Cross-view location alignment enhanced spatial-topological aware dual transformer for travel time estimation

Publication Type

Journal Article

Publication Date

10-2024

Abstract

Accurately estimating route travel time is crucial for intelligent transportation systems. Urban road networks and routes can be viewed from spatial and topological perspectives while existing works typically focus on one view and disregard important information from the other perspective. In this paper, we propose, a novel travel time estimation model. It incorporates an alignment-enhanced spatial-topological aware dual transformer model to adaptively incorporate intra-and inter-view features in the route, guided by cross-view location alignment matrices with clear correspondences between locations in two views. Additionally, we propose a sparsity-aware dual-view traffic feature extraction module to effectively capture temporal traffic state changes. Compared to baseline models, demonstrates improved performance on the MAPE and MAE metrics for Chengdu and Shanghai datasets, achieving improvements of 8.32%, 7.03%, 8.06% and 9.51% respectively, validating the effectiveness of in travel time estimation.

Keywords

Roads, Estimation, Transformers, Feature extraction, Predictive models, Accuracy, Long short term memory, Trajectory, Computational modeling, Adaptation models, Travel time estimation, transformer, spatial-temporal data mining, multi-view learning

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

IEEE Transactions on Intelligent Transportation Systems

Volume

25

Issue

12

First Page

20508

Last Page

20522

ISSN

1524-9050

Identifier

10.1109/TITS.2024.3463501

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://doi.org/10.1109/TITS.2024.3463501

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