Ride-hailing platforms such as Uber and Lyft promise to reduce the negative externalities of driving and improve access to transportation. However, recent empirical evidence has been mixed about the impact of ride-hailing on US cities, often resulting in a net increase in traffic congestion and greenhouse gas (GHG) emissions, largely due to increased travel demand and competition with public transit. Pooled rides, in which multiple passengers share a single vehicle, are an effective solution to improve the sustainability of ride-hailing, reducing GHG emissions and traffic congestion and appealing to price-sensitive population segments by offering relatively cheaper rides. Yet, most ride-hailing trips are unprofitable currently, resulting from ride-hailing rides being subsidized (especially pooled) to compete with cheaper transportation alternatives such as public transit. In this paper, we consider whether price optimization can be used to improve ride-hailing revenues while also reducing the environmental impacts of ride-hailing, particularly as the cost of ride-hailing is expected to fall into the future with the introduction of automated vehicles. Using a discrete choice experiment and multinomial logit choice model with a representative sample of the US population, we estimate consumer preferences for the attributes of ride-hailing services and use them to explore how ride prices affect the revenue of ride-hailing platforms and the total vehicle miles traveled (VMT) by the ride-hailing fleet. We show that as the costs of driving fall, continuously increasing the difference between the prices of individual and pooled rides is financially optimal for ride-hailing platforms. Importantly, this pricing strategy also significantly reduces total VMT, resulting in a win–win for ride-hailing platforms and cities. We perform extensive sensitivity analyses and show that our results are qualitatively robust under a wide range of consumer preferences and market conditions but that the optimal trajectory of prices and realized gains vary, highlighting opportunities for ride-hailing services to influence the future of urban transportation.
All Science Journal Classification (ASJC) codes
- Management Science and Operations Research
- Industrial and Manufacturing Engineering
- Management of Technology and Innovation