作者
Yunjie Huang, Xiaozhuang Song, Shiyao Zhang, Lei Li, and James Jianqiao Yu*

发表
IEEE Internet of Things Journal, in press, 2024

摘要
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 Travel Time Estimation (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 datasets and observe improvement by approximately 17% compared to the state-of-the-art baselines.