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【Applied Energy最新原创论文】基于智能卡数据的城市公共交通系统碳足迹时空分析

AEii国际应用能源  · 公众号  ·  · 2023-11-22 18:30

正文

原文信息

Spatio-temporal analysis of carbon footprints for urban public transport systems based on smart card data

原文链接:

https://doi.org/10.1016/j.apenergy.2023.121859

Highlights

Spatio-temporal analyses of carbon footprints for UPTS during the COVID-19 pandemic are conducted based on smart card data.

A multi-layer urban rail network model is proposed to estimate the carbon footprints of URT with traffic assignment model.

Projection and interpolation methods are utilized to achieve more accurate bus trajectories.

Statistical analysis shows varied patterns of carbon footprint distribution on selected days, all following a power-law distribution.

Spatial correlation relationship between buses and URT in Beijing is positive.

摘要

随着全球气候变化日益严重,减少碳排放成为一个迫切的全球议题。公共交通系统作为城市交通的重要组成部分,在碳减排中发挥着关键作用。但城市公共交通系统缺乏碳足迹分析研究。本研究基于北京的智能卡数据,对COVID-19大流行期间城市公共交通系统的碳足迹进行了时空分析。碳足迹计算的核心是估计城市轨道交通(URT)和公交车的乘客出行轨迹和客流量。我们构建了一个多层城市轨道交通网络模型,通过交通分配模型计算乘客量和出行轨迹。此外,我们利用广义相加模型(GAM)分析了公共汽车与城市轨道交通碳足迹之间的相关关系。此外,我们还对城市公共交通系统的碳足迹进行了统计分析。城市公共交通系统碳足迹的时空分析结果显示,与工作日相比,假日期间的碳排放量显著降低,高峰时段的排放量约占城市公共交通系统每日总排放量的一半,而不同地区的碳足迹分布存在显著差异。此外,我们的分析揭示了公交车的碳足迹与城市轨道交通碳足迹之间的正相关关系。统计分析反映了疫情期间不同日期的碳足迹分布模式不同,但选定日期的碳足迹分布都遵循幂律分布。本研究有助于了解新冠肺炎大流行期间城市公共交通系统对环境的影响,也为制定碳减排战略提供重要指导和参考。

更多关于“carbon footprint”的文章请见: https://www.sciencedirect.com/search?qs=carbon%20footprint&pub=Applied%20Energy&cid=2714299&qs=Battery%20energy%20storage

Abstr act

The increasing severity of global climate change has made reductions in carbon emissions an urgent global issue. The relative lack of carbon footprint analyses of urban public transportation systems (UPTS) is therefore surprising, given that UPTS is an important component of urban transportation and one that may play a crucial role in carbon emission reduction. This study conducts a spatio-temporal analysis of carbon footprints for UPTS during the COVID-19 pandemic based on smart card data in Beijing. Since the core of carbon footprint calculation is to estimate travellers’ trip trajectories and the ridership of urban rail transit (URT) and buses, we construct a novel multi-layer urban rail network model to calculate passenger volume and travellers’ trajectories through a traffic assignment model. Furthermore, we utilize the Generalized Additive Model (GAM) to analyse the correlation relationship between the carbon footprints of buses and URT. Additionally, we conduct statistical analysis of the carbon footprint of UPTS. The results of the spatio-temporal analysis of carbon footprints for UPTS show significantly lower carbon emissions during holidays compared to those on working days, and emissions during peak hours contribute approximately half of the total daily UPTS emissions, while there are notable variations in the distribution of the carbon footprint among different districts. Moreover, our analysis reveals a positive correlation between the carbon footprints of buses and URT. The statistical analysis reflects different patterns of carbon footprint distribution on different dates during the pandemic, but the carbon footprint distributions on selected dates all follow a power-law distribution. This study may facilitate the understanding to the impacts of UPTS on the environment during the COVID-19 pandemic, and also provide important guidance and reference for the development of carbon emission reduction strategies.

Keywords:

COVID-19

spatial-temporal analysis

public transport

carbon footprint

carbon neutrality

Graphics

图1 城市公共交通系统碳足迹的分析框架

图2 北京市公交和轨道交通的客运量

(a)公交

(b)轨道交通

图3 城市公共交通系统在半对数尺度上的碳足迹分布







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