Abstract

Ride-hailing services using shared autonomous electric vehicles (SAEVs) are soon expected to serve a significant portion of the public transportation demands, especially in metropolitan cities. The transportation demands will arise randomly across all transportation nodes and during all hours of operation. This spatio-temporal nature of the transportation demand together with the constraints of trip length, battery size and consumption rate, and power network will guide the actions of the SAEVs. These actions are pair-with-demand, park, charge, and relocate, which in turn will determine the necessary infrastructure plan for ride-hailing services. The plan will comprise SAEV fleet size, charging hub locations across the nodes of the transportation network, and the number of charging stations in each hub. We formulate a profit-maximizing mixed integer linear model comprising a space-time-battery representation of the transportation network and a DC power flow representation of the power network. The model, intended for developing infrastructure plans for ride-hailing services, is robustified to accommodate randomness in travel and electric power demand. The model solution is demonstrated via an illustrative numerical case study representing the Tampa Bay region of Florida, USA. The results show how the infrastructure plans and their profitability depend on the demand fulfillment goal, battery size, battery consumption rate, and power network configurations. A linear relaxation of some of the integer variables of the model to reduce computational time is discussed.