Multi-scale spatiotemporal analysis of shared bike-metro interactions in Shanghai: Implications for sustainable urban mobility
发布时间：2026-02-15
点击次数：
DOI码：10.1016/j.rtbm.2026.101627
所属单位：中南大学交通运输工程学院
发表刊物：Research in Transportation Business & Management
项目来源：湖南省自科基金
关键字：Shared bike-metro interactions; Multi-scale analysis; Spatiotemporal heterogeneity;Emission mitigation; Sustainable transport management
摘要：Shared bikes and metro systems intertwine to enhance low-carbon urban mobility, yet their diverse interactions, including connectivity, substitution, and gap-filling, pose untapped potential for sustainable transport. This study pioneers a multi-scale spatiotemporal weighted regression (MSTWR) model, using Shanghai's trip data, to outperform geographically and temporally weighted regression (GTWR) by 18% with tailored bandwidths, revealing how residential zones and bicycle lanes enhance connectivity in morning peaks. High metro density unexpectedly curbs substitution, while mixed land use subtly dampens bike usage with varied transit options. Environmentally, shared bikes in Shanghai's central district reduce approximately 143.81 tons of daily CO2 emissions, with connective trips dominating at 48.32%. While usage-phase emissions offset 31.42% of the total benefits, presenting a striking operational challenge. This study advances sustainable mobility theory by uncovering scale-sensitive interaction mechanisms and proposes actionable strategies integrating infrastructure investment, land use planning, and behavioral incentives to achieve carbon neutrality goals.
合写作者：Ying Zhou, Yaxin Wang, Yanzi Xiao, Ruiting Cai
第一作者：Jianming Cai
论文类型：期刊论文
学科门类：工学
文献类型：J
卷号：66
期号：2026
页面范围：101627
是否译文：否
发表时间：2026-02-13
收录刊物：SSCI