Study on the Spatiotemporal Characteristics of Bike‐Sharing and Urban Public Transport Integration: A Case Study of Lanzhou, China

With urbanization, public transportation resources are becoming increasingly strained. As a key complement to urban transit systems, shared bikes offer distinct advantages in solving the ‘last‐mile’ issue for urban commuters. However, one pressing challenge in integrating shared bikes with public transportation is the uneven spatiotemporal distribution. Using Lanzhou City as a case study, this paper provides a detailed analysis of the spatiotemporal characteristics of shared bike and public transport connections. Through the mining of cycling data and analysis of travel demands, a random forest regression (RFR) model is employed to identify factors influencing shared bike usage. The results reveal that variables such as age, population density, and cycling distance significantly impact the efficiency of shared bike connections. Based on these findings, several improvement strategies are proposed, including optimizing the allocation and distribution of shared bikes, addressing the specific needs of various age groups, enhancing cycling safety, and improving bike maintenance. By implementing these strategies, the integration of shared bikes with public transport can be enhanced, increasing shared bike usage and improving the overall efficiency of urban commuting, while promoting green travel and sustainable urban development.