Optimal prices for ridesourcing in the presence of taxi, public transport and car competition

Abstract

Ridesourcing platforms have acquired an important role as an alternative travel mode in almost all major cities in the world. These new mobility applications offer benefits for users over traditional taxi and public transport alternatives. However, there is mounting evidence that ridesourcing worsens traffic congestion. Given this potential negative impact and the complicated relationship between travel modes, it is unclear how policymakers should respond to the appearance of this new transport option. In this paper, we present a social welfare maximization model with four modes (automobile, taxi, buses and ridesourcing services) to derive the optimal first-best and second-best fares, considering the congestion externality generated by each mode. We apply this model to Santiago, Chile, using available parameter estimates to derive optimal fares with a novel inverse demand system (Inverse Product Differentiation Logit model). The results indicate that ridesourcing fares should be 29% higher per ride in the first-best scenario and 59% higher in the second-best scenario compared to current levels. However, our second-best scenario (in which buses and taxis have fares given by current levels and there is no congestion pricing for cars) reaches only 18% of the welfare gains from a first-best scenario. Sensitivity analysis shows that these results are not sensitive to several key assumptions, however they are sensitive to the parametrization of the flow-delay function in the second-best scenario. Our simulations also show that the optimal surcharge should be slightly higher if the average occupancy rate for ridesourcing services increases. This result is due to the higher overall use of this service, as the average fare per passenger decreases when more passengers ride together.