The Profitability Puzzle of On-Demand Carpooling: Pricing, Matching, and Network Effects
With advancements in technology and Internet networks, carpooling services will inevitably become more automated and intelligent. Based on research into different pricing models and matching systems, Prof. Xing Hu shared strategies on how online car-hailing platforms can enhance the profitability of carpooling.
Challenges of Sustainable Travel
Against the backdrop of global resource scarcity and worsening environmental problems, ride-sharing has emerged as a crucial driver in promoting sustainable development. As an innovative way of shared transportation, online carpooling services optimise resource allocation and reduce carbon monoxide emissions. They also effectively lower public travel costs. However, this seemingly ideal model faces profitability challenges in the real world.
Taking Uber and Lyft as examples, both platforms suspended carpooling services during the COVID-19 pandemic. Although later reinstated, their operational outcomes diverged significantly. While Uber still offers shared rides, Lyft discontinued them only one year and ten months after its resumption (Note 1). This gives a glimpse into the many challenges online car-hailing service providers face on the technological and market front.
The Shared Ride Profitability Predicament
Before the pandemic, it was already difficult for Uber and Lyft to achieve financial sustainability for their carpooling services (Note 2). To encourage passengers to accept inconveniences such as detours and longer waits, both companies provided fixed discounts when they began to roll out shared rides. When large numbers of carpooling requests failed to match, however, the companies became single-ride orders. As drivers were still paid based on the trip’s distance and travel time, unsuccessful carpooling requests significantly increased the platforms’ operating costs.
Overcoming the Profitability Predicament
To address the profitability challenges, car-hailing platforms can explore the following three ways of offering carpooling services, which will be detailed below. Our research has found that the network effect (the ride-sharing success rate within a specific timeframe) has a decisive impact on the profitability of carpooling services and are decisive factors in determining the best strategy. The stronger the network effects, the easier it will be for service providers to match passengers with similar destinations and travel times, thereby lifting the likelihood of shared rides. This approach optimises resources and is effective in reducing operating costs.
- Optimising Pricing Models
Pricing models are most important for the profitability of carpooling services. While a ‘fixed price’ model provides price stability, it could also lead to losses for the online platform. The ‘contingent pricing’ model is different, as prices are adjusted depending on whether passengers have successfully secured a carpool.
Our study found:
- When network effects are strong, the platform can lock in price expectations by setting a fixed price and yield profits from a relatively higher carpool matching rate. In certain regions or time periods, reducing prices appropriately can even strengthen network effects.
- When network effects are weak, contingent pricing provides an edge as prices can be dynamically adjusted. This releases cost pressures from failed matches. It also explains why Uber has transitioned to a contingent pricing model post-pandemic.
- Optimise Matching Mechanisms
China’s largest ride-hailing platform DiDi attempted to reduce operating costs by increasing the success rate of ridesharing through a pre-matching mechanism (dispatching vehicles only when carpool requests have been successfully matched) (Note 3). However, when compared to the traditional rapid carpooling mechanism (dispatching vehicles even if carpool matching fails), the pre-matching mechanism shows no clear advantage. This is because the pre-matching may well lose passengers who have not been allocated a shared ride. Moreover, this method also requires passengers to spend extra time while waiting for the carpooling result. When this happens, the platform must lower carpooling prices to attract passengers, further squeezing profit margins.
- Prioritised-Carpooling Strategy
Platforms can adopt a strategy that prioritises shared rides. By increasing fares to narrow the profit difference between solo rides and shared rides, this approach can tempt passengers to choose carpooling, which strengthens network effects. Our research demonstrated that once automated driving matures, an absolute carpooling service may become mainstream.
Carpooling Services and Opportunities in Green Travel
Through research on profitability conditions under different pricing models and matching mechanisms, our research provides strategic advice to online ride-hailing platforms to realise sustainable development in shared rides. As autonomous driving technologies advance, more accurate traffic predictions and dynamic dispatching will drive carpooling services to be more automated and smarter. Not only will this enhance the user experience and cut running costs, but it will also reduce energy consumption while presenting new opportunities for ride-sharing.
This article is based on a research paper co-authored with Professor Zhixi Wan, Area Head of Innovation and Information Management at HKU Business School and Qin Zhou, Assistant Professor at East China Normal University. For more on our “The Profitability Puzzle of On-Demand Carpooling: Pricing, Matching, and Network Effects” research paper, please visit: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5083645
Note 1： Davalos, J (2023) ‘Lyft will discontinue pooled rides, launch new airport feature’, Bloomberg, 11 May
Note 2：Bellan, R. (2023) ‘Lyft might drop shared rides, stay focused on basics under new CEO’, Yahoo, 30 March
https://finance.yahoo.com/news/lyft-might-drop-shared-rides-231024896.html
Note 3：‘滴滴大动作不断，推出“青菜拼车”，不止白菜价，晚3分钟赔1元！’腾讯网https://news.qq.com/rain/a/20200721A0Q7MJ00
Prof. Xing HU
Associate Professor of Innovation and Information Management
This article was also published on February 5, 2025 on the Financial Times’ Chinese website
