عنوان مقاله [English]
Autonomous vehicles (AVs) provide safe and efficient transportation without human intervention through their sensors and communication technologies. The advent of such vehicles will lead to an unprecedented revolution in transportation if they are accepted and used by the people. Higher technology acceptance rates justify investment in the infrastructure required for the expansion of such technologies as well as their ultimate success since eliminating economic and technical obstacles while neglecting the human barrier of acceptance would be futile. Most researchers have employed the Unified Theory of Acceptance and Use of Technology (UTAUT) to investigate the latent variables affecting the acceptance of AVs. Although the theory incorporates the majority of variables from eight technology acceptance models, it overlooks several factors. Moreover, the majority of studies on acceptance have been conducted in developing countries. The purpose of this study is to investigate the heterogeneity between people in accepting this technology by considering moderating variables. For this purpose, first, using the UTAUT, the latent factors affecting acceptance have been identified and in the next step, considering the socio-economic variables, people's heterogeneity has been investigated. To evaluate the conceptual model, 641 stated preference (SP) surveys were distributed to the residents of 22 districts of Tehran in 2019. The results of structural equation modeling analysis show that performance expectancy (PE), effort expectancy (EE) and social influence (SI) have the highest impact on acceptance, respectively. The results of model calibration for the data obtained from 641 questionnaires indicate the positive and significant effect of all latent variables (PE, EE and SI) on acceptance. Also, gender and the having postgraduate education moderate the coefficients of the variables of EE and SI, age over 65 years and having a certificate moderate the coefficients of PE and EE. The results of this study can be used by transportation authorities to identify the incentives and inhibiting factors concerning the acceptance of AVs. As a result, obstacles to the use of the technology can be eliminated, allowing communities to exploit its potential advantages.