Unveiling the evolution of urban rail transit network: considering ridership attributes

ABSTRACT

Previous research on urban rail transit (URT) evolution mainly focused on network topology, neglecting ridership attributes. This study extracts ridership and network topology indicators from Chinese URT data. Employing a self-organizing mapping neural network model, it divides China’s URT development into four stages. The initial stage and the development stage form the framework of URT network. The network diameter reaches the maximum in the networked operation stage. In the mature stage, URT network densification occurs alongside a significant increase in resident ridership. It is also found that each network indicator has a significant nonlinear relationship with ridership attributes. These findings are of guiding significance for urban planners to accurately understanding URT’s future development and rational network planning and construction.

KEYWORDS:

• Urban rail transit
• network evolution
• ridership attributes
• complex network theory
• network topology index
• self-organizing mapping neural networks

Acknowledgments

This research was funded by the Scientific Research Foundation for Advanced Talents of Nanjing Forestry University [grant numbers 163106041]; and the Project of Philosophy and Social Science Research in Jiangsu University [grant numbers 2020SJA0125]; the Postgraduate Research and Practice Innovation Program of Jiangsu Province [grant numbers KYCX23_1155].

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.