An Agent-Based Modeling Approach for the Diffusion Analysis of Electric Vehicles With Two-Stage Purchase Choice Modeling

Abstract

Abstract Diffusion research of innovative technologies is crucial for new product positioning and strategic planning in product design. As a versatile system simulation method, agent-based modeling (ABM) has been used in many previous studies on the diffusion analysis of electrical vehicles (EVs). In these simulations, modeling consumers’ purchase decisions is a significant step. Previous studies often adopt simple rule-based decision criteria in this step, while a more accurate purchase decision model can contribute to more reasonable diffusion analysis of EVs. To fill this gap, this paper presents an agent-based modeling approach for the diffusion analysis of electric vehicles with two-stage choice modeling. The basic idea is to separate consumers’ decision-making process for purchasing a car into two stages. Consumers form a small choice set from the whole auto market in the first stage. In the second stage, consumers make the final choice from the choice set built in the first stage. In addition, the word-of-mouth (WOM) effect and consumers’ social network are also considered in the ABM, which can further improve the accuracy of the diffusion analysis of electric vehicles. A case study using data collected from Shanghai, China, is presented to demonstrate the proposed approach. The influence of factors such as government policy and technological improvement on the diffusion of EVs are also discussed. These insights can assist automakers in further improving their product design and enhancing their market competitiveness.