چكيده به لاتين
Increasing consumption of automobiles, increasing environmental pollution caused by the operation of automobiles, and limited energy resources and fossil fuels, have led countries in recent years to reconsider their energy consumption and reduce all their vehicles with the aim of reducing Design fuel consumption and cars market with high efficiency, low fuel consumption, and very low emissions. Electric and hybrid vehicles are a good solution to reduce the use of fossil fuels and reduce environmental and air pollution. The purpose of this study is to model the factors affecting the acceptance of electric vehicle technology with a machine learning approach. For this purpose, the data of this study were collected by the library method and review of Internet resources and studies. A questionnaire was prepared to measure the effective variables and after ensuring the validity and reliability of the measurement tool, it was distributed among 688 people in cyberspace. The obtained data were analyzed by the machine learning, hierarchical clustering algorithm. According to the hierarchical clustering, potential buyers of electric vehicles in Iran are divided into 5 clusters. Each cluster has its own behavioral characteristics that make it different from other clusters. Cluster 3, identify a group of society in which hybrid vehicle technology is more accepted than other clusters, and in order to expand the acceptance of hybrid vehicle technology in Iran, this cluster should be targeted and policies should be adopted to expand this cluster. Up-to-date technology is important for the people of this cluster, car design as well as a reputable brand is one of the effective factors in choosing them. Diversity in hybrid car brands in Iran is considered an effective factor in the wider acceptance of this technology in this country. The limited selection of these cars and the poor after-sales service of their batteries are considered obstacles to the expansion of these cars in Iran.
