Optimizing Electric Taxi Charging System: A Data-Driven Approach from Transport Energy Supply Chain Perspective

TL;DR

This paper presents a data-driven method for optimizing electric taxi charging station placement by modeling the problem as a location problem within the transport energy supply chain, evaluated through a case study in Beijing.

Contribution

It introduces a novel perspective of the transport energy supply chain and applies P-median and Min-max models to improve charging station allocation for electric taxis.

Findings

Enhanced system efficiency with optimized station placement

Improved service quality in electric taxi charging networks

Comparative analysis of congestion effects on station allocation

Abstract

In the last decade, the development of electric taxis has motivated rapidly growing research interest in efficiently allocating electric charging stations in the academic literature. To address the driving pattern of electric taxis, we introduce the perspective of transport energy supply chain to capture the charging demand and to transform the charging station allocation problem to a location problem. Based on the P-median and the Min-max models, we developed a data-driven method to evaluate the system efficiency and service quality. We also conduct a case study using GPS trajectory data in Beijing, where various location strategies are evaluated from perspectives of system efficiency and service quality. Also, situations with and without congestion are comparatively evaluated.