Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
8-2022
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.
Keywords
Estimating the time of arrival, Self-attention network, Hierarchical representation learning
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing | Operations Research, Systems Engineering and Industrial Engineering
Research Areas
Intelligent Systems and Optimization
Publication
KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, August 14-18
First Page
2771
Last Page
2779
ISBN
9781450393850
Identifier
10.1145/3534678.3539051
Publisher
ACM
City or Country
New York
Citation
CHEN, Zebin; XIAO, Xiaolin; GONG, Yue-Jiao; FANG, Jun; MA, Nan; CHAI, Hua; and CAO, Zhiguang.
Interpreting trajectories from multiple views: A hierarchical self-attention network for estimating the time of arrival. (2022). KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, August 14-18. 2771-2779.
Available at: https://ink.library.smu.edu.sg/sis_research/8136
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3534678.3539051
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons, Operations Research, Systems Engineering and Industrial Engineering Commons