A large-scale empirical study on impacting factors of taxi charging station utilization

Abstract

Charging station planning is critical in the implementation of public transport electrification. However, in cities, there is insufficient experience and knowledge of the factors that influence the utilization of charging stations, particularly charging stations aimed at serving a fleet of electric taxis. Shenzhen is one of the pioneers in promoting electric taxis. In this paper, we collect large-scale datasets from Shenzhen and provide a data-driven space–time analysis of the relationship between charging station utilization and urban form and demand for taxi services. We use a Random Forest Regression model to explore these relationships and apply a Shapley value method to interpret the results. We find that demand for taxi services, measured as hourly pick-up and drop-off densities, have a non-linear relationship with utilization. Metro station density positively correlates with utilization, whereas the relationships between population density, land-use entropy, road density, and bus station density are more complicated.

Introduction

With innovations in battery technology and increasing concerns about environmental problems, electric vehicles (EVs) have attracted increasing attention in recent years (Morrissey et al., 2016a). Many cities are actively promoting the electrification of their public transport systems (Tu et al., 2016), for instance, electric taxis (e-taxis) and buses. Many charging stations (CSs) need to be built to satisfy the increasing charging demand. Especially for e-taxis, they represent a large percentage of urban charging demand (Yang et al., 2018), and their daily operation exhibits complex spatial–temporal dynamics (Wong et al., 2014, Qian and Ukkusuri, 2015). Furthermore, charging stations occupy a large number of parking facilities, which is a limited resource in many cities. In this context, it is essential to understand what factors affect charging facility utilization in more significant detail. This can support decision-making concerning the placement of charging stations and potentially achieve better utilization across urban areas.

There are two major gaps in the literature. First, there are few studies that systematically and empirically explore the impact factors of charging station utilization. In fact, good knowledge of what affects charging station utilization is a prerequisite for a successful planning scheme. Second, previous studies generally predefine a relationship between CS utilization and impacting factors (Wagner et al., 2014, Wolbertus et al., 2018, Olk et al., 2019, Mortimer et al., 2022). However, such a linear relationship may only be effective within a certain range of impacting factors (Tao et al., 2020).

The objectives of this study are to explore the impacting factors of charging station utilization and investigate the potential non-linear relationship between them. To this end, in this study, we use a Random Forest Regression (RFR) model to explore the factors influencing charging station utilization using data sets collected from Shenzhen, a city with the largest e-taxi fleet in the world. Furthermore, a SHapley Additive exPlanations (SHAP) value method (Lundberg and Lee, 2017) is applied in the RFR to explain the non-linear relationships between charging station utilization and impacting factors (e.g., built environment and taxi demand).

The study contributes in three aspects to the literature and practice. From the empirical perspective, it enriches the understanding of the relationship between collective factors and charging station utilization. By examining the non-linear relationship and threshold effects, the study provides a further understanding of how the factors effectively affect charging station utilization. From the methodology perspective, a random forest model, explained by the SHAP value method, is applied to capture complicated relationships between charging station utilization and explanatory variables. The methodology is proved to be more effective in revealing non-linear relationships than conventional linear regression models. From the perspective of planning practice, the study quantifies the relative importance of factors and gives guidance on prioritizing factors with tense land resources and limited planning alternatives. The study also offers implications for policy interventions to stimulate higher and more balanced e-taxi utilization at charging stations.

The remainder of the article is organized as follows. In the next section, we review the literature on the utilization of charging stations and the impacting factors analysis and identify research gaps. Section 3 illustrates the study area and data preparation and introduces the dependent and explanatory variables used in the model. Section 4 explains the methodology of the study. The results and discussions are provided in Section 5. In Section 6, we summarize the key results and discuss the associated policy implications.