  Ride-hailing is rapidly changing urban and personal transportation. 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 95% and 91% of the predictive power in
determining whether a trip is requested to share and whether it is successfully
shared, respectively. Spatial and temporal attributes, sociodemographic, built
environment, and transit supply variables do not entail predictive power at the
trip level in presence of these travel impedance variables. This implies that
pricing signals are most effective to encourage riders to share their rides.
Our findings shed light on sharing behavior in ride-hailing trips and can help
devise strategies that increase shared ride-hailing, especially as the demand
recovers from pandemic.
