Polynomial-Time Carsharing Optimization: Linear Formulation and Large-Scale Simulations

Shared mobility services are useful for low-income people that are not satisfied with public transport and cannot comfortably afford their own vehicle. Among the low-cost shared mobility services, carsharing stands out because it avoids the cost of a driver. This work applies a polynomial-time linear programming formulation to optimize different carsharing business models for the Metropolitan Region of Sao Paulo. Real mobility data are used, focusing on inhabitants who are able to drive. Scenarios are evaluated varying distances that clients would be flexible to walk to get to an available vehicle or parking slot, comparing carsharing business models, and considering different rental prices. Results show that it is possible to offer a profitable low-cost carsharing service without performing vehicle relocations if clients are flexible enough to walk and if only a subset of trips is selected to be served. Results also demonstrate that trips selected to be served are similar among the different business models; are concentrated on Sao Paulo's downtown region; are shorter than the average trip, but otherwise behave in a similar way as compared to the complete set of trips; and the lack of parking slots may be a risk to the carsharing company.