Modeling and Optimization of Ridesplitting Operations

Ridesourcing has driven a lot of attention in recent years with the expansion of companies like Uber, Lift, and many others around the world. Companies use mobile applications connected through the internet to match drivers and their passengers real-time. Due to the nature of their operations, these companies may be called Transportation Network Companies (TNCs). These on-demand transportation services sound like a promising direction to improve mobility and fight car ownership. Moreover, many TNCs offer, among the service options, shared rides (called ridesplitting). In general, these services seem to have a positive impact on economic efficiency. New emerging modes of transportation such as ridesourcing create additional opportunities, but also more complexity to the authorities. Although it is not clear whether ridesourcing is beneficial or unfavorable (or whether it does not cause anything significantly) for traffic congestion, the path to clear it is to understand how it is replacing traditional transportation modes. In case ridesourcing trips are directly substituting private vehicles or taxis trips then, they should have a secondary influence on congestion. However, if ridesourcing competes with public transportation modes (buses, trains, metro) or inducing latent demand, then the effects on congestion should be significant. Additionally, it might increase vehicle kilometers traveled (VKT) when vehicles cruise for passengers or when it induces latent demand. Another nebulous point, specific from ridesplitting, is the role played by the infrastructure of a city on ridesplitting capability of generating positive environmental impacts. In this work, ridesourcing will be studied through simulations on realistic networks and field data to provide supporting evidence for the development of tools to help authorities under this new urban configuration. The first body of evidence will look for the effects of ridesourcing over traffic condition. The second body of evidence will develop a set of supporting tools to control ridesourcing fleets visioning service quality without compromising congestion conditions. The third body of evidence will investigate how passengers may cooperate to increase the chances of sharing their rides. The final body of evidence will seek the development of matching algorithms to bring together passengers with potential on sharing. Dissemination of research results is part of the program and include participations on scientific conferences and publications on scientific periodicals.