Optimization of the Transport Structure Driven by Urban Rail Transit Under Low-Carbon Target
低炭素目標下での都市鉄道交通による交通構造の最適化 (AI 翻訳)
Han Sun, Keping Li, Yuanxi Xu, Yan Liang
🤖 gxceed AI 要約
日本語
本論文は、低炭素交通の実現を目指し、都市鉄道交通を軸とした多目的最適化モデルを構築。CO2排出量と旅行コストの最小化、旅行品質と公共交通利用率の最大化を図る。改良型NSGA-IIで解き、北京市の事例で有効性を検証。都市旅客交通の構造転換を示す。
English
This paper constructs a multi-objective optimization model driven by urban rail transit to achieve low-carbon transport, minimizing CO2 emissions and travel costs while maximizing travel quality and public transport utilization. Using an improved NSGA-II, the model is tested with Beijing case study, demonstrating a structural transformation towards sustainable urban passenger transport.
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
中国北京市の事例だが、日本の都市交通政策（特に鉄道中心の都市計画）にも示唆を与える。低炭素目標と交通需要管理の統合的アプローチは、日本のGX施策（グリーン交通等）の参考になる。
In the global GX context
While focused on Beijing, this optimization framework is relevant for global urban transport decarbonization. It demonstrates how rail transit can be a lever for structural change, aligning with global low-carbon transport goals (e.g., IPCC, C40).
👥 読者別の含意
🔬研究者:Provides a multi-objective optimization method for urban transport structure that can be adapted to other cities.
🏢実務担当者:City planners and transport authorities can use this model to evaluate rail transit expansion plans under carbon reduction targets.
🏛政策担当者:Highlights the role of urban rail transit in achieving transport emission reductions, useful for national and local climate action plans.
📄 Abstract（原文）
A reasonable urban transport structure is necessary to develop low-carbon transport and establish a cleaner and more efficient urban transport system. Urban rail transit plays a significant role in the development of low-carbon transport due to its advantages of efficiency, punctuality, safety, and environmental protection. In this paper, we construct a multi-objective model driven by urban rail transit to optimize the urban passenger transport structure from a systemic perspective. The objective functions of this model include minimizing transport CO2 emissions and travel costs while maximizing travel quality and the utilization rate of public transport operation lines. The non-dominated sorting genetic algorithm II (NSGA–II) is a classic multi-objective optimization algorithm used to optimize conflicting objectives simultaneously. In this paper, the multi-objective optimization model is solved using an improved NSGA–II, extending the local search mechanism into the NSGA–II. To evaluate the validity of the model, this paper takes Beijing, China, as the case area. Based on the development plans of urban rail transit we analyze from a specific year and multiple years. The results illustrate a structural transformation in urban passenger transport and embody a sustainable urban passenger transport structure driven by urban rail transit. This paper proposes a valid method, providing guidance for optimizing the urban transport structure.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/app16104769first seen 2026-05-17 05:45:48
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