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The aim is to develop a method for generating vehicle trajectories without complete single-vehicle annotations, utilizing surveillance data.

Zero-shot trajectory generation with self-supervised diffusion model

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The aim is to develop a method for generating vehicle trajectories without complete single-vehicle annotations, utilizing surveillance data.
Developed a zero-shot trajectory generation method based on a diffusion model.
Employed aggregated traffic flow statistics and road network topology for training without needing complete single-vehicle trajectory data.
Validated the model on real-world traffic datasets from Dadukou District, Chongqing.
Generated synthetic trajectories closely aligning with real trajectories.
Displayed comparable effectiveness in trajectory similarity and distributional similarity.
Achieved effective trajectory generation without relying on complete single-vehicle trajectories.

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Randomized trial demonstrates effective trajectory generation using surveillance data, suggesting new transportation insights.

A 2026 study studied this question.

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