Electric Vehicles (EVs) are energy-efficient and often presented as a zero-emission transport mode to achieve longer-term decarbonization visions in the transport sector. The implementation of a sustainable transportation environment through EV utilization, however, requires the addressing of certain cost and environmental concerns, before its full potential can be realized. These include EVs’ limited driving range and issues related to battery charging. Taxis are visible and thus EV use in taxi service can bring attention in urban life to a commitment towards sustainability in the public's opinion. For this reason, this study proposes an integrated approach incorporating EV operation and an appropriate shared-ride conceptual design for taxi service. Despite several obvious societal and environmental benefits, it is however true that EV use entails certain vehicle productivity loss due to the time lost in charging. As this could lead to a deterioration in system performance, and thus in demand as well, it is important to look at whether the expected performance loss from the passengers’ and systems’ standpoint can be offset with ingenuity in operational design. A combination of shared-taxi and EV fleet is proposed for this purpose, as it can be competitive in passenger travel and wait times with conventional non-EV taxis. Such systems are modeled and analyzed using simulation in this paper, under routing algorithms modified from previous research. More specifically, EV charging schemes for taxi service implementation were proposed and the effects of the limited driving range and battery charging details were examined from a system performance viewpoint. First, this study shows illustrative results on the impact of the EV taxi fleet’s vehicle charging on system performance. Then, real-time shared-taxi operation schemes are developed and applied to maximize the system efficiency with such a fleet. Some limitations and future research agenda have also been discussed.

A new facility location model and a solution algorithm are proposed that feature 1) itinerary-interception instead of flow-interception; 2) stochastic demand as dynamic service requests; and 3) queueing delay. These features are essential to analyze battery-powered electric shared-ride taxis operating in a connected, centralized dispatch manner. The model and solution method are based on a bi-level, simulation-optimization framework that combines an upper level multiple-server allocation model with queueing delay and a lower level dispatch simulation based on earlier work by Jung and Jayakrishnan. The solution algorithm is tested on a fleet of 600 shared-taxis in Seoul, Korea, spanning 603 km2, a budget of 100 charging stations, and up to 22 candidate charging locations, against a benchmark “naïve” genetic algorithm that does not consider cyclic interactions between the taxi charging demand and the charger allocations with queue delay. Results show not only that the proposed model is capable of locating charging stations with stochastic dynamic itinerary-interception and queue delay, butt that the bi-level solution method improves upon the benchmark algorithm in terms of realized queue delay, total time of operation of taxi service, and service request rejections. Furthermore, we show how much additional benefit in level of service is possible in the upper-bound scenario when the number of charging stations approaches infinity.