共有オンデマンド交通の炭素排出削減ポテンシャルと都市環境の時空間的関連
Spatiotemporal associations between the built environment and carbon emission reduction potential of shared on-demand transit (原題)
Xiaofan Wei, Zhe Zhang, Chao Li, Meigen Xue, Hongdi He
🤖 gxceed AI 要約
日本語
共有オンデマンド交通（ODT）による最大炭素削減量を整数線形計画と乗客最適化で推計し、XGBoost・SHAPで都市環境特性との関係を分析した研究。ODTは朝17.67%、夜14.48%の削減が可能で、雇用・教育関連土地利用や郊外駅周辺で削減ポテンシャルが高いことを示す。低炭素モビリティ政策の空間的ターゲティングに有用な知見を提供する。
English
This study estimates the maximum carbon reduction achievable through shared on-demand transit (ODT) using integer linear programming and multi-passenger optimization, then applies XGBoost with SHAP to link reductions to built-environment characteristics. ODT could cut emissions by up to 17.67% (morning) and 14.48% (evening), with higher potential near educational facilities, employment centers, and suburban metro stations. Findings support spatially targeted low-carbon mobility planning.
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
日本では地域公共交通の縮小と運転手不足が深刻で、ODTは地方都市の交通脱炭素と生活交通維持を両立する手段として注目される。都市環境特性と削減ポテンシャルの空間分析は、自治体の地域交通計画やグリーンスローモビリティ政策の立案に示唆を与える。
In the global GX context
As cities worldwide pursue transport decarbonization under net-zero targets, this work offers an empirical method to spatially target shared mobility deployment for maximum emission cuts. It complements global low-carbon transport planning and can inform municipal climate action plans and mobility-related disclosure of urban emissions.
👥 読者別の含意
🔬研究者:ODTの炭素削減ポテンシャルをMLで空間分析する手法と、都市環境特性の非線形・空間異質性の扱いを学べる。
🏢実務担当者:MaaS・交通事業者がODT導入エリアの優先順位を炭素削減効果の観点から検討する材料になる。
🏛政策担当者:自治体が低炭素交通政策やODT実証の対象地域を、都市環境特性に基づき設計する際の根拠となる。
📄 Abstract（原文）
Shared on-demand transit has emerged as a promising mobility solution for reducing transport-related carbon emissions and supporting more sustainable urban environments. However, limited evidence exists on how built environment characteristics are associated with its carbon emission reduction potential across space and time. This study aimed to quantify the maximum carbon emission reduction achievable through ODT and identify the built environment factors associated with its spatial variation. An integer linear programming model combined with a multi-passenger travel optimization framework was developed to estimate achievable carbon emission reductions using travel demand data. An XGBoost model was then used to examine their relationships with built environment characteristics, while SHAP and partial dependence analyses were employed to interpret nonlinear, threshold, and spatially heterogeneous relationships. The results showed that ODT could reduce carbon emissions by up to 17.67% in the morning and 14.48% in the evening. Built environment characteristics exhibited pronounced spatiotemporal heterogeneity, with employment- and education-related land uses showing higher nonlinear thresholds in the evening than in the morning. Local interpretation further revealed substantial spatial variation in feature contributions, with areas surrounding educational facilities, employment centers, and suburban metro stations associated with greater carbon emission reduction potential. These findings provide spatial evidence for targeted ODT implementation and highlight the potential of built environment characteristics to inform low-carbon mobility strategies and sustainable urban transport planning.
🔗 Provenance — このレコードを発見したソース
- primary_source https://doi.org/10.3389/fpubh.2026.1927997first seen 2026-10-24 17:24:06
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