Insights into Carsharing Demand Dynamics: Outputs of an Agent-Based Model Application to Lisbon, Portugal

Two important reasons claiming for carsharing systems are their increased flexibility and potential contribution to reducing transport externalities such as pollution. Carsharing typically involves a fleet of vehicles in stations around a city that clients may use on an hourly-payment basis. Classical round-trip systems address a niche market of urban trips like shopping and errands, however a growing market is now rising providing one-way trips to clients. Great uncertainty remains on the financial and economic viability of this type of carsharing given the complex relation between supply and demand, and how this may impact the level of service provided. Realistic modeling tools that include both supply and demand characterization and allow testing several operational parameters of carsharing systems are scarce. In this sense, a detailed agent-based model (ABM) was developed to simulate one-way carsharing systems. The simulation incorporates a stochastic demand model discretized in time and space and a detailed environment characterization with realistic travel times in the road network. The model was applied to the case-study city of Lisbon to understand the complex relationship between carsharing supply and demand. Results show that comparing to other modes, carsharing performs worse than private cars both in terms of time and cost. Nevertheless, it clearly outperforms taxis in terms of cost, and outperforms buses, metro and walking in terms of travel time. The competitiveness of carsharing is highly determined by trip length, becoming more competitive than other modes (travel-time wise) as trips become longer.