首页|Map-Informed Trajectory Recovery With Adaptive Spatio-Temporal Autoencoder

Map-Informed Trajectory Recovery With Adaptive Spatio-Temporal Autoencoder

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  • NETL
  • NSTL
  • IEEE
The recovery of coarsely sampled trajectories considering the road network topology characteristics is a crucial task for many downstream applications in intelligent transportation systems. Existing approaches in this domain primarily focus on extracting spatio-temporal correlations for the observed trajectory points but neglect the critical role of road network topology characteristics in making the recovery results more accurate and realistic. In addition, too many road segments in cities undermine the model inference performance. To address these challenges, we propose a novel Map-informed Adaptive Spatio-Temporal Autoencoder, which follows an encoder-decoder architecture for trajectory recovery. Specifically, we utilize a pre-trained attributed network embedding module to incorporate the road segment characteristics into the input data to make it easier for the model to extract the spatio-temporal dependencies from coarse trajectories. Furthermore, we construct a novel adaptive mask inference module that contains a distance-based mask matrix and a learnable adaptive mask matrix to assist the model in making segment inferences by weighting each candidate segment adaptively in the recovery process. To evaluate the performance of the proposed model, we conduct a series of comprehensive case studies on two representative real-world trajectory datasets. The experimental results demonstrate that the proposed model consistently outperforms state-of-the-art approaches.

TrajectoryRoadsFeature extractionData modelsData miningAdaptation modelsTopologyCorrelationNetwork topologyDeep learning

Yongchao Ye、Ao Wang、Adnan Zeb、Shiyao Zhang、James Jianqiao Yu

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Department of Data Science, City University of Hong Kong, Hong Kong, SAR, China

Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

School of Intelligent Manufacturing and Smart Transportation, Suzhou City University, Suzhou, China

Research Institute for Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China

School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China|Department of Computer Science, University of York, York, U.K.

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2025

10.1109/TITS.2024.3483941

IEEE transactions on intelligent transportation systems

IEEE transactions on intelligent transportation systems

ISSN：
年,卷(期)：2025.26(1)
  • 52