Multi-modal public transport modelling under traffic disruptions

This study presents a novel integrated multi-modal modelling framework for dynamic public transport (PT) networks, addressing the limitations of single-mode analysis in disruption impact assessment. Our approach extends the traditional Gravity Models-based OD estimation method by incorporating entropy-weighted calibration to fuse traffic characteristics, such as travel time, distance and fare cost, with topological features including connections, closeness and straightness. This integration enhances the accuracy of stop-by-stop OD estimation, outperforming conventional methods in terms of RMSE, MAPE, and MAE. Additionally, we introduce a dynamic Fourier transform-based method to decompose PT patronage patterns, effectively isolating significant components and reducing noise. By integrating heterogeneous data sources such as GTFS, smart card and incident log data, our framework simulates spatial-temporal impacts of disruptions on traffic states, including delay, travel time, flow and density. Furthermore, a dynamic traffic assignment model captures adaptive mode and route shift behaviour under disruptions. The proposed methodology provides a comprehensive tool for assessing network vulnerability and formulating effective disruption control strategies, ultimately enhancing the resilience of urban multi-modal transport systems.

Author dc:creator

• Zhao, Dong

Statement dc:rights

• info:eu-repo/semantics/openAccess
• The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
• © 2025 Dong Zhao
• au.edu.uts.lib/cph

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/10453/190090

OAI identifier oai:identifier

oai:opus.lib.uts.edu.au:10453/190090