| 混行环境下的交通碳排放测算与演化规律 |
| 摘要点击 1580 全文点击 298 投稿时间：2023-12-11 修订日期：2024-02-21 |
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| 中文关键词 城市交通 碳排放 混行环境 碳排放时空分布演化 梯度提升迭代决策树模型（GBDT） |
| 英文关键词 urban traffic carbon emissions mixed environment evolution of spatio-temporal distribution of carbon emissions gradient boosting iterative decision tree model（GBDT） |
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| 中文摘要 |
| 随着电动汽车的大规模使用,在混行环境下进行交通碳排放测算并分析其演化规律,对有效实现交通碳减排具有重要意义.提出一种电动出租车与燃油出租车混行状态下的交通碳排放测算方法,使用2016年、2018年、2020年和2022年出租车GPS数据对西安市出租车碳排放水平进行测算,并通过地图匹配将测算结果匹配至栅格,之后运用K-means算法对碳排放区域进行空间聚类与碳排放时空分布演化分析,并使用梯度提升迭代决策树模型探究建成环境对碳排放的影响.结果表明,各年的周末碳排放均大于工作日,且随着电动出租车比例的增加,周末与工作日的碳排放差异逐年减小；2022年周末总体碳排放相较于2016年下降了约56%,工作日总体碳排放下降了约40%；西安市的碳排放区域经历了由2016年的全区域环状分布演变为2022年的外围低碳排放区域环状分布、中碳排放区域部分网状分布、高碳排放区域网状分布的演化过程；从建成环境因素的影响来看,居住用地与人口密度对于碳排放重要度在各年均较高,公共设施用地在工作日重要度均大于周末,休闲娱乐用地周末重要度均大于工作日.研究结果揭示了碳排放在时空上的分布演化规律,可为混行状态下的交通碳排放控制和管理提供参考. |
| 英文摘要 |
| With the extensive use of electric vehicles, it is of great significance to measure traffic carbon emissions and analyze its evolution law under the mixed traffic environment in order to effectively achieve traffic carbon reduction. Utilizing taxi GPS data from 2016, 2018, 2020, and 2022, we assessed the carbon emission levels of taxis in Xi'an and matched the results to a grid using map matching. The K-means algorithm was used to analyze the spatial clustering and spatial-temporal distribution of carbon emissions, and the gradient boosting iterative decision tree model （GBDT） was used to explore the influence of built environments on carbon emissions. The results showed that： Weekend carbon emissions were greater than weekday emissions in all years, and the difference between weekend and weekday carbon emissions decreased year by year with the increase in the proportion of electric cabs. The overall weekend carbon emissions in 2022 decreased by approximately 56%, and the overall weekday carbon emissions decreased by approximately 40% compared to those in 2016. The carbon emission region in Xi'an had experienced an evolution from a region-wide ring-shaped distribution in 2016 to 2022. The evolution process of the ring-shaped distribution of peripheral low-carbon emission regions, partial reticulation of medium-carbon emission regions, and reticulation of high-carbon emission regions. From the importance analysis of built environmental factors, it could be seen that residential land and population density had relatively high importance for carbon emissions in each year. The importance of public facilities land was higher on weekdays than that on weekends, while the importance of leisure and entertainment land was higher on weekends than that on weekdays. This work reveals the spatial and temporal distribution evolution of carbon emissions, which can provide a reference for the control and management of transportation carbon emissions under the mixed traffic state. |
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