Name
Title | ||
|---|---|---|
Interpreting Trajectories from Multiple Views: A Hierarchical Self-Attention Network for Estimating the Time of Arrival |
Abstract | ||
|---|---|---|
Estimating the time of arrival is a crucial task in intelligent transportation systems. Although considerable efforts have been made to solve this problem, most of them decompose a trajectory into several segments and then compute the travel time by integrating the attributes from all segments. The segment view, though being able to depict the local traffic conditions straightforwardly, is insufficient to embody the intrinsic structure of trajectories on the road network. To overcome the limitation, this study proposes multi-view trajectory representation that comprehensively interprets a trajectory from the segment-, link-, and intersection-views. To fulfill the purpose, we design a hierarchical self-attention network (HierETA) that accurately models the local traffic conditions and the underlying trajectory structure. Specifically, a segment encoder is developed to capture the spatio-temporal dependencies at a fine granularity, within which an adaptive self-attention module is designed to boost performance. Further, a joint link-intersection encoder is developed to characterize the natural trajectory structure consisting of alternatively arranged links and intersections. Afterward, a hierarchy-aware attention decoder is designed to realize a tradeoff between the multi-view spatio-temporal features. The hierarchical encoders and the attentive decoder are simultaneously learned to achieve an overall optimality. Experiments on two large-scale practical datasets show the superiority of HierETA over the state-of-the-arts. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3534678.3539051 | KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
21 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zebin Chen | 1 | 0 | 0.68 |
| Xiaolin Xiao | 2 | 36 | 6.57 |
| Yue-Jiao Gong | 3 | 63 | 5.86 |
| Jun Fang | 4 | 1039 | 94.15 |
| Nan Ma | 5 | 0 | 0.34 |
| Hua Chai | 6 | 0 | 0.34 |
| Zhiguang Cao | 7 | 0 | 0.34 |