Carsharing demand estimation and fleet simulation with EV adoption

Carsharing helps mitigate transportation congestion, parking demand, as well as the environmental, and social transportation challenges in cities. Besides providing vehicle access to lower income households, it lowers emissions and reduces vehicle miles traveled (VMT) as well as the number of vehicles registered. Despite these benefits, carsharing systems have been slow to develop in China. Little is known about carsharing demand in China's context, and subsequent system design parameters. This paper explores potential carsharing demand, fleet size, and economic performance in Beijing. To determine these, carsharing mode split is estimated by a stated preference choice modeling exercise. Adequate fleet size is estimated through a Monte Carlo simulation that includes factors such as vehicle types (electric or gasoline vehicle), charger types for electric vehicles (level 2 or level 3 chargers) that influence charging time, arrival rates, travel distance, and travel time based on the time intervals (peak or non-peak hours). In addition, this study estimates the payback period to recover sunk costs. Results indicate that the carsharing mode split ranges from 9% to 32% and an electric vehicle fleet with level 2 chargers is more appropriate for carsharing in Beijing.