道路ネットワークにおける時空間的炭素排出特性と持続可能な削減戦略：対象路線制御と車両電動化のシミュレーション

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Validates emission reduction potential of EVs and road segment control using real trajectory data and LCA, providing a replicable methodology for urban carbon accounting.

🏢実務担当者:Urban planners and transportation authorities can use the ranking-based optimization approach to design effective carbon reduction measures targeting specific road segments or vehicle electrification.

🏛政策担当者:Supports the design of integrated policies that combine EV adoption incentives with traffic management to maximize carbon reductions, offering simulation evidence for policy targets.

全球气候变化对城市可持续发展构成严峻挑战。因此，建设低碳交通系统是提升特大城市道路网络可持续性的核心战略。结合城市道路交通网络的特征，本文基于GPS轨迹数据（5699辆车，北京，2019年9月）和COPERT排放模型，建立了一种车辆出行分割与碳排放估算方法，分析了车辆排放的时空分布特征。通过纳入电动汽车的生命周期评估（LCA）排放，本研究从路段和车辆电动化两个维度，提出了基于随机选择与排序优化的碳减排策略。采用仿真方法评估了四种车辆电动化结构（金字塔型、倒金字塔型、橄榄型和哑铃型）下不同策略的有效性及路网碳排放情况。结果表明，高峰时段交通拥堵导致碳排放强度显著上升。在LCA框架下，纯电动汽车（BEVs）和插电式混合动力电动汽车（PHEVs）的排放显著低于传统内燃机汽车（ICEVs）。在特定情景假设下，排序优化方案估计产生的碳减排量分别约为随机选择方案的2倍（路段控制）和3倍（电动化）。研究得出结论，将电动汽车推广政策与精准碳减排控制策略相结合，可以有效降低城市路网碳排放。

AI 翻訳（deepseek-v4-flash）。 正確を期す場合は下の原文を参照してください。

Global climate change poses a critical challenge to sustainable urban development. The construction of low-carbon transportation systems is therefore a core strategy for enhancing the sustainability of mega-city road networks. Combining the characteristics of urban road traffic networks, this paper establishes a method for vehicle trip segmentation and carbon emission estimation based on GPS trajectory data (5699 vehicles, Beijing, September 2019) and the COPERT emission model, analyzing the spatiotemporal distribution characteristics of vehicle emissions. By incorporating the Life Cycle Assessment (LCA) emissions of electric vehicles, this study proposes carbon reduction strategies based on stochastic selection and ranking-based optimization from two dimensions: road-segment and vehicle electrification. Simulation methods are employed to evaluate the effectiveness of different strategies, as well as road network carbon emissions, under four vehicle electrification structures: Pyramid, Inverted Pyramid, Olive, and Dumbbell. Results indicate that carbon emission intensity rises significantly due to traffic congestion during peak hours. Under the LCA framework, Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs) show significantly lower emissions than traditional Internal Combustion Engine Vehicles (ICEVs). Under the specified scenario assumptions, the ranking-based optimization scheme is estimated to yield carbon reductions approximately 2 times (segment control) and 3 times (electrification) those of the stochastic selection scheme, respectively. The study concludes that integrating EV promotion policies with precise carbon reduction control strategies can effectively mitigate urban road network carbon emissions.

• openalex https://doi.org/10.3390/su18136773first seen 2026-07-05 05:14:22