Economics Research
•Analyzed user preferences within the UberX Share feature, incorporating trip characteristics, monetary fees, and temporal/geographical patterns in a robust sample dataset of 49,997 ride observations in New York City•Utilized a comprehensive approach by employing the linear probability model and logistic regression, followed by an examination of marginal effects. This method allowed for a thorough assessment of key variables, including congestion surcharge, trip distance, duration, base fare, tolls, and various temporal and geographical factors•The presence of a congestion surcharge is associated with a significant decrease in the probability of a shared ride request across all regression models.•Average marginal effects indicate a consistent negative impact, suggesting that the imposition of a congestion surcharge reduces the likelihood of passengers opting for shared rides•Time-of-day variations show that the probability of a shared ride request significantly increases during the evening and late-night hours.•Temporal pattern holds consistently across different models, suggesting that certain times of the day are more conducive to shared rides