[Impact of Low-carbon Transport Mode Conversion on Carbon Emissions in Multiple Scenarios]

With the increasing demand for urban transportation， green travel has become one of the main ways for cities to reduce carbon emissions. By utilizing multi-source transportation data such as taxi GPS data， subway card usage data， and bike-sharing order data， we aim to reveal the carbon emission reduction potential of the transformation of urban public transportation travel patterns. Firstly， by extracting the driving mileage of different transportation modes， a "bottom-up" urban transportation carbon emission calculation model is established to fit the spatial distribution characteristics of carbon emissions from different travel modes. Combined with the implementability of carbon reduction policies， three low-carbon combined travel scenarios are proposed to evaluate the carbon reduction potential of urban public transportation in differentiated scenarios. Taking Shenzhen City as an example for verification， the results showed that taxi travel， subway travel， and shared bike travel in urban transportation had a high degree of spatial overlap， and taxi travel could be replaced by combined low-carbon travel modes. The combined travel of "shared bikes + rail transit" could reduce carbon emissions by up to 20.34%. The connection and transfer capacity of subway stations will affect the willingness to choose low-carbon modes. Under the travel alternative scenarios considering the differentiation of station transfer capabilities， combined travel could reduce carbon emissions by up to 13.57%. Increasing the deployment of shared electric bikes could reduce carbon emissions by up to 22.01%， which would be a further decrease of 11.21% compared with the substitution of shared bikes. The research results can provide references for optimizing transfer facilities and reducing carbon emissions in areas with higher demand.