  Ridesplitting, which is a form of pooled ridesourcing service, has great
potential to alleviate the negative impacts of ridesourcing on the environment.
However, most existing studies only explored its theoretical environmental
benefits based on optimization models and simulations. By contrast, this study
aims to reveal the real-world emission reduction of ridesplitting and its
determinants based on the observed data of ridesourcing in Chengdu, China.
Integrating the trip data with the COPERT model, this study calculates the CO2
emissions of shared rides (ridesplitting) and their substituted single rides
(regular ridesourcing) to estimate the CO2 emission reduction of each
ridesplitting trip. The results show that not all ridesplitting trips reduce
emissions from ridesourcing in the real world. The CO2 emission reduction rate
of ridesplitting varies from trip to trip, averaging at 43.15g/km. Then,
interpretable machine learning models, gradient boosting machines, are applied
to explore the relationship between the CO2 emission reduction rate of
ridesplitting and its determinants. Based on the SHapley Additive exPlanations
(SHAP) method, the overlap rate and detour rate of shared rides are identified
to be the most important factors that determine the CO2 emission reduction rate
of ridesplitting. Increasing the overlap rate, the number of shared rides,
average speed, and ride distance ratio while decreasing the detour rate, actual
trip distance, and ride distance gap can increase the CO2 emission reduction
rate of ridesplitting. In addition, nonlinear effects and interactions of the
determinants are examined through the partial dependence plots. To sum up, this
study provides a scientific method for the government and ridesourcing
companies to better assess and optimize the environmental benefits of
ridesplitting.
