【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

• 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.

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