LAUSR

Abstract Taxis are often motivated to drive in crowded areas to earn more money by serving customers. Clean taxicab fleets in cities will have a significant impact on reducing air pollution and cutting emissions. Use of electric taxis is a highly efficient solution to address the issue of greenhouse effects, because electric cars are cleaner and cheaper than gasoline-powered cars. Battery swapping is an efficient and fast recharging method enabling taxi drivers to go to a battery swapping station (BSS) and replace their empty batteries with full ones. We study a new dynamic optimization model that helps the planning process in joint consideration of inventory, routing, and dynamic pricing to distribute electric taxi batteries between a BSS and a battery charging station (BCS). A novel dynamic programming (DP) model is proposed to incorporate a Markov decision process (MDP) with an actual demand function, operator cost, customer delay, and a dynamic pricing strategy using a social optimization function through a look-ahead policy. The results indicate that the average response time to transfer batteries from BCS nodes to BSS nodes under the look-ahead policy is reduced by up to 9% compared to the myopic case. We also observe that the average social welfare under the look-ahead policy increases by 22 % compared to a policy without look-ahead. The numerical analyses highlighted the benefits of supplying batteries at a socially efficient price instead of the standard monopoly price.