Carbon footprint portraits for multimodal trip chains in the context of Mobility as a Service

Urban transportation has become one of the fastest-growing sources of carbon emissions, driven by rapid urbanization and the widespread use of private cars. Although many studies have attempted to quantify these emissions, most remain at the aggregate level or focus on single transport modes, leaving a gap in understanding the full-chain carbon footprint of individual travel. The rise of Mobility as a Service (MaaS) offers new opportunities to close this gap by integrating multimodal travel data and enabling dynamic emission monitoring. This study develops a novel framework for constructing carbon footprint portraits of multimodal trip chains, which are multidimensional profiles that capture the carbon emission, reduction potential, and travel strcutures of individual trip chains. The framework integrates a MaaS-enabled multimodal data monitoring system, a comprehensive carbon footprint accounting model for multimodal trip chains, and a clustering approach to classify trip chains into distinct carbon footprint portraits. To validate the framework, the Geolife trajectory dataset is used as a proxy for MaaS-generated data, resulting in the reconstruction of 1865 trip chains in Beijing. Results show that car-dominated trip chains produce the highest emissions, while bus-, subway-, and active travel–dominated trip chains achieve significant reductions. Clustering analysis further identifies 8 distinct carbon footprint portraits, three of which represent low-carbon patterns. This study contributes by (1) introducing a methodology for fine-grained, individual-level profiling of full-chain carbon emissions, (2) demonstrating the feasibility of MaaS-enabled data integration for dynamic carbon monitoring, and (3) offering policy-relevant insights for designing differentiated incentives and promoting low-carbon mobility.