Revealing the CO2 emission reduction of ridesplitting and its determinants based on real-world data

TL;DR

This study empirically analyzes real-world CO2 emission reductions from ridesplitting in Chengdu, revealing key determinants and variability in environmental benefits using observed data and machine learning models.

Contribution

It provides the first real-world measurement of ridesplitting's emission reduction and identifies key factors influencing its environmental impact using interpretable machine learning.

Findings

Average emission reduction of 43.15g/km per trip

Overlap rate and detour rate are key determinants

Increasing overlap and speed while reducing detours improves reductions

Abstract

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.