Assessing spatiotemporal bikeability using multi-source geospatial big data: A case study of Xiamen, China - Dai, Shaoqing; Zhao, Wufan; Wang, Yanwen; Huang, Xiao; Chen, Zhidong; Lei, Jinghan; Stein, Alfred; Jia, Peng (International journal of applied earth observation and geoinformation, 2023)
This study focuses on the development of a new framework for evaluating bikeability in urban environments with the aim of enhancing sustainable urban transportation planning. To close the research gap that previous studies have disregarded the dynamic environmental factors and trajectory data, we propose a framework that comprises four sub-indices: safety, comfort, accessibility, and vitality. Utilizing open-source data, advanced deep neural networks, and GIS spatial analysis, the framework eliminates subjective evaluations and is more efficient and comprehensive than prior methods. The experimental results on Xiamen, China, demonstrate the effectiveness of the framework in identifying areas for improvement and enhancing cycling mobility. The proposed framework provides a structured approach for evaluating bikeability in different geographical contexts, making reproducing bikeability indices easier and more comprehensive to policymakers, transportation planners, and environmental decision-makers. •A novel spatiotemproal framework is proposed for quantifying the bikeability.•Multi-source geospatial big data fusion is used for the evaluation of bikeability.•Field validation ensures the effectiveness of our bikeability results.•This framework could provide policy implications for government.