梳理公共交通客流预测研究脉络，揭示方法演进与关键短板。
Review of Passenger Flow Modelling Approaches Based on a Bibliometric Analysis
- 基于814篇文献的引文网络与主题建模分析
- 2008年后深度学习取代传统统计模型成主流
- 指出数据融合、模型可解释性等实际部署瓶颈
本文对1984至2024年间本地公共交通短时客流预测领域的814篇文献进行了文献计量分析。除常规文献计量工具外，还构建了改进版引用网络并开展主题建模。结果表明，2008年前研究活动呈间歇性，之后显著加速，研究范式从传统的统计与机器学习方法（如ARIMA、SVM、基础神经网络）转向专用深度学习架构。基于此，建立了与机器学习和时间序列建模等更广泛领域间的关联。此外，识别出空间、语言和模式偏差，并验证和量化了现有二次文献的发现。研究揭示了若干关键缺口：数据融合受限、开放（多变量）数据不足，以及对模型可解释性、成本效益和算法性能与实际部署平衡等挑战重视不足。同时，基础模型在该领域相关性的增长也尤为显著。
原文摘要 · Abstract (English)
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.
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