Impact of lockdown policies on urban traffic conditions and carbon dioxide emissions based on an emission model on the link level in hour granularity

Abstract

This study presents an emission model at the link level, with hourly granularity, incorporating traffic conditions and driving characteristics to analyze the impact of lockdown measures on carbon dioxide (CO2) emissions within an urban road network. A case study in Beijing, China, utilizing comprehensive datasets, was conducted. More than 25 million data points from floating cars and 13 million activity data records from light-duty vehicles were collected and employed in this study. The results reveal significant changes in traffic conditions during lockdown compared to the post-lockdown period. However, driving characteristics remained consistent between the two periods. The temporal and spatial aspects of CO2 emissions are examined in detail. Furthermore, we quantified the emissions reduction resulting from "working from home," which accounted for 9.1% of the total daily CO2 emissions from light-duty vehicles post-lockdown. The model developed in this study is transferable, providing a valuable tool for researchers to analyze the spatial and temporal characteristics of urban road traffic and emissions at high resolution. These findings have practical implications for the implementation of urban carbon-reduction policies, such as working from home.