Quantifying Urban Travel Resilience Under Multi-Source External Stimuli: Linking Social Perception, Green Exposure, and Low-Carbon Mobility
マルチソース外部刺激下における都市交通レジリエンスの定量化：社会的認識、グリーン露出、低炭素モビリティの連関 (AI 翻訳)
Yantong Li, Taoyu Chen, Yajie Guo, Rui Wang, Shisen Meng, Haitao Zhang
🤖 gxceed AI 要約
日本語
本研究は、突発的な外部刺激（猛暑・石油価格変動）に対する都市住民の交通行動変化を、自然言語処理（NLP）とXGBoost-SHAPを用いて分析。中国Sina Weiboデータから、猛暑時は移動削減（52.4%）と自動車依存（24.6%）が生じ、気温38-39℃が低炭素移動から高炭素移動への転換点であることを発見。石油価格上昇時は新エネルギー車志向が64.4%を占め、コスト主導の低炭素代替が促進された。
English
This study uses NLP and XGBoost-SHAP on Sina Weibo data to analyze urban travel behavior changes under extreme heat and oil price shocks. Key findings: heat leads to trip reduction (52.4%) and motorized travel (24.6%), with a transition interval at 38-39°C where high-carbon willingness surpasses low-carbon. Oil price increases drive cost-based low-carbon substitution, with new energy vehicle intentions at 64.4%.
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、猛暑日の増加やエネルギー価格変動が交通行動に与える影響を踏まえた、都市の低炭素化政策に示唆を与える。特に、熱ストレスと交通弱者の移動保障を考慮したゾーニングや、気象連動型MaaSの設計に応用可能。
In the global GX context
Globally, this framework links climate shocks (heat) and economic shocks (oil price) to mobility behavior, offering evidence for demand-side transport policy. The findings support adaptive low-carbon infrastructure planning, especially for heat-vulnerable populations, and align with TCFD/ISSB's focus on climate-related transition and physical risk impacts on transportation.
👥 読者別の含意
🔬研究者:Provides a replicable NLP-based framework to model behavioral resilience under multi-source shocks, combining social media data and machine learning for low-carbon transport research.
🏢実務担当者:Offers actionable insights for urban planners and transport operators on how extreme heat and fuel price changes shift modal choice, informing adaptive mobility services and green infrastructure investments.
🏛政策担当者:Highlights the need for differentiated policies for forced mobility groups (e.g., essential workers) and the potential of heat-related spatial interventions to promote low-carbon travel.
📄 Abstract（原文）
Demand-side management is increasingly important for low-carbon transport governance. However, many studies assume relatively stable travel preferences and pay limited attention to behavioural changes under sudden external shocks. This study proposes an Event–Behaviour–Resilience framework and applies Natural Language Processing to Sina Weibo data to examine travel responses to extreme heat and refined oil price adjustments. The results show asymmetric response patterns. Oil price increases were associated with cost-based low-carbon substitution, with new-energy vehicle intentions accounting for 64.4% of the share. In contrast, extreme heat was associated with both trip reduction and motorised travel. Travel reduction reached 52.4%, while ride-hailing or taxi responses accounted for 24.6%. A quadratic fitting analysis identified 38.0–39.0 °C as an observed transition interval, within which high-carbon motorised willingness began to exceed low-carbon slow mobility willingness. Group-level analysis showed unequal behavioural flexibility. While 80.0% of the general population reduced travel under extreme heat, the forced mobility group showed limited travel reduction and maintained a high level of low-carbon willingness at 86.87%. XGBoost-SHAP results indicated that temperature, emotional valence, and behavioural constraints contributed to low-carbon mobility intention. These findings suggest that behavioural responses can help identify spatial interventions for low-carbon transport, especially in relation to heat exposure, mobility flexibility, and access to adaptive travel options.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/land15061019first seen 2026-06-11 05:25:44
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