分析城市行人轨迹预测研究前沿，揭示智能城市交通的演进方向。
Mapping the Urban Mobility Intelligence Frontier: A Scientometric Analysis of Data-Driven Pedestrian Trajectory Prediction and Simulation
- 通过文献计量方法梳理人工智能与城市信息学融合趋势
- 发现图神经网络、Transformer等模型推动该领域发展
- 适合关注智慧城市、公共安全与数字孪生的研究者
理解并预测行人动态对打造更安全、响应更快、以人为本的城市环境至关重要。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，描绘其知识演化与跨学科结构。基于Web of Science核心合集的文献数据，采用SciExplorer和Bibliometrix工具识别主要趋势、关键贡献者与新兴前沿。结果表明，人工智能、城市信息学与人群行为建模之间呈现强融合——由图神经网络、Transformer及生成模型驱动。除技术进步外，该领域日益服务于城市交通设计、公共安全规划与智慧城市的数字孪生建设。然而，在可解释性、包容性与跨领域迁移能力方面仍存挑战。本文通过连接方法演进与城市应用，凸显数据驱动方法如何丰富城市治理，为未来自适应、负责任的移动智能铺平道路。
原文摘要 · Abstract (English)
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.
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