This paper presents a bibliometric analysis of the field of short-term passenger flow forecasting within local public transit, covering 814 publications that span from 1984 to 2024. In addition to common bibliometric analysis tools, a variant of a citation network was developed, and topic modelling was conducted. The analysis reveals that research activity exhibited sporadic patterns prior to 2008, followed by a marked acceleration, characterised by a shift from conventional statistical and machine learning methodologies (e.g., ARIMA, SVM, and basic neural networks) to specialised deep learning architectures. Based on this insight, a connection to more general fields such as machine learning and time series modelling was established. In addition to modelling, spatial, linguistic, and modal biases were identified and findings from existing secondary literature were validated and quantified. This revealed existing gaps, such as constrained data fusion, open (multivariate) data, and underappreciated challenges related to model interpretability, cost-efficiency, and a balance between algorithmic performance and practical deployment considerations. In connection with the superordinate fields, the growth in relevance of foundation models is also noteworthy.

翻译：本文对地方公共交通短期客流预测领域进行了文献计量分析，涵盖了1984年至2024年间的814篇文献。除了常用的文献计量分析工具外，本研究还构建了一种引文网络变体并进行了主题建模。分析表明，2008年之前的研究活动呈现零星分布特征，此后则显著加速，其特点表现为从传统统计与机器学习方法（如ARIMA、SVM及基础神经网络）向专业化深度学习架构的转变。基于这一发现，本研究建立了与机器学习、时间序列建模等更广泛领域的关联。除建模方法外，研究还识别了空间、语言和模态偏差，并对现有二手文献的结论进行了验证与量化。这揭示了当前存在的空白领域，例如受限的数据融合、开放（多变量）数据，以及未被充分重视的模型可解释性、成本效益、算法性能与实际部署考量之间的平衡等挑战。在与上位领域的关联中，基础模型相关性的增长趋势同样值得关注。