Promoting low carbon mobility: A case study of Beijing MaaS

A carbon emissions simulation framework for assessing Mobility-as-a-Service (MaaS) schemes is proposed in this study. The MaaS platform incorporates an added recommendation of combined modes based on traditional travel recommendations. Different MaaS schemes are represented by the proportion of rides taken by ride-hailing services in the combined mode. A carbon emission prediction model integrating the IPCC (Intergovernmental Panel on Climate Change) moving source emission model with a multi-agent system has been developed. This model employs a multi-agent approach to simulate heterogeneous user mode choice behavior and uses the IPCC’s “bottom-up” method to calculate system carbon emissions, considering the impact of congestion. The model is validated through a case study using data from the Beijing MaaS platform. This study examines the effects of two MaaS recommendation schemes on system carbon emissions under varying user numbers and heterogeneity, as well as the impact of green incentive policies. The case study reveals that disregarding user heterogeneity overestimates system carbon emissions. Increasing user numbers raises total system carbon emissions but does not necessarily increase per capita emissions. MaaS schemes with a lower proportion of ride-hailing trips in combined modes more effectively encourage car users to switch to subway and ride-hailing, and help carless users adopt the subway + ride-hailing combination, thus reducing system emissions. Higher rewards in green incentive policies do not necessarily lead to lower carbon emissions. The model provides a detailed approach for carbon emission calculations, which could apply for MaaS carbon reduction assessment, MaaS scheme design, and green incentive design.