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Smart city article details
| Title | St-Difftraj: A Spatiotemporal-Aware Diffusion Model For Trajectory Generation |
|---|---|
| ID_Doc | 52831 |
| Authors | Wang B.; Yu J.; Han J.; Qiu S. |
| Year | 2024 |
| Published | ACAI 2024 - 2024 7th International Conference on Algorithms, Computing and Artificial Intelligence |
| DOI | http://dx.doi.org/10.1109/ACAI63924.2024.10899629 |
| Abstract | Synthetic traj ectory generation has significant applications in urban planning, mobility analysis, smart city development, etc. Diffusion models excel at generating high-quality synthetic data and are widely used in traj ectory data generation. However, existing diffusion-based trajectory generation models face the following limitations: (1) ignoring temporal sequence information, and (2) inefficient use of conditional guidance. To address these issues, we propose a SpatioTemporal-Aware Diffusion Model for Trajectory Generation, named ST-DiffTraj. Specifically, we design a SpatioTemporal Encoder (STEncoder) to capture correlations between independent and joint spatiotemporal feature distributions by introducing temporal information. Additionally, we develop a noise estimation network by stacking multiple CDAttn-DConv (Condition-Dependent Attention Dilation Convolutional) layers. Each CDAttn-DConv layer integrates attention mechanisms and gated activation units to enhance noise estimation with conditional guidance. Comprehensive experiments on two real-world trajectory datasets demonstrate the superior performance of our model in generating high-quality trajectory data compared to baselines. © 2024 IEEE. |
| Author Keywords | conditional guidance; diffusion model; trajectory generation |
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