Review of Passenger Flow Modelling Approaches Based on a Bibliometric Analysis

2025-11-12

AI Summary

Short-term public transit passenger flow forecasting faces persistent challenges—including weak multimodal data fusion, underutilization of open heterogeneous data, poor model interpretability, and high deployment costs—despite rapid methodological evolution. Method: This study conducts a systematic bibliometric analysis of 814 publications (1984–2024), integrating improved citation network modeling with BERTopic-based thematic analysis to quantitatively trace disciplinary evolution. Results: Research activity surged post-2008, shifting from classical statistical models (e.g., ARIMA) toward deep learning paradigms—particularly graph neural networks and spatiotemporal Transformers. Crucially, we propose a dynamic weighted citation network to uncover structural biases in knowledge diffusion and empirically demonstrate how foundation models are reshaping methodological priorities. These findings provide a theoretically grounded, technically actionable framework for developing trustworthy, deployable intelligent passenger flow forecasting systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

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.

Problem

• Analyzing passenger flow forecasting research trends from 1984 to 2024
• Identifying shifts from statistical methods to deep learning approaches
• Revealing research gaps in data fusion and practical model deployment

Innovation

• Developed citation network variant analysis
• Applied topic modeling to bibliometric data
• Identified deep learning shift in forecasting