Causal Modeling of Factors Affecting the Performance of Autonomous Vehicles in Intelligent Transportation Systems (ITS)

Autonomous vehicles, as one of the key components of Intelligent Transportation Systems (ITS), play a significant role in enhancing the safety, efficiency, and sustainability of urban transportation networks. Despite considerable technological advancements in automation, sensor technologies, and communication systems, a comprehensive understanding of the causal relationships among the technical, human, infrastructural, and institutional factors influencing the performance of autonomous vehicles remains limited. The present study aims to develop and empirically test a comprehensive causal–statistical model within the ITS framework.
The research data were collected from 320 experts and potential users through purposive and convenience sampling methods. Based on the 10-times rule in Partial Least Squares Structural Equation Modeling (PLS-SEM), the sample size was considered adequate for accurate parameter estimation. Data analysis was conducted using the PLS-SEM approach. The findings revealed that the level of autonomy, sensor reliability, and the quality of ITS communications have a positive and significant effect on transportation system efficiency through the mediating variables of trust, perceived safety, and technology acceptance. Furthermore, the results indicate that user trust and perceived safety play a decisive role in the acceptance and effective utilization of autonomous vehicles. Overall, the findings of this study provide a scientific and practical framework to support policymaking, infrastructure planning, and the successful development of intelligent transportation systems based on autonomous vehicles