Enhancing sustainable urban mobility: a multi-agent analysis of low-carbon policy impacts on travel behavior in Fuzhou’s main urban area, China

The escalating carbon emissions from urban transportation pose significant challenges to environmental sustainability and public health. This study leverages the theory of random utility maximization and employs a nested logit model (NL) within an agent-based modeling (ABM) framework to investigate the impact of low-carbon transportation policies on travel mode choices among urban residents. Focusing on Fuzhou’s main urban area as a case study, the research integrates residents’ demographic attributes, travel preferences, and the influence of policy interventions to simulate and analyze the dynamics of travel behavior under various low-carbon policy scenarios. The study’s simulation experiments encompass traditional license plate restrictions, community-based social network interventions, and composite carbon trading policies. The results, benchmarked against real-world survey data, validate the agent-based model’s feasibility and adaptability, offering novel insights into policy effectiveness and providing decision support for policymakers. This research contributes to the discourse on sustainable urban development by elucidating the transformative potential of low-carbon policies and their capacity to reshape travel mode choices in the pursuit of environmental goals.