Analysis of Bike Sharing | Data and Perhaps Theory?

Bike sharing is a great topic, it’s green and has lots of data. A relevant study:

Understanding intra-urban human mobility through an exploratory spatiotemporal analysis of bike-sharing trajectories

Wenwen Li et al.

(shortened abstract) This paper presents a data-driven framework analyzing bike-sharing trips in Shanghai, the world’s largest bike-share city. It integrates multiple data sources—transport networks, road characteristics, and urban land use—to study short-trip mobility patterns. Findings highlight usage trends, trip distribution, and route choice factors, offering insights for city planning, bike-sharing operations, and sustainable transportation development.

Bike-Sharing Market & Consumer Choices

Bike rentals typically work in two ways: monthly passes or pay-per-ride. The three dominant companies are MeiTuan Bike, HelloBike (Alipay), and DiDi Bike.

How do consumers choose? Key factors include bike availability, quality, and pricing. However, bike-sharing platforms exhibit complex network effects—not always straightforward. For example, as MeiTuan’s demand grows, more bikes are deployed, improving availability and convenience. But with more users, maintenance costs rise, potentially degrading service quality. This creates a double-edged network effect—expansion enhances accessibility but can also reduce user experience.

Let’s simplify, say, companies set pricing strategies while considering:

• User switching friction (how easy it is to switch to a competitor)
• Network effects (how demand influences service quality)

The market equilibrium—shaped by pricing, competition, and user behavior—makes bike-sharing a fascinating case study in urban mobility economics, theoretically.