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

TL;DR

This study analyzes ride-hailing sharing behavior in Chicago using machine learning, revealing that trip costs and trip length influence sharing willingness, with implications for policy and pricing strategies to promote ride sharing.

Contribution

It provides empirical evidence on trip-level sharing behavior and identifies key factors affecting ride sharing, using a novel dataset and machine learning models.

Findings

Sharing willingness decreased from 27% to 12.8% over a year.

Travel impedance variables explain over 90% of sharing behavior.

Pricing signals are most effective to encourage ride sharing.

Abstract

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.