Danyue Zhi, Ying Lv, Huijun Sun, Xiaoyan Feng, Weize Song, Alejandro Tirachini, y Constantinos Antoniou
Abstract
Ride-splitting, a special kind of ride-hailing service, presents significant potential for energy savings and emission reduction. Studying factors that promote ride-splitting can help build sustainable transportation systems. Although many studies have analyzed the impact of the built environment and sociodemographic variables on ride-splitting, there is a lack of consideration of variables specific to ride-hailing systems. This study aims to analyze the complex impact of explanatory variables (including ride-hailing system-specific variables) on ride-splitting, based on an interpretable machine-learning framework. Firstly, the price ratio between shared and solo trips, the distance passengers wait for the driver to pick them up (called passenger waiting distance), and the driver’s detour index are extracted from Beijing’s data. Then, a machine learning-based framework combining XGBoost and SHAP is constructed. The explained variables are the daily trip numbers of ride-splitting and solo ride-hailing between origin–destination (OD) pairs. The results show that price ratio, passenger waiting distance, and detour index have a greater impact on ride-splitting than solo ride-hailing. Based on SHAP values, a nonlinear threshold-based relationship between individual variables and ride-splitting demand is investigated. Exogenous variables related to the high adoption of ride-splitting include OD pairs having trip durations shorter than 20 min, a zonal per capita GDP below a certain threshold, and being located away from the city center. The interaction effects of multiple variables on ride-splitting, such as distance from the origin/destination to the city center and travel time, are investigated based on the SHAP interaction value. These findings help to adapt specific variables to facilitate the shift from solo trips to shared trips, which is conducive to more sustainable transportation patterns.
