Characterization of segmented urban transportation travel behavior and countermeasures under carbon inclusive incentive policies

In the context of global climate change and the "dual carbon" target, this study focuses on the regulatory mechanism of carbon inclusive incentive policies on urban transportation behavior. Based on 1126 valid questionnaires and 51.6 million public transportation IC card data in Xiong' an New Area, an innovative Multi Discrete-Continuous Extreme Value with Count (MDCNTEV) model was used to systematically analyze the hierarchical differentiation, family constraints, and spatial dependence characteristics of travel behavior under the interaction of multimodal factors. The impact mechanism of carbon credit incentive policies on travel mode selection was quantitatively analyzed. Research has found that under the incentive of carbon inclusive policies, the conversion effect of high-income groups' travel modes is significant, but the rebound of carbon emissions caused by high-frequency travel offsets some of the emission reduction benefits. Built environment factors significantly regulate travel decisions through spatial interaction effects, and the elderly group shows a policy response lag period of 4-6 months due to their own characteristics. The research focuses on examining the behavioral characteristics of different travel entities, proposes three specific research questions and corresponding hypotheses and exploring the impact path of carbon inclusive policies based on travel characteristics, aiming to systematically address the structural contradictions in existing carbon-inclusive mechanisms and provide scientific basis for policy optimization.