With major investments in electric taxis emerging around the world, there is a need to better understand resource allocation trade-offs in subsidizing electric vehicle taxis (e-taxis) and investing in electric charging infrastructure. This is addressed using simulation experiments conducted in New York City: 2016 taxi pickups/drop-offs, a Manhattan road network (16,782 nodes, 23,337 links), and 212 charging stations specified by a 2013 Taxi & Limousine Commission study. The simulation is based on a platform used to evaluate taxi operations in California and Seoul. Eleven scenarios are analyzed: a baseline of 7,000 non-electric taxis, five scenarios ranging from 1,000 e-taxis to 5,000 e-taxis, and another five scenarios in which the e-taxis have infinite chargers as an upper bound. The study finds that the number of charging locations recommended in the earlier study may be insufficient at some locations even under the 3,000+ e-taxi scenarios. More importantly, despite an average revenue of $260 per taxi for the 7,000 non-electric taxis and about $247 per taxi for electric taxis over the finite charger scenarios, the revenue gap between e-taxis and non-electric taxis in a mixed fleet increases significantly as the e-taxi share increases. This is because the increasing queue delay imposed on e-taxis gives non-electric taxis an increasing competitive advantage, raising their average revenue from $260 per taxi (1,000 e-taxis) up to $286 per taxi (5,000 e-taxis, 150% revenue gap increase), all other operating costs being equal. This has implications for individual versus whole-fleet policies, as the individual-oriented policies may be less effective.
Best strategy to control data on internet-of-robotic-things in heterogeneous networks