Towards a Configurational Multimodal Urban Network Model: A Data-Driven Approach to Public Transport Modelling.

Paper in proceeding, 2024

The development of multimodal urban network models, integrating public transport and the street network, is crucial for achieving more integrated and precise spatial analysis, supporting research and practice towards understanding and creating sustainable environments. Despite their importance, building these models still faces challenges. While a reproducible, data-driven approach is widely embraced for calculating travel times and accessibility analyses with high metric and temporal precision, a gap remains in generating simplified models to advance towards a configurational approach. To address this challenge, this paper introduces a data-driven approach for developing simplified, flexible, and interoperable multimodal public transport network models capable of supporting both time-based and configurational analysis. These models are constructed by aggregating data from the General Transit Feed Specification (GTFS) at different levels of simplification to cater for different types of analysis. The integration of multimodal public transport network models with specific street network models opens the potential configurational analysis that includes all modes of transportation. By capturing crucial information about the city across various scales, these models serve as a robust toolkit to support the endeavours of researchers and practitioners working towards more sustainable cities.