Sharing Behavior in Ride-hailing Trips: A Machine Learning Inference Approach

Abstract

Ride sharing or pooling is important to mitigate negative externalities of ride-hailing such as increased congestion and environmental impacts. However, there lacks empirical evidence on what affect trip-level sharing behavior in ride-hailing. Using a novel dataset from all ride-hailing trips in Chicago in 2019, we show that the willingness of riders to request a shared ride has monotonically decreased from 27.0% to 12.8% throughout the year, while the trip volume and mileage have remained statistically unchanged. We find that the decline in sharing preference is due to an increased per-mile costs of shared trips and shifting shorter trips to solo. Using ensemble machine learning models, we find that the travel impedance variables (trip cost, distance, and duration) collectively contribute to the predictive power by 95% in the propensity to share and 91% in successful matching of a trip. Spatial and temporal attributes, sociodemographic, built environment, and transit supply variables do not entail significant predictive power at the trip level in presence of these travel impedance variables. Our findings shed light on sharing behavior in ride-hailing trips and can help devise strategies that increase shared ride-hailing.