Paper Type
Complete
Abstract
This paper explores the relationship between bicycle-sharing platforms like Citi Bike and ridesharing services like Uber and Lyft. Both are innovative transportation models, but their effects on each other remain debated. Some suggest bicycle-sharing complements ridesharing by addressing last-mile barriers, expanding the customer base, and improving financial viability. Others argue it substitutes ridesharing, particularly for short trips, reducing overall ridership. We empirically examine Citi Bike’s entry in New York City and its impact on ridesharing ridership, measured by pick-ups, using causal inference techniques such as the difference-in-differences method. Our analysis investigates how this effect varies based on travel distance, pooled rides, and rush-hour demand. Our findings provide insights for ridesharing and bicycle-sharing companies to refine business strategies and for policymakers to develop informed transportation policies that reduce traffic congestion and emissions in metropolitan areas. This research contributes to the ongoing discussion of shared mobility services.
Paper Number
1146
Recommended Citation
Sengupta, Ayush and Tripathi, Sambit, "Share-the-Road: Exploring the Relationship between Bike-sharing and Ridesharing Platforms" (2025). AMCIS 2025 Proceedings. 1.
https://aisel.aisnet.org/amcis2025/sig_dspe/sig_dspe/1
Share-the-Road: Exploring the Relationship between Bike-sharing and Ridesharing Platforms
This paper explores the relationship between bicycle-sharing platforms like Citi Bike and ridesharing services like Uber and Lyft. Both are innovative transportation models, but their effects on each other remain debated. Some suggest bicycle-sharing complements ridesharing by addressing last-mile barriers, expanding the customer base, and improving financial viability. Others argue it substitutes ridesharing, particularly for short trips, reducing overall ridership. We empirically examine Citi Bike’s entry in New York City and its impact on ridesharing ridership, measured by pick-ups, using causal inference techniques such as the difference-in-differences method. Our analysis investigates how this effect varies based on travel distance, pooled rides, and rush-hour demand. Our findings provide insights for ridesharing and bicycle-sharing companies to refine business strategies and for policymakers to develop informed transportation policies that reduce traffic congestion and emissions in metropolitan areas. This research contributes to the ongoing discussion of shared mobility services.
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