Road transport has the highest carbon emissions of all transport modalities. However, the types of vehicles with high emissions on expressways have not been clarified, and spatiotemporal analyses of carbon emissions are relatively imprecise. To address these problems, high-precision data and the “bottom-up” carbon-emission method were used to construct an expressway emission estimation model. This model includes the emissions of all cars, buses, and trucks at the road-section scale. Finally, various emission-reduction scenarios were set up based on relevant policies to identify the optimal pathway for reducing expressway emissions. The findings of this study are as follows. (1) Freight trucks are the primary source of expressway emissions. (2) The ring and national expressways are the areas with the highest passenger-vehicle and freight-truck emissions, respectively. (3) The diurnal variations of expressway emissions have two peaks and two minima. (4) Single-policy simulations show that increasing the percentage of new-energy cars will lead to the most efficient emission reduction, while multi-policy simulations show that simultaneously reducing the fuel consumption of all vehicles is the optimal pathway. This study has expanded the use of traffic big data in carbon emissions analysis, and provided practical solutions to the sustainable development of expressways.

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基于OTC数据的高速公路碳排放估算及政策模拟——以广东省为例

公路运输是所有运输方式中碳排放量最高的。然而，高速公路上高排放车辆的类型尚未明确，碳排放的时空分析也相对不精确。针对这些问题，采用高精度数据和“自下而上”的碳排放方法构建了高速公路排放估算模型。该模型包括路段范围内所有轿车、公共汽车和卡车的排放量。最后，根据相关政策设定各种减排情景，找出高速公路减排的最佳路径。本研究的结果如下。 （1）货车是高速公路排放的主要来源。 (2) 环线和国家高速公路分别是客车和货车排放最高的区域。 (3)高速公路排放日变化有两个峰值和两个最小值。 （4）单政策模拟表明，提高新能源汽车占比是最有效的减排效果，而多政策模拟表明，同时降低所有车辆的燃料消耗是最优路径。该研究拓展了交通大数据在碳排放分析中的运用，为高速公路的可持续发展提供了切实可行的解决方案。