Predicting human displacements is crucial for addressing various societal challenges, including urban design, traffic congestion, epidemic management, and migration dynamics. While predictive models like deep learning and Markov models offer insights into individual mobility, they often struggle with out-of-routine behaviours. Our study introduces an approach that dynamically integrates individual and collective mobility behaviours, leveraging collective intelligence to enhance prediction accuracy. Evaluating the model on millions of privacy-preserving trajectories across three US cities, we demonstrate its superior performance in predicting out-of-routine mobility, surpassing even advanced deep learning methods. Spatial analysis highlights the model's effectiveness near urban areas with a high density of points of interest, where collective behaviours strongly influence mobility. During disruptive events like the COVID-19 pandemic, our model retains predictive capabilities, unlike individual-based models. By bridging the gap between individual and collective behaviours, our approach offers transparent and accurate predictions, crucial for addressing contemporary mobility challenges.

翻译：预测人类位移对于应对城市设计、交通拥堵、流行病管理及人口迁移动态等多项社会挑战至关重要。尽管深度学习与马尔可夫模型等预测方法能洞察个体移动模式，但它们在处理非日常行为时往往表现不佳。本研究提出了一种动态整合个体与集体流动行为的全新方法，借助集体智慧提升预测精度。我们对横跨美国三座城市的数百万条隐私保护轨迹进行了模型评估，结果显示该方法在预测非日常流动性方面显著优于包括先进深度学习方法在内的各类模型。空间分析进一步揭示了模型在兴趣点密度高的城市区域周边表现尤为出色，这些区域的集体行为对流动性具有强烈影响。在新冠肺炎疫情期间等破坏性事件中，与传统基于个体的模型不同，本模型仍能保持预测能力。通过弥合个体与集体行为之间的差距，本文提出的方法能够提供透明且精准的预测，这对应对当代流动性挑战至关重要。