Your browser doesn't support javascript.
Sharing behavior in ride-hailing trips: A machine learning inference approach
Transportation Research: Part D ; 103:N.PAG-N.PAG, 2022.
Article in English | Academic Search Complete | ID: covidwho-1683635
ABSTRACT
[Display omitted] • Predict sharing behavior in Chicago's ride-haling trips using ensemble ML methods. • Willingness to share a ride declined over 52% throughout 2019. • Over time, per-mile cost of shared trips increased, shorter trips shifted to solo. • Travel impedance variables have the highest predictive power in sharing behavior. 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. [ FROM AUTHOR] Copyright of Transportation Research Part D is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
Keywords

Full text: Available Collection: Databases of international organizations Database: Academic Search Complete Language: English Journal: Transportation Research: Part D Year: 2022 Document Type: Article

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: Databases of international organizations Database: Academic Search Complete Language: English Journal: Transportation Research: Part D Year: 2022 Document Type: Article