Optimizing Electric Taxi Charging System: A Data-Driven Approach from Transport Energy Supply Chain Perspective
For urban planners and electric taxi operators, this work provides a data-driven framework to improve charging infrastructure allocation, though it is an incremental application of existing models to a specific domain.
This paper introduces a transport energy supply chain perspective to optimize electric taxi charging station allocation, transforming it into a location problem. Using GPS trajectory data from Beijing, the proposed data-driven method evaluates system efficiency and service quality, comparing strategies with and without congestion.
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.