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<p>Public bicycle sharing (PBS) systems play a critical role in strengthening first and last mile connectivity to metro networks, yet their implementation in Indian cities remains fragmented and weakly guided by evidence. This study proposes a rigorous data driven framework to prioritize metro stations for PBS deployment in Bengaluru. A total of 66 operational metro stations were evaluated using spatial, demographic, operational and environmental indicators. Principal Component Analysis was applied to confirm indicator independence, followed by hybrid weighting using the AHP and CRITIC. Station suitability was assessed through an ensemble of five multi criteria decision making methods comprising EDAS, MARCOS, COPRAS, WASPAS and CoCoSo, supported by consensus ranking, sensitivity analysis and spatial zoning. The results indicate that six principal components explain 79.73% of the total variance, confirming dataset adequacy. Hybrid weighting identifies the metro footfall, population and intersections as the most influential criteria. Ensemble rankings the demonstrate strong inter-method agreement, with Spearman correlation values exceeding 0.88 and large effect sizes reaching 2.34 between high and low priority stations, alongside high stability across weighting configurations. The proposed framework provides a reproducible and policy ready basis for phased PBS deployment and supports data-driven and equitable urban mobility planning in rapidly growing Indian megacities.</p> Graphical abstract <p></p>

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Spatial prioritization of metro stations for public bicycle sharing integration in Bengaluru

  • 期刊论文

  • 发表日期：

    2026年6月27日

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  • T. S. Shagufta,
  • Seelam Srikanth,
  • Subhashish Dey

摘要

Public bicycle sharing (PBS) systems play a critical role in strengthening first and last mile connectivity to metro networks, yet their implementation in Indian cities remains fragmented and weakly guided by evidence. This study proposes a rigorous data driven framework to prioritize metro stations for PBS deployment in Bengaluru. A total of 66 operational metro stations were evaluated using spatial, demographic, operational and environmental indicators. Principal Component Analysis was applied to confirm indicator independence, followed by hybrid weighting using the AHP and CRITIC. Station suitability was assessed through an ensemble of five multi criteria decision making methods comprising EDAS, MARCOS, COPRAS, WASPAS and CoCoSo, supported by consensus ranking, sensitivity analysis and spatial zoning. The results indicate that six principal components explain 79.73% of the total variance, confirming dataset adequacy. Hybrid weighting identifies the metro footfall, population and intersections as the most influential criteria. Ensemble rankings the demonstrate strong inter-method agreement, with Spearman correlation values exceeding 0.88 and large effect sizes reaching 2.34 between high and low priority stations, alongside high stability across weighting configurations. The proposed framework provides a reproducible and policy ready basis for phased PBS deployment and supports data-driven and equitable urban mobility planning in rapidly growing Indian megacities.

Graphical abstract