  As a foreseeable future mode of transport with lower emissions and higher
efficiencies, electric vehicles have received worldwide attention. For
convenient centralized management, taxis are considered as the fleet with
electrification priority. In this work, we focus on the study on electric taxis
dispatching, with consideration of picking up customers and recharging, based
on real world traffic data of a large number of taxis in Beijing. First, the
assumed electric taxi charging stations are located using the K mean method.
Second, based on the station locations and the order demands, which are in form
of origin-destination pairs and extracted from the trajectory data, a
dispatching strategy as well as the simulation framework is developed with
consideration of reducing customer waiting time, mitigating electric taxi
charging congestion, and balancing order number distribution among electric
taxis. The proposed method models the electric taxi charging behaviors
temporally discretely from the aspects of charging demands and availability of
chargers, and further incorporates a centralized and intelligent fleet
dispatching platform, which is capable of handling taxi service requests and
arranging electric taxis' recharging in real time. The methodology in this
paper is readily applicable to dispatching of different types of electric
vehicle fleet with similar dataset available. Among the method, we use queueing
theory to model the electric vehicle charging station waiting phenomena and
include this factor into dispatching platform. Carbon emission is also surveyed
and analyzed.

Optimization and Control;Systems and Control;Systems and Control