Multisource data-driven diagnostic assessment of Hangzhou’s cycling system: A zonal policy framework for smart low-carbon urban mobility
マルチソースデータ駆動型の杭州自転車システム診断評価：スマート低炭素都市モビリティのためのゾーン別政策枠組み (AI 翻訳)
Xiaoyi Zhang, Yichen Ruan, Shaohua Wang, Xiaowen Ma, Qiuxiao Chen
🤖 gxceed AI 要約
日本語
本研究は、杭州市の自転車システムを対象に、NDVIやPOI、交通量データなどのマルチソースデータを統合した診断フレームワークを提案。12指標を用いて利便性・活力・安全性の3次元で評価し、都心部から郊外への勾配を明らかにした。ゾーン別の戦略（優位ゾーン・優先ゾーン・推進ゾーン・体験ゾーン）を提案し、低炭素都市モビリティ実現への示唆を与える。
English
This study proposes a multi-source data-driven diagnostic framework for assessing Hangzhou's cycling system, integrating NDVI, POI, traffic data, and more. Using 12 indicators across convenience, vitality, and safety, it reveals a center-periphery gradient and proposes zonal strategies (Dominant, Priority, Advocacy, Experience) for low-carbon urban mobility.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の自転車活用推進計画やコンパクトシティ政策と親和性が高く、特に市街地郊外でのギャップを埋めるゾーン別アプローチは、低炭素まちづくりに応用可能。SSBJやTCFD開示には直接関係しないが、自治体の統合報告書における交通分野の脱炭素施策として参考になる。
In the global GX context
This paper offers a replicable data-driven framework for urban cycling assessment that aligns with global low-carbon mobility goals. While China-specific, the zonal governance model provides insights for cities worldwide aiming to integrate cycling into sustainable transport systems, relevant to TCFD/ISSB scope 3 transport emissions reporting.
👥 読者別の含意
🔬研究者:The multi-source data integration and zone-specific indicator weighting methodology can be adapted for other cities' non-motorized transport assessments.
🏢実務担当者:Urban planners and mobility departments can use the zonal strategies to prioritize infrastructure investments and improve first- and last-mile connectivity.
🏛政策担当者:The framework supports evidence-based policy for low-carbon urban mobility, particularly in balancing development across central and peripheral zones.
📄 Abstract（原文）
Amidst China’s territorial spatial planning reforms, Hangzhou’s cycling system—a central component of non-motorized transport (NMT)—prioritizes low-carbon, safe, green, efficient, and convenient travel, aligning with the city’s goal of building a people-centered, sustainable urban mobility network. This study proposes a multi-source data-driven diagnostic framework for urban cycling assessment. The framework integrates diverse geospatial data, including the Normalized Difference Vegetation Index (NDVI) for tree-shaded pathway analysis, OpenStreetMap bicycle lanes mapping, Point of Interest (POI) data for filtering bicycle-sharing station locations, and Gaode real-time traffic volume indices. The assessment employed 12 quantified indicators spanning three dimensions: convenience, vitality, and safety, with differential weighting across four functional zones. The analysis revealed a pronounced center-periphery gradient, with higher scores in downtown areas declining toward suburbs, reflecting imbalances in infrastructure investment and service accessibility. Correlation heatmaps further identified indicators with high sensitivity to zone-specific performance variations. Tailored strategies are proposed for each zones: Class I (Dominant) Zones should maintain high service levels through integrated network optimization; Class II (Priority) Zones, where the Public Bicycle System (PBS) serves both commuting and leisure, require additional stations, shaded corridors, and seamless metro connections to boost first- and last-mile access; Class III (Advocacy) Zones must prioritize transit linkages and traffic safety; Class IV (Experience) Zones should strengthen experiential quality through placemaking and greenway connectivity. The findings demonstrate that this adaptive zonal governance framework bridges smart city goals and NMT development, offering a low-carbon model that balances infrastructure efficiency, user experience, and environmental sustainability.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1080/15568318.2026.2691402first seen 2026-07-17 05:02:41
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