Optimizing the economic and environmental benefits of ride‐hailing and pooling

Abstract

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.

Ride-Hailing Preferences for First- and Last-Mile Connectivity at Intercity Transit Hubs

“…The literature also explores the key important reasons for utilizing RHSs. In general, cheaper and transparent fees [47][48][49][50][51], being cashless [35,52], having predictable and short travel times including waiting times [51,53], comfort [14,47], security [54][55][56], and safety [57,58] are key determinates for RHS use. For example, a study revealed that RHSs employ pricing tactics like surge pricing and incentive bonuses to manage the equilibrium between supply and demand, thus guaranteeing transparent fee structures [48,49].…”

“…Table 1 presents a summary of prior studies outlining the motivations behind the utilization of RHSs. Positive correlation Easy handling of hefty luggage [61] Positive correlation Convenience [14,47] Positive correlation Secure and safety [54][55][56][57][58]62] Positive correlation Cashless [35,52] Positive correlation Simple ordering process [35] Positive correlation Door-to-door service [62] Positive correlation Clarity of pickup location [35] Positive correlation Cheaper and transparent fee [47][48][49][50][51] Positive correlation Short waiting time [51,53] Positive correlation Reliable travel time [53,61] Positive correlation H5. RHS use for FM affects LM Self-developed Positive correlation * Yogyakarta city and ** airport as a reference category.…”

Bilateral Pricing of Ride-Hailing Platforms Considering Cross-Group Network Effect and Congestion Effect

“…Zhong et al [39] explored the role of surcharge policies for a ride-hailing service platform's profit, consumer surplus, and driver surplus considering heterogeneous congestion-sensitive customers and reservation rates of local and long-distance drivers. Naumov and Keith [40] estimated consumer preferences for the attributes of ride-hailing services and used them to explore how ride prices affect the revenue of ride-hailing platforms as well as the total vehicle miles traveled (VMT) by the ride-hailing fleet. However, few scholars have considered the pricing decisions of ride-hailing platforms while taking into account both inter-group network effects and congestion effects.…”