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. To put into practice,
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, the
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
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 and decreasing the detour rate, actual trip
distance, ride distance gap can increase the CO2 emission reduction rate of
ridesplitting. In addition, nonlinear effects and interactions of several key
factors are examined through the partial dependence plots. This study provides
a scientific method for the government and ridesourcing companies to better
assess and optimize the environmental benefits of ridesplitting.Comment: 33 pages, 12 figure