道路，常用于捕获交通网络中的空间信息。然而，由于记录轨迹数据的固有格式，除了纬度和经度信息之外，从轨迹数据中提取区域空间信息也构成了重大挑战。有鉴于此，我们提出了一种用于 TTE 的图变换器（GT-TTE），以利用图变换器有效地适应轨迹的顺序和空间特征，从而提高 TTE 性能。通过GT-TTE遍历轨迹节点，为所有轨迹点构建图结构，从而获得每个点的相对空间信息。此外，我们根据经验获得了比顺序数据特征更丰富的区域邻接。我们在三个现实世界代表性数据集上评估 GT-TTE，观察到与最先进的基线相比提高了约 17%。