Assessing the potential for traffic carbon emission reductions through residential travel mode shifts: insights from massive vehicle trajectory data and scenario simulations

Abstract

The choice of transportation mode by residents significantly affects road traffic carbon emissions. Recent studies have explored the carbon reduction effects of green travel behaviors, such as bicycle-sharing and metro travel. However, there remains a gap in research estimating the carbon reduction potential associated with the transition from motorized transport to low-carbon alternatives. In this study, we propose a carbon emission reduction scenario simulation framework based on vehicle trajectory big data. This framework is designed to evaluate the impact of shifts in residential travel modes on carbon emissions at a fine spatial and temporal scale. Our analyses indicate that only 7.2 % of car trips are suitable for a shift to active transportation options, while over 64 % of trips qualify as multimodal, particularly involving e-bikes in combination with metro, which can result in annual carbon reductions of up to 3,138 tons. This highlights the importance of multimodal transport in reducing transportation-related carbon emissions. Regarding the spatial pattern, peripheral areas present substantial carbon reduction potential, accounting for nearly 50 % of the total. Moreover, significant carbon reduction potential exists in road sections connecting central and peripheral areas. In terms of timing, we observe two peaks in emission reductions on weekdays, occurring between 7–9 AM and 4–6 PM, with an additional peak on weekends around 9 PM. Ultimately, our research highlights that multimodal transportation, especially the combination of walking or conventional cycling with metro, may offer greater carbon reduction efficiency than relying solely on active transportation options. The findings of this study can significantly inform urban transportation policy-making and guide residents toward sustainable travel choices.