Interpretable Machine Learning Approach to Predicting Electric Vehicle Buying Decisions

Abstract

To address the problem of climate change emissions from the transport sector, many countries are promoting electric vehicles (EVs). To support such efforts, it is essential to know what influences the choice of an EV over a traditional internal combustion engine vehicle (ICEV). To study this, a discrete choice experiment was developed, and 2,015 valid responses were gathered from Canadian adults with a driver’s license. In place of a more traditional analysis, a machine learning approach, XGBoost, was applied. However, two key issues were addressed with respect to its application. First, a practical question related to how best to split the training and testing data was examined. A new technique based on the Coyote optimization algorithm (COA) is developed that automatically determines the split that leads to the greatest prediction accuracy. The policy-relevant results of the analysis found that an individual’s Climate Change-Stage of Change (CC-SoC) and the price ratio of EVs to ICEVs are the most important direct influences. The interaction effect of the first two (CC-SoC and price ratio) is also influential. However, this leads to the second key issue: interpretability. Although high prediction accuracy (87.1%) was achieved, the black-box nature of the approach limits its policy relevance. As such, this research applied a technique, Accumulated Local Effects (ALE), that can determine the strength and direction of influence of the variable. This research demonstrates how machine learning can be applied to a policy-relevant question and provide information that is useful to policy decision makers.

Measurement of Regional Electric Vehicle Adoption Using Multiagent Deep Reinforcement Learning

“…The reward is based on CO 2 reduction estimates obtained by multiplying the updated constant by CO 2 reduction estimates. The rationale for designing the agents to seek the highest reward involving CO 2 reduction is derived from findings that environmental awareness is a significant factor impacting the purchase of EVs [6][7][8]26]. Discretization was implemented to save on the cost of training.…”

“…reward involving CO2 reduction is derived from findings that environmental awarenes is a significant factor impacting the purchase of EVs [6][7][8]26]. Discretization was imple mented to save on the cost of training.…”

“…The importance of addressing their reluctance to adopt EVs should be recognized. Our study incorporates the peer effect among consumers [24,25] and the interaction effect of climate change on EV purchasing decisions [26], using multiagent systems that represent high-and lowincome groups.…”