A data-driven framework for natural feature profile of public transport ridership: Insights from Suzhou and Lianyungang, China

Urban public transport systems, characterised by their complexity, generate vast data sets that pose challenges to traditional analytical methods. To address this issue, our research introduces an innovative natural feature profile framework, leveraging a comprehensive, data-driven approach that incorporates big data, data mining, machine learning, and correlation analysis. This approach provides detailed insights essential for transport planning and policy development. The framework's core is its three-layered structure: the data layer, the feature layer, and the application layer, complemented by a unique four-level feature tagging system. This system investigates correlations, significance, and sensitivities amongst feature tags. It facilitates the extraction of natural feature profiles from voluminous data sets, rendering the framework highly applicable in practical scenarios. The implementation of this framework in Suzhou and Lianyungang demonstrated its adaptability and effectiveness. The findings underscored distinct cityspecific transport patterns, highlighting the necessity for customised transport strategies. Furthermore, our framework excels at capturing spatial-temporal dynamics, offering essential insights grounded in evidence. Overall, this paper introduces a methodical, adaptable, and dataoriented framework, signalling a promising future for the development of intelligent and sustainable urban public transport systems.