The pervasive integration of GPS-enabled devices and population growth, alongside diverse transportation options, has led to an exponential increase in the collection of trajectory data, driving advancement in spatiotemporal data mining and urban planning. However, sharing trajectory data that includes sensitive user information can significantly threaten personal privacy, as location-based services like mobile apps and vehicle tracking offer valuable insights into travel routes while simultaneously raising serious concerns about the exposure of personal geolocation information. To solve this dilemna, we propose a novel generative model based on a spatiotemporal diffusion probabilistic model: Trajectory Denoising Diffusion Generative Adversarial Network (TrajDD-GAN). TrajDD-GAN reconstructs and synthesizes geographic trajectories through a forward diffusion process that adds Gaussian noise to real trajectory data, followed by a multimodal conditional GAN for iterative denoising. This process significantly reduces the number of required diffusion steps. Our experiments demonstrate the potential of integrating probabilistic diffusion techniques with generative adversarial networks to achieve better sample quality, diversity and efficiency for real-world applications. Extensive experimental results show that TrajDD-GAN generates synthetic trajectories that preserve the statistical properties of the original mobility data. One of TrajDD-GAN’s main advantages compared to other diffusion models is its cost-effective inference process: fewer steps mean faster generation. This allows for the efficient generation of large amounts of synthetic data. By optimizing the denoising steps and integrating advanced generative techniques, these models significantly reduce computational overhead while maintaining high-quality output, making them suitable for large-scale applications in trajectory generation and analysis.