With global urbanization, the focus on sustainable cities has largely grown, driving research into equity, resilience, and urban planning, which often relies on mobility data. The rise of web-based apps and mobile devices has provided valuable user data for mobility-related research. However, real-world mobility data is costly and raises privacy concerns. To protect privacy while retaining key features of real-world movement, the demand for synthetic data has steadily increased. Recent advances in diffusion models have shown great potential for mobility trajectory generation due to their ability to model randomness and uncertainty. However, existing approaches often directly apply identically distributed (i.i.d.) noise sampling from image generation techniques, which fail to account for the spatiotemporal correlations and social interactions that shape urban mobility patterns. In this paper, we propose CoDiffMob, a diffusion method for urban mobility generation with collaborative noise priors, we emphasize the critical role of noise in diffusion models for generating mobility data. By leveraging both individual movement characteristics and population-wide dynamics, we construct novel collaborative noise priors that provide richer and more informative guidance throughout the generation process. Extensive experiments demonstrate the superiority of our method, with generated data accurately capturing both individual preferences and collective patterns, achieving an improvement of over 32\%. Furthermore, it can effectively replace web-derived mobility data to better support downstream applications, while safeguarding user privacy and fostering a more secure and ethical web. This highlights its tremendous potential for applications in sustainable city-related research.

翻译：随着全球城市化进程的推进，对可持续城市的关注度大幅提升，推动了关于公平性、韧性和城市规划的研究，这些研究通常依赖于移动性数据。基于网络的应用和移动设备的兴起，为移动性相关研究提供了宝贵的用户数据。然而，现实世界的移动性数据获取成本高昂，并引发隐私担忧。为了在保护隐私的同时保留现实世界移动的关键特征，对合成数据的需求稳步增长。扩散模型的最新进展因其建模随机性和不确定性的能力，在移动轨迹生成方面显示出巨大潜力。然而，现有方法通常直接沿用图像生成技术中的独立同分布噪声采样，未能充分考虑塑造城市移动模式的时空相关性及社会交互作用。本文提出CoDiffMob，一种具有协作噪声先验的城市移动性生成扩散方法，我们强调噪声在扩散模型生成移动性数据中的关键作用。通过利用个体移动特征和群体动态，我们构建了新颖的协作噪声先验，在整个生成过程中提供更丰富、信息量更大的指导。大量实验证明了我们方法的优越性，生成的数据能准确捕捉个体偏好和集体模式，实现了超过32%的性能提升。此外，该方法能有效替代网络衍生的移动性数据，更好地支持下游应用，同时保护用户隐私并促进更安全、更合乎伦理的网络环境。这凸显了其在可持续城市相关研究应用中的巨大潜力。