Demand Adaptive Multi-Objective Electric Taxi Fleet Dispatching with Carbon Emission Analysis

Abstract

As a foreseeable future mode of transport with lower emissions and higher efficiencies, electric vehicles (EVs) 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 (ETs) 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 ET 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 (OD) 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 ET charging congestion, and balancing order number distribution among ETs. The proposed method models the ET 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 ETs’ recharging in real time. The methodology in this paper is readily applicable to dispatching of different types of EV fleet with similar dataset available. Among the method, we use queueing theory to model the EV charging station waiting phenomena and include this factor into dispatching platform. Carbon emission is also surveyed and analyzed.