Understanding and predicting pedestrian dynamics has become essential for shaping safer, more responsive, and human-centered urban environments. This study conducts a comprehensive scientometric analysis of research on data-driven pedestrian trajectory prediction and crowd simulation, mapping its intellectual evolution and interdisciplinary structure. Using bibliometric data from the Web of Science Core Collection, we employ SciExplorer and Bibliometrix to identify major trends, influential contributors, and emerging frontiers. Results reveal a strong convergence between artificial intelligence, urban informatics, and crowd behavior modeling--driven by graph neural networks, transformers, and generative models. Beyond technical advances, the field increasingly informs urban mobility design, public safety planning, and digital twin development for smart cities. However, challenges remain in ensuring interpretability, inclusivity, and cross-domain transferability. By connecting methodological trajectories with urban applications, this work highlights how data-driven approaches can enrich urban governance and pave the way for adaptive, socially responsible mobility intelligence in future cities.

翻译：理解和预测行人动态已成为塑造更安全、响应更迅速、以人为本的城市环境的关键。本研究对数据驱动的行人轨迹预测与人群模拟研究进行了全面的科学计量分析，绘制了其知识演进与跨学科结构图。利用来自Web of Science核心合集的书目计量数据，我们运用SciExplorer和Bibliometrix识别了主要趋势、有影响力的贡献者以及新兴前沿。结果表明，在图形神经网络、Transformer和生成模型的推动下，人工智能、城市信息学与人群行为建模之间呈现出强烈的融合趋势。除了技术进步，该领域正日益为城市移动性设计、公共安全规划以及智慧城市的数字孪生开发提供信息支持。然而，在确保可解释性、包容性和跨领域可迁移性方面仍存在挑战。通过将方法学轨迹与城市应用相连接，本工作强调了数据驱动方法如何能够丰富城市治理，并为未来城市中适应性、社会责任的移动智能铺平道路。