不同新能源混合条件下城市交叉口的交通碳排放

Traffic Carbon Emissions at Urban Intersections under Different New Energy Blending Ratios

  • 摘要: 以城市交叉口为研究对象,选取交通运行状况与新能源车占比2个关键因素,基于微观交通仿真技术构建交通需求模型,分析其对交叉口碳排放量的影响机理。研究结果表明:交叉口碳排放量主要取决于燃油车流量及其单位碳排放强度,后者与交通运行状况直接相关。当新能源车作为燃油车替代需求时,通过降低燃油车占比可有效减少碳排放;作为新增需求时,会加剧交通拥堵,导致单位燃油车碳排放强度上升。机理研究表明:在通行顺畅状态下,碳排放量与燃油车数量呈线性关系;而在拥堵状态下,二者关系则转变为指数关系。交通运行状况每降低一个等级,单位燃油车碳排放强度增幅达15%~60%,其中从轻微拥堵向中度拥堵过渡时增幅最为显著,当达到严重拥堵状态时,增幅可达80%~90%。本研究为预测与评估城市交叉口及路网碳排放奠定了理论基础,为实施精准化交通需求管控提供科学参考。

     

    Abstract: Focusing on urban intersections, this research examines traffic operational conditions and new energy vehicle (NEV) penetration. A traffic demand model and microscopic simulation techniques were used to analyze mechanisms affecting intersection carbon emissions. Results show emissions depend on conventional vehicle flow and their per-vehicle emission rate, the latter directly linked to traffic conditions. When replacing, NEVs reduce emissions by decreasing fuel vehicle share; when added to demand, they exacerbate congestion, increasing per-vehicle emission rate. The mechanistic analysis reveals a linear emission-conventional vehicle relationship under smooth traffic, shifting to exponential under congestion (slight, moderate, or severe). Each level of traffic deterioration raises per-vehicle emission rate by 15%~60%, with the sharpest increase (80%~90%) occurring during the slight-to-moderate congestion transition and severe congestion. This study establishes a theoretical foundation for predicting intersection/network emissions and supports precision traffic management strategies.

     

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